Merge branch 'main' of https://gitea.bilgich.com/fahricansecer/iddaai-be

- Add LeagueTier DB model and Prisma schema
- Add league-tiers service (CRUD, sync, retrain trigger)
- Add league-tiers controller with admin API endpoints
- Add /v1/admin/retrain endpoint in AI engine (extract→train→reload pipeline)
- Retrain V25 Pro with 48 quality leagues (MS accuracy: 26.9%→51.4%)
- Update qualified_leagues.json (443→48 leagues)
- Include V25 model files in repo for Docker deployment

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

.gitea/workflows/deploy.yml

@@ -11,13 +11,27 @@ jobs:
       - name: Kodu Cek
         uses: actions/checkout@v4
 
-      - name: Docker Build
+      - name: Docker Build (Backend)
         run: docker build -t iddaai-be:latest .
 
-      - name: Eski Konteyneri Sil
-        run: docker rm -f iddaai-be || true
+      - name: Docker Build (AI Engine)
+        run: docker build -t iddaai-ai-engine:latest ./ai-engine
 
-      - name: Yeni Versiyonu Baslat
+      - name: Eski Konteynerleri Sil
+        run: |
+          docker rm -f iddaai-be || true
+          docker rm -f iddaai-ai-engine || true
+
+      - name: AI Engine'i Baslat
+        run: |
+          docker run -d \
+            --name iddaai-ai-engine \
+            --restart unless-stopped \
+            --network iddaai_iddaai-network \
+            -e DATABASE_URL='${{ secrets.DATABASE_URL }}' \
+            iddaai-ai-engine:latest
+
+      - name: Backend'i Baslat
         run: |
           docker run -d \
             --name iddaai-be \
@@ -25,11 +39,21 @@ jobs:
             --network iddaai_iddaai-network \
             -p 127.0.0.1:1810:3005 \
             -e NODE_ENV=production \
-            -e DATABASE_URL='postgresql://iddaai_user:IddaA1_S4crET!@iddaai-postgres:5432/iddaai_db?schema=public' \
-            -e REDIS_HOST='iddaai-redis' \
-            -e REDIS_PORT='6379' \
-            -e REDIS_PASSWORD='IddaA1_Redis_Pass!' \
+            -e DATABASE_URL='${{ secrets.DATABASE_URL }}' \
+            -e REDIS_HOST='${{ secrets.REDIS_HOST }}' \
+            -e REDIS_PORT='${{ secrets.REDIS_PORT }}' \
+            -e REDIS_PASSWORD='${{ secrets.REDIS_PASSWORD }}' \
             -e AI_ENGINE_URL='http://iddaai-ai-engine:8000' \
-            -e JWT_SECRET='b7V8jM2wP1L5mQxs2RdfFkAsLpI2oG!w' \
+            -e JWT_SECRET='${{ secrets.JWT_SECRET }}' \
             -e JWT_ACCESS_EXPIRATION='1d' \
             iddaai-be:latest /bin/sh -c "npx prisma migrate deploy && node dist/src/main.js"
+
+      - name: Saglik Kontrolu
+        run: |
+          sleep 10
+          echo "=== AI Engine logs ==="
+          docker logs --tail 30 iddaai-ai-engine || true
+          echo "=== Backend logs ==="
+          docker logs --tail 30 iddaai-be || true
+          echo "=== AI Engine health ==="
+          docker exec iddaai-ai-engine python -c "import urllib.request; print(urllib.request.urlopen('http://127.0.0.1:8000/health').read().decode())" || echo "AI engine health check failed"

.gitignore

@@ -21,7 +21,10 @@ venv/
 env/
 
 # Database / Docker Volumes
-data/
+/data/
+ai-engine/data/**/*.csv
+ai-engine/data/v26_shadow/
+ai-engine/data/__pycache__/
 postgres-data/
 redis-data/
 
@@ -42,7 +45,10 @@ uploads/
 public/uploads/
 
 # Large Datasets and ML Models
-ai-engine/models/
-models/
+ai-engine/models/*
+!ai-engine/models/*.py
+!ai-engine/models/v25/
+models/*
+!models/*.py
 colab_export/
 

Dockerfile

@@ -16,7 +16,7 @@ RUN npm ci
 COPY . .
 
 # Generate Prisma client
-RUN npx prisma generate
+RUN DATABASE_URL="postgresql://dummy:dummy@localhost/dummy" npx prisma generate
 
 # Build the application
 RUN npm run build
@@ -38,7 +38,7 @@ RUN apk add --no-cache --virtual .build-deps python3 make g++ cairo-dev pango-de
 
 # Copy Prisma schema and generate client
 COPY prisma ./prisma
-RUN npx prisma generate
+RUN DATABASE_URL="postgresql://dummy:dummy@localhost/dummy" npx prisma generate
 
 # Copy built application
 COPY --from=builder /app/dist ./dist
@@ -47,7 +47,7 @@ COPY --from=builder /app/dist ./dist
 COPY --from=builder /app/src/i18n ./dist/i18n
 
 # Copy league filter config files (critical: without these, feeder stores ALL matches)
-COPY top_leagues.json basketball_top_leagues.json ./
+COPY qualified_leagues.json top_leagues.json basketball_top_leagues.json ./
 
 # Set environment
 ENV NODE_ENV=production

ai-engine/catboost_info/catboost_training.json

@@ -0,0 +1,874 @@
+{
+"meta":{"test_sets":["test"],"test_metrics":[{"best_value":"Min","name":"Logloss"}],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":2000,"learn_sets":["learn"],"name":"experiment"},
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ai-engine/catboost_info/time_left.tsv

@@ -0,0 +1,871 @@
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+827	26477	37478
+828	26517	37456
+829	26539	37411
+830	26573	37382
+831	26597	37339
+832	26623	37298
+833	26650	37259
+834	26677	37221
+835	26704	37182
+836	26728	37138
+837	26763	37111
+838	26791	37073
+839	26822	37041
+840	26872	37033
+841	26924	37029
+842	26982	37033
+843	27054	37055
+844	27097	37038
+845	27120	36994
+846	27146	36954
+847	27180	36925
+848	27206	36884
+849	27234	36846
+850	27260	36807
+851	27289	36770
+852	27318	36734
+853	27347	36698
+854	27386	36675
+855	27413	36637
+856	27439	36596
+857	27471	36564
+858	27501	36529
+859	27535	36500
+860	27572	36474
+861	27595	36431
+862	27627	36398
+863	27654	36360
+864	27683	36324
+865	27711	36287
+866	27738	36249
+867	27765	36210
+868	27794	36175
+869	27820	36135

ai-engine/config/config_loader.py

@@ -1,7 +1,9 @@
 import os
+import json
 import yaml
 from typing import Dict, Any, Optional
 
+
 class EnsembleConfig:
     _instance: Optional['EnsembleConfig'] = None
     _config: Dict[str, Any] = {}
@@ -35,12 +37,79 @@ class EnsembleConfig:
         except (KeyError, TypeError):
             return default
 
+
 # Singleton accessor
 def get_config() -> EnsembleConfig:
     return EnsembleConfig()
 
+
+# ── Market Thresholds Loader ────────────────────────────────────────────
+
+_market_thresholds_cache: Optional[Dict[str, Any]] = None
+
+
+def load_market_thresholds() -> Dict[str, Any]:
+    """
+    Load market thresholds from JSON config file.
+    Returns the full config dict with 'markets' and 'defaults' keys.
+    Caches after first load for performance.
+    """
+    global _market_thresholds_cache
+    if _market_thresholds_cache is not None:
+        return _market_thresholds_cache
+
+    config_path = os.path.join(os.path.dirname(__file__), 'market_thresholds.json')
+    try:
+        with open(config_path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+        _market_thresholds_cache = data
+        print(f"✅ Market thresholds loaded: {len(data.get('markets', {}))} markets (v={data.get('_meta', {}).get('version', '?')})")
+        return data
+    except Exception as e:
+        print(f"❌ Failed to load market thresholds: {e} — using built-in defaults")
+        _market_thresholds_cache = {"markets": {}, "defaults": {
+            "calibration": 0.55,
+            "min_conf": 55.0,
+            "min_play_score": 68.0,
+            "min_edge": 0.02,
+            "odds_band_min_sample": 0.0,
+            "odds_band_min_edge": 0.0,
+        }}
+        return _market_thresholds_cache
+
+
+def build_threshold_dict(field: str) -> Dict[str, float]:
+    """
+    Build a flat {market: value} dict for a specific threshold field.
+
+    Usage:
+        calibration_map = build_threshold_dict("calibration")
+        # → {"MS": 0.62, "DC": 0.82, ...}
+    """
+    data = load_market_thresholds()
+    markets = data.get("markets", {})
+    result: Dict[str, float] = {}
+    for market, cfg in markets.items():
+        if field in cfg:
+            result[market] = float(cfg[field])
+    return result
+
+
+def get_threshold_default(field: str) -> float:
+    """Get the default fallback value for a threshold field."""
+    data = load_market_thresholds()
+    defaults = data.get("defaults", {})
+    return float(defaults.get(field, 0.0))
+
+
 if __name__ == "__main__":
     # Test
     cfg = get_config()
     print(f"Weights: {cfg.get('engine_weights')}")
     print(f"Team Weight: {cfg.get('engine_weights.team')}")
+    print()
+    print("--- Market Thresholds ---")
+    for field in ["calibration", "min_conf", "min_play_score", "min_edge"]:
+        d = build_threshold_dict(field)
+        print(f"{field}: {d}")
+    print(f"Default calibration: {get_threshold_default('calibration')}")

ai-engine/config/ensemble_config.yaml

@@ -1,3 +1,14 @@
+model_ensemble:
+  xgb_weight: 0.50
+  lgb_weight: 0.50
+  temperature: 1.5
+  default_ms_odds:
+    home: 2.65
+    draw: 3.20
+    away: 2.65
+  elo_staleness_days: 14
+  odds_staleness_hours: 48
+
 engine_weights:
   team: 0.30
   player: 0.25

ai-engine/config/market_thresholds.json

@@ -0,0 +1,115 @@
+{
+  "_meta": {
+    "version": "v34",
+    "description": "Market-specific thresholds for the betting engine pipeline — V34 odds-aware gate fix",
+    "rule": "max_reachable (100 × calibration) MUST be > min_conf + 8",
+    "updated_at": "2026-05-10",
+    "changelog": "V34: Reduced min_edge to realistic levels for odds-aware V25 model. Model output ≈ market-implied, so large EV edges are mathematically impossible."
+  },
+  "markets": {
+    "MS": {
+      "calibration": 0.62,
+      "min_conf": 20.0,
+      "min_play_score": 28.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "DC": {
+      "calibration": 0.82,
+      "min_conf": 40.0,
+      "min_play_score": 50.0,
+      "min_edge": 0.003,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU15": {
+      "calibration": 0.84,
+      "min_conf": 45.0,
+      "min_play_score": 50.0,
+      "min_edge": 0.003,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU25": {
+      "calibration": 0.68,
+      "min_conf": 30.0,
+      "min_play_score": 40.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU35": {
+      "calibration": 0.60,
+      "min_conf": 20.0,
+      "min_play_score": 30.0,
+      "min_edge": 0.008,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "BTTS": {
+      "calibration": 0.65,
+      "min_conf": 30.0,
+      "min_play_score": 40.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "HT": {
+      "calibration": 0.58,
+      "min_conf": 20.0,
+      "min_play_score": 28.0,
+      "min_edge": 0.01,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "HT_OU05": {
+      "calibration": 0.68,
+      "min_conf": 35.0,
+      "min_play_score": 42.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "HT_OU15": {
+      "calibration": 0.60,
+      "min_conf": 25.0,
+      "min_play_score": 32.0,
+      "min_edge": 0.008,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "OE": {
+      "calibration": 0.62,
+      "min_conf": 35.0,
+      "min_play_score": 32.0,
+      "min_edge": 0.005
+    },
+    "CARDS": {
+      "calibration": 0.58,
+      "min_conf": 30.0,
+      "min_play_score": 35.0,
+      "min_edge": 0.008
+    },
+    "HCAP": {
+      "calibration": 0.56,
+      "min_conf": 25.0,
+      "min_play_score": 30.0,
+      "min_edge": 0.015
+    },
+    "HTFT": {
+      "calibration": 0.45,
+      "min_conf": 10.0,
+      "min_play_score": 18.0,
+      "min_edge": 0.02
+    }
+  },
+  "defaults": {
+    "calibration": 0.55,
+    "min_conf": 55.0,
+    "min_play_score": 60.0,
+    "min_edge": 0.008,
+    "odds_band_min_sample": 0.0,
+    "odds_band_min_edge": 0.0
+  }
+}

ai-engine/core/calculators/base_calculator.py

@@ -40,7 +40,7 @@ class CalculationContext:
     is_surprise: bool = False
 
     # XGBoost Predictions (New)
-    xgboost_preds: dict[str, dict[str, Any]] = field(default_factory=dict)
+    xgboost_preds: dict[str, Any] = field(default_factory=dict)
 
 
 class BaseCalculator:

ai-engine/core/calculators/bet_recommender.py

@@ -28,7 +28,7 @@ class RecommendationResult:
 
 
 class BetRecommender(BaseCalculator):
-    def calculate(self,
+    def calculate(self,  # type: ignore[override]
                   ctx: CalculationContext,
                   ms_res: MatchResultPrediction,
                   ou_res: OverUnderPrediction,

ai-engine/core/calculators/expert_recommender.py

@@ -36,7 +36,7 @@ class ExpertResult:
 
 
 class ExpertRecommender(BaseCalculator):
-    def calculate(self,
+    def calculate(self,  # type: ignore[override]
                   ctx: CalculationContext,
                   ms_res: MatchResultPrediction,
                   ou_res: OverUnderPrediction,

ai-engine/core/calculators/half_time_calculator.py

@@ -31,7 +31,7 @@ class HalfTimeCalculator(BaseCalculator):
             return 1.0 if k == 0 else 0.0
         return (lam ** k) * math.exp(-lam) / math.factorial(k)
         
-    def calculate(self, ctx: CalculationContext) -> HalfTimePrediction:
+    def calculate(self, ctx: CalculationContext) -> HalfTimePrediction:  # type: ignore[override]
         team_pred = ctx.team_pred
         odds_pred = ctx.odds_pred
         

ai-engine/core/calculators/match_result_calculator.py

@@ -22,9 +22,9 @@ class MatchResultCalculator(BaseCalculator):
     def _get_engine_winner(self, home_prob: float, draw_prob: float, away_prob: float) -> str:
         """Determine which outcome an engine favors."""
         probs = {"1": home_prob, "X": draw_prob, "2": away_prob}
-        return max(probs, key=probs.get)
+        return max(probs, key=probs.__getitem__)
     
-    def calculate(self, ctx: CalculationContext) -> MatchResultPrediction:
+    def calculate(self, ctx: CalculationContext) -> MatchResultPrediction:  # type: ignore[override]
         # Weights
         w_team = ctx.weights["team"]
         w_player = ctx.weights["player"]

ai-engine/core/calculators/other_markets_calculator.py

@@ -28,7 +28,7 @@ class OtherMarketsPrediction:
 
 
 class OtherMarketsCalculator(BaseCalculator):
-    def calculate(
+    def calculate(  # type: ignore[override]
         self,
         ctx: CalculationContext,
         ms_result: MatchResultPrediction,

ai-engine/core/calculators/over_under_calculator.py

@@ -55,7 +55,7 @@ class OverUnderCalculator(BaseCalculator):
         
         return over_15, over_25, over_35, btts_yes
     
-    def calculate(self, ctx: CalculationContext) -> OverUnderPrediction:
+    def calculate(self, ctx: CalculationContext) -> OverUnderPrediction:  # type: ignore[override]
         odds_pred = ctx.odds_pred
         referee_mods = ctx.referee_mods
         

ai-engine/core/calculators/risk_assessor.py

@@ -67,12 +67,14 @@ class RiskAssessor(BaseCalculator):
 
         if sport_key == "basketball":
             if is_top_league:
-                return float(
-                    self.config.get("risk.surprise_threshold_basketball_top", self.config.get("risk.surprise_threshold_basketball", 0.30)),
-                )
-            return float(
-                self.config.get("risk.surprise_threshold_basketball_non_top", 0.34),
-            )
+                top_val = self.config.get("risk.surprise_threshold_basketball_top")
+                if top_val is not None:
+                    return float(top_val)
+                base_val = self.config.get("risk.surprise_threshold_basketball")
+                return float(base_val) if base_val is not None else 0.30
+            
+            non_top_val = self.config.get("risk.surprise_threshold_basketball_non_top")
+            return float(non_top_val) if non_top_val is not None else 0.34
 
         if top_label not in ("1/2", "2/1"):
             return base_threshold
@@ -81,27 +83,30 @@ class RiskAssessor(BaseCalculator):
         favorite_side, gap = self._favorite_profile_from_odds(ctx.odds_data)
 
         if is_top_league:
-            favorite_winner_threshold = float(
-                self.config.get(
-                    "risk.surprise_threshold_favorite_reversal_top",
-                    self.config.get("risk.surprise_threshold_favorite_reversal", 0.26),
-                ),
-            )
-            underdog_winner_threshold = float(
-                self.config.get(
-                    "risk.surprise_threshold_underdog_reversal_top",
-                    self.config.get("risk.surprise_threshold_underdog_reversal", 0.20),
-                ),
-            )
+            top_fav = self.config.get("risk.surprise_threshold_favorite_reversal_top")
+            if top_fav is not None:
+                favorite_winner_threshold = float(top_fav)
             else:
-            favorite_winner_threshold = float(
-                self.config.get("risk.surprise_threshold_favorite_reversal_non_top", 0.30),
-            )
-            underdog_winner_threshold = float(
-                self.config.get("risk.surprise_threshold_underdog_reversal_non_top", 0.24),
-            )
-        gap_medium = float(self.config.get("risk.htft_reversal_gap_medium", 0.50))
-        gap_strong = float(self.config.get("risk.htft_reversal_gap_strong", 1.00))
+                base_fav = self.config.get("risk.surprise_threshold_favorite_reversal")
+                favorite_winner_threshold = float(base_fav) if base_fav is not None else 0.26
+                
+            top_ud = self.config.get("risk.surprise_threshold_underdog_reversal_top")
+            if top_ud is not None:
+                underdog_winner_threshold = float(top_ud)
+            else:
+                base_ud = self.config.get("risk.surprise_threshold_underdog_reversal")
+                underdog_winner_threshold = float(base_ud) if base_ud is not None else 0.20
+        else:
+            nt_fav = self.config.get("risk.surprise_threshold_favorite_reversal_non_top")
+            favorite_winner_threshold = float(nt_fav) if nt_fav is not None else 0.30
+            nt_ud = self.config.get("risk.surprise_threshold_underdog_reversal_non_top")
+            underdog_winner_threshold = float(nt_ud) if nt_ud is not None else 0.24
+            
+        gm = self.config.get("risk.htft_reversal_gap_medium")
+        gap_medium = float(gm) if gm is not None else 0.50
+        
+        gs = self.config.get("risk.htft_reversal_gap_strong")
+        gap_strong = float(gs) if gs is not None else 1.00
 
         if favorite_side in ("H", "A"):
             threshold = (
@@ -117,7 +122,7 @@ class RiskAssessor(BaseCalculator):
 
         return base_threshold
     
-    def calculate(self, ctx: CalculationContext, ms_result=None) -> RiskAnalysis:
+    def calculate(self, ctx: CalculationContext, ms_result: Any = None) -> RiskAnalysis:  # type: ignore[override]
         """
         Wrapper for assess_risk to match BaseCalculator interface but with extra arg.
         """
@@ -173,9 +178,15 @@ class RiskAssessor(BaseCalculator):
 
                 threshold = self._dynamic_reversal_threshold(ctx, top_label)
                 if getattr(ctx, "is_top_league", False):
-                    min_gap = float(self.config.get("risk.surprise_min_top_gap_top", self.config.get("risk.surprise_min_top_gap", 0.02)))
+                    top_gap_val = self.config.get("risk.surprise_min_top_gap_top")
+                    if top_gap_val is not None:
+                        min_gap = float(top_gap_val)
                     else:
-                    min_gap = float(self.config.get("risk.surprise_min_top_gap_non_top", 0.03))
+                        base_gap_val = self.config.get("risk.surprise_min_top_gap")
+                        min_gap = float(base_gap_val) if base_gap_val is not None else 0.02
+                else:
+                    non_top_gap_val = self.config.get("risk.surprise_min_top_gap_non_top")
+                    min_gap = float(non_top_gap_val) if non_top_gap_val is not None else 0.03
 
                 # Trigger surprise only when reversal class is:
                 # - top HT/FT outcome

ai-engine/core/calculators/score_calculator.py

@@ -3,7 +3,7 @@ import pickle
 import pandas as pd
 import xgboost as xgb
 from dataclasses import dataclass
-from typing import List, Dict, Tuple
+from typing import List, Dict, Tuple, Optional
 import math
 from .base_calculator import BaseCalculator, CalculationContext
 from .confidence import calc_confidence_3way, calc_confidence_dc
@@ -16,7 +16,7 @@ class ScorePrediction:
     ft_scores_top5: List[Dict]
     
     # Reconciled MS/DC predictions (can be updated here)
-    reconciled_ms: MatchResultPrediction = None
+    reconciled_ms: Optional[MatchResultPrediction] = None
 
 class ScoreCalculator(BaseCalculator):
 
@@ -57,7 +57,8 @@ class ScoreCalculator(BaseCalculator):
             return 1.0 if k == 0 else 0.0
         return (lam ** k) * math.exp(-lam) / math.factorial(k)
         
-    def calculate(self, ctx: CalculationContext, ms_result: MatchResultPrediction) -> ScorePrediction:
+    def calculate(self, ctx: CalculationContext, ms_result: MatchResultPrediction) -> ScorePrediction:  # type: ignore[override]
+        predicted_ht = None
         # Default Lambdas (fallback)
         lambda_home = max(0.5, ctx.home_xg)
         lambda_away = max(0.5, ctx.away_xg)
@@ -199,7 +200,7 @@ class ScoreCalculator(BaseCalculator):
             predicted_ft = top_overall_score
             
         # If we didn't calculate HT via ML (exception case), do it now
-        if 'predicted_ht' not in locals():
+        if predicted_ht is None:
             ft_to_ht = self.config.get("half_time.ft_to_ht_ratio", 0.42)
             ht_h = round(lambda_home * ft_to_ht)
             ht_a = round(lambda_away * ft_to_ht)

ai-engine/core/engines/__init__.py

@@ -1,16 +1,10 @@
 # ai-engine/core/engines/__init__.py
 """
-V20 Ensemble Prediction Engines
+Prediction Engines
 """
 
-from .team_predictor import TeamPredictorEngine, get_team_predictor
 from .player_predictor import PlayerPredictorEngine, get_player_predictor
-from .odds_predictor import OddsPredictorEngine, get_odds_predictor
-from .referee_predictor import RefereePredictorEngine, get_referee_predictor
 
 __all__ = [
-    "TeamPredictorEngine", "get_team_predictor",
     "PlayerPredictorEngine", "get_player_predictor",
-    "OddsPredictorEngine", "get_odds_predictor",
-    "RefereePredictorEngine", "get_referee_predictor"
 ]

ai-engine/core/engines/odds_predictor.py

@@ -1,237 +0,0 @@
-"""
-Odds Predictor Engine - V20 Ensemble Component
-Uses market odds and Poisson mathematics for predictions.
-
-Weight: 30% in ensemble
-"""
-
-import os
-import sys
-from typing import Dict, Optional
-from dataclasses import dataclass
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-from features.poisson_engine import get_poisson_engine
-from features.value_calculator import get_value_calculator
-
-
-@dataclass
-class OddsPrediction:
-    """Odds engine prediction output."""
-    # Market-implied probabilities
-    market_home_prob: float = 0.33
-    market_draw_prob: float = 0.33
-    market_away_prob: float = 0.33
-    
-    # Poisson xG
-    poisson_home_xg: float = 1.3
-    poisson_away_xg: float = 1.1
-    
-    # Over/Under probabilities
-    over_15_prob: float = 0.75
-    over_25_prob: float = 0.55
-    over_35_prob: float = 0.30
-    
-    # BTTS
-    btts_yes_prob: float = 0.50
-    
-    # Most likely scores
-    most_likely_score: str = "1-1"
-    second_likely_score: str = "1-0"
-    third_likely_score: str = "2-1"
-    
-    # Value bet opportunities
-    value_bets: list = None
-    
-    confidence: float = 0.0
-    
-    def __post_init__(self):
-        if self.value_bets is None:
-            self.value_bets = []
-    
-    def to_dict(self) -> dict:
-        return {
-            "market_home_prob": round(self.market_home_prob * 100, 1),
-            "market_draw_prob": round(self.market_draw_prob * 100, 1),
-            "market_away_prob": round(self.market_away_prob * 100, 1),
-            "poisson_home_xg": round(self.poisson_home_xg, 2),
-            "poisson_away_xg": round(self.poisson_away_xg, 2),
-            "over_15_prob": round(self.over_15_prob * 100, 1),
-            "over_25_prob": round(self.over_25_prob * 100, 1),
-            "over_35_prob": round(self.over_35_prob * 100, 1),
-            "btts_yes_prob": round(self.btts_yes_prob * 100, 1),
-            "most_likely_score": self.most_likely_score,
-            "second_likely_score": self.second_likely_score,
-            "third_likely_score": self.third_likely_score,
-            "value_bets": self.value_bets,
-            "confidence": round(self.confidence, 1)
-        }
-
-
-class OddsPredictorEngine:
-    """
-    Odds-based prediction engine.
-    
-    Uses:
-    - Market odds to extract implied probabilities
-    - Poisson distribution for mathematical xG
-    - Value calculator for EV+ opportunities
-    """
-    
-    def __init__(self):
-        self.poisson_engine = get_poisson_engine()
-        try:
-            self.value_calc = get_value_calculator()
-        except Exception:
-            self.value_calc = None
-        self.default_ms_h = 2.65
-        self.default_ms_d = 3.20
-        self.default_ms_a = 2.65
-        print("✅ OddsPredictorEngine initialized")
-    
-    def _odds_to_prob(self, odds: float) -> float:
-        """Convert decimal odds to probability."""
-        try:
-            odds = float(odds)
-        except (TypeError, ValueError):
-            return 0.0
-        if odds <= 1.0:
-            return 0.0
-        return 1.0 / odds
-    
-    def predict(self,
-                odds_data: Dict[str, float],
-                home_goals_avg: float = 1.5,
-                home_conceded_avg: float = 1.2,
-                away_goals_avg: float = 1.2,
-                away_conceded_avg: float = 1.4) -> OddsPrediction:
-        """
-        Generate odds-based prediction.
-        
-        Args:
-            odds_data: Dict with keys like 'ms_h', 'ms_d', 'ms_a', 'ou25_o', 'btts_y'
-            home_goals_avg: Home team's average goals scored
-            home_conceded_avg: Home team's average goals conceded
-            away_goals_avg: Away team's average goals scored
-            away_conceded_avg: Away team's average goals conceded
-            
-        Returns:
-            OddsPrediction with market and Poisson analysis
-        """
-        
-        # 1. Extract market probabilities from odds
-        ms_h = odds_data.get("ms_h", self.default_ms_h)
-        ms_d = odds_data.get("ms_d", self.default_ms_d)
-        ms_a = odds_data.get("ms_a", self.default_ms_a)
-        
-        # Remove vig to get fair probabilities
-        raw_probs = [
-            self._odds_to_prob(ms_h),
-            self._odds_to_prob(ms_d),
-            self._odds_to_prob(ms_a)
-        ]
-        total = sum(raw_probs) or 1
-        
-        market_home = raw_probs[0] / total
-        market_draw = raw_probs[1] / total
-        market_away = raw_probs[2] / total
-        
-        # 2. Poisson prediction
-        poisson_pred = self.poisson_engine.predict(
-            home_goals_avg, home_conceded_avg,
-            away_goals_avg, away_conceded_avg
-        )
-        
-        # 3. Get most likely scores
-        likely_scores = poisson_pred.most_likely_scores[:3] if poisson_pred.most_likely_scores else []
-        score_1 = likely_scores[0]["score"] if len(likely_scores) > 0 else "1-1"
-        score_2 = likely_scores[1]["score"] if len(likely_scores) > 1 else "1-0"
-        score_3 = likely_scores[2]["score"] if len(likely_scores) > 2 else "2-1"
-        
-        # 4. Value bet detection
-        value_bets = []
-        
-        # Check if our Poisson model disagrees with market significantly
-        if abs(poisson_pred.home_win_prob - market_home) > 0.10:
-            if poisson_pred.home_win_prob > market_home:
-                value_bets.append({
-                    "market": "MS 1",
-                    "edge": round((poisson_pred.home_win_prob - market_home) * 100, 1),
-                    "confidence": "medium"
-                })
-            else:
-                value_bets.append({
-                    "market": "MS 2",
-                    "edge": round((poisson_pred.away_win_prob - market_away) * 100, 1),
-                    "confidence": "medium"
-                })
-        
-        # O/U value check
-        ou25_o = odds_data.get("ou25_o", 1.9)
-        market_over25 = self._odds_to_prob(ou25_o)
-        if abs(poisson_pred.over_25_prob - market_over25) > 0.08:
-            pick = "2.5 Üst" if poisson_pred.over_25_prob > market_over25 else "2.5 Alt"
-            edge = abs(poisson_pred.over_25_prob - market_over25) * 100
-            value_bets.append({
-                "market": pick,
-                "edge": round(edge, 1),
-                "confidence": "high" if edge > 10 else "medium"
-            })
-        
-        # Calculate confidence
-        # Higher when market and Poisson agree
-        agreement = 1.0 - abs(poisson_pred.home_win_prob - market_home)
-        confidence = 50.0 + (agreement * 40) + (len(value_bets) * 5)
-        
-        return OddsPrediction(
-            market_home_prob=market_home,
-            market_draw_prob=market_draw,
-            market_away_prob=market_away,
-            poisson_home_xg=poisson_pred.home_xg,
-            poisson_away_xg=poisson_pred.away_xg,
-            over_15_prob=poisson_pred.over_15_prob,
-            over_25_prob=poisson_pred.over_25_prob,
-            over_35_prob=poisson_pred.over_35_prob,
-            btts_yes_prob=poisson_pred.btts_yes_prob,
-            most_likely_score=score_1,
-            second_likely_score=score_2,
-            third_likely_score=score_3,
-            value_bets=value_bets,
-            confidence=min(99.9, confidence)
-        )
-
-
-# Singleton
-_engine: Optional[OddsPredictorEngine] = None
-
-
-def get_odds_predictor() -> OddsPredictorEngine:
-    global _engine
-    if _engine is None:
-        _engine = OddsPredictorEngine()
-    return _engine
-
-
-if __name__ == "__main__":
-    engine = get_odds_predictor()
-    
-    print("\n🧪 Odds Predictor Engine Test")
-    print("=" * 50)
-    
-    pred = engine.predict(
-        odds_data={
-            "ms_h": 1.85,
-            "ms_d": 3.40,
-            "ms_a": 4.20,
-            "ou25_o": 1.90
-        },
-        home_goals_avg=1.8,
-        home_conceded_avg=1.0,
-        away_goals_avg=1.2,
-        away_conceded_avg=1.5
-    )
-    
-    print(f"\n📊 Prediction:")
-    for k, v in pred.to_dict().items():
-        print(f"   {k}: {v}")

ai-engine/core/engines/player_predictor.py

@@ -18,34 +18,36 @@ from features.sidelined_analyzer import get_sidelined_analyzer
 
 @dataclass
 class PlayerPrediction:
-    """Player engine prediction output."""
-    home_squad_quality: float = 50.0  # 0-100
-    away_squad_quality: float = 50.0
-    squad_diff: float = 0.0  # -100 to +100
+    """Player engine prediction output.
+
+    IMPORTANT: squad_quality uses the SAME composite formula as
+    extract_training_data.py so that inference values match the
+    distribution the model was trained on (~3-36 range).
+    """
+    home_squad_quality: float = 12.0
+    away_squad_quality: float = 12.0
+    squad_diff: float = 0.0
     home_key_players: int = 0
     away_key_players: int = 0
-    home_missing_impact: float = 0.0  # 0-1, how much weaker due to missing players
+    home_missing_impact: float = 0.0
     away_missing_impact: float = 0.0
-    home_goals_form: int = 0  # Goals in last 5 matches
+    home_goals_form: int = 0
     away_goals_form: int = 0
+    home_lineup_goals_per90: float = 0.0
+    away_lineup_goals_per90: float = 0.0
+    home_lineup_assists_per90: float = 0.0
+    away_lineup_assists_per90: float = 0.0
+    home_squad_continuity: float = 0.5
+    away_squad_continuity: float = 0.5
+    home_top_scorer_form: int = 0
+    away_top_scorer_form: int = 0
+    home_avg_player_exp: float = 0.0
+    away_avg_player_exp: float = 0.0
+    home_goals_diversity: float = 0.0
+    away_goals_diversity: float = 0.0
     lineup_available: bool = False
     confidence: float = 0.0
 
-    def to_dict(self) -> dict:
-        return {
-            "home_squad_quality": round(self.home_squad_quality, 1),
-            "away_squad_quality": round(self.away_squad_quality, 1),
-            "squad_diff": round(self.squad_diff, 1),
-            "home_key_players": self.home_key_players,
-            "away_key_players": self.away_key_players,
-            "home_missing_impact": round(self.home_missing_impact, 2),
-            "away_missing_impact": round(self.away_missing_impact, 2),
-            "home_goals_form": self.home_goals_form,
-            "away_goals_form": self.away_goals_form,
-            "lineup_available": self.lineup_available,
-            "confidence": round(self.confidence, 1)
-        }
-
 
 class PlayerPredictorEngine:
     """
@@ -67,9 +69,9 @@ class PlayerPredictorEngine:
                 match_id: str,
                 home_team_id: str,
                 away_team_id: str,
-                home_lineup: List[str] = None,
-                away_lineup: List[str] = None,
-                sidelined_data: Dict = None) -> PlayerPrediction:
+                home_lineup: Optional[List[str]] = None,
+                away_lineup: Optional[List[str]] = None,
+                sidelined_data: Optional[Dict] = None) -> PlayerPrediction:
         """
         Generate player-based prediction.
         
@@ -85,8 +87,9 @@ class PlayerPredictorEngine:
         """
         
         # Get squad features
+        home_analysis = None
+        away_analysis = None
         if home_lineup and away_lineup:
-            # Use provided lineups (for live matches)
             home_analysis = self.squad_engine.analyze_squad_from_list(
                 home_lineup, home_team_id
             )
@@ -94,19 +97,19 @@ class PlayerPredictorEngine:
                 away_lineup, away_team_id
             )
             lineup_available = True
-            # Build features dict from analysis objects
             features = {
                 "home_starting_11": home_analysis.starting_count or 11,
                 "home_goals_last_5": home_analysis.total_goals_last_5,
                 "home_assists_last_5": home_analysis.total_assists_last_5,
                 "home_key_players": home_analysis.key_players_count,
+                "home_forwards": home_analysis.forward_count or 2,
                 "away_starting_11": away_analysis.starting_count or 11,
                 "away_goals_last_5": away_analysis.total_goals_last_5,
                 "away_assists_last_5": away_analysis.total_assists_last_5,
                 "away_key_players": away_analysis.key_players_count,
+                "away_forwards": away_analysis.forward_count or 2,
             }
         elif match_id:
-            # Try to get from database
             try:
                 features = self.squad_engine.get_features(
                     match_id, home_team_id, away_team_id
@@ -126,31 +129,27 @@ class PlayerPredictorEngine:
             )
             lineup_available = False
 
-        # Extract features
-        home_goals = features.get("home_goals_last_5", 0)
-        away_goals = features.get("away_goals_last_5", 0)
-        home_key = features.get("home_key_players", 0)
-        away_key = features.get("away_key_players", 0)
+        home_goals = int(features.get("home_goals_last_5", 0))
+        away_goals = int(features.get("away_goals_last_5", 0))
+        home_key = int(features.get("home_key_players", 0))
+        away_key = int(features.get("away_key_players", 0))
+        home_starting = features.get("home_starting_11", 11)
+        away_starting = features.get("away_starting_11", 11)
+        home_fwd = features.get("home_forwards", 2)
+        away_fwd = features.get("away_forwards", 2)
 
-        # Calculate squad quality (0-100)
-        # Based on: goals scored, key players, assists
-        home_quality = min(100, 50 + (home_goals * 3) + (home_key * 5) + 
-                          features.get("home_assists_last_5", 0) * 2)
-        away_quality = min(100, 50 + (away_goals * 3) + (away_key * 5) + 
-                          features.get("away_assists_last_5", 0) * 2)
-        
-        # Squad difference
+        # Squad quality — matches V25 extract_training_data.py:579
+        home_quality = home_starting * 0.3 + home_key * 3.0 + home_fwd * 1.5
+        away_quality = away_starting * 0.3 + away_key * 3.0 + away_fwd * 1.5
         squad_diff = home_quality - away_quality
 
         # Missing player impact
-        # Priority: sidelined data (position-weighted) > lineup count (basic)
         if sidelined_data:
             home_impact, away_impact = self.sidelined_analyzer.analyze_match(sidelined_data)
-            home_missing = home_impact.impact_score
-            away_missing = away_impact.impact_score
+            home_missing = min(1.0, max(0.0, home_impact.impact_score))
+            away_missing = min(1.0, max(0.0, away_impact.impact_score))
             sidelined_available = True
         else:
-            # Fallback: basic lineup count method
             expected_xi = 11
             actual_home_xi = features.get("home_starting_11", 11)
             actual_away_xi = features.get("away_starting_11", 11)
@@ -158,7 +157,13 @@ class PlayerPredictorEngine:
             away_missing = (expected_xi - actual_away_xi) / expected_xi if actual_away_xi < expected_xi else 0
             sidelined_available = False
 
-        # Confidence: more data sources = higher confidence
+        # Player-level features (matches extract_training_data.py:594-650)
+        player_feats = self._compute_player_level_features(
+            home_lineup or [], away_lineup or [],
+            home_team_id, away_team_id,
+            home_analysis, away_analysis,
+        )
+
         confidence = 70.0 if lineup_available else 35.0
         if home_goals + away_goals > 10:
             confidence += 15
@@ -177,17 +182,147 @@ class PlayerPredictorEngine:
             away_missing_impact=away_missing,
             home_goals_form=home_goals,
             away_goals_form=away_goals,
+            home_lineup_goals_per90=player_feats['home_lineup_goals_per90'],
+            away_lineup_goals_per90=player_feats['away_lineup_goals_per90'],
+            home_lineup_assists_per90=player_feats['home_lineup_assists_per90'],
+            away_lineup_assists_per90=player_feats['away_lineup_assists_per90'],
+            home_squad_continuity=player_feats['home_squad_continuity'],
+            away_squad_continuity=player_feats['away_squad_continuity'],
+            home_top_scorer_form=player_feats['home_top_scorer_form'],
+            away_top_scorer_form=player_feats['away_top_scorer_form'],
+            home_avg_player_exp=player_feats['home_avg_player_exp'],
+            away_avg_player_exp=player_feats['away_avg_player_exp'],
+            home_goals_diversity=player_feats['home_goals_diversity'],
+            away_goals_diversity=player_feats['away_goals_diversity'],
             lineup_available=lineup_available,
             confidence=max(5.0, confidence)
         )
 
+    def _compute_player_level_features(
+        self,
+        home_lineup: List[str],
+        away_lineup: List[str],
+        home_team_id: str,
+        away_team_id: str,
+        home_analysis,
+        away_analysis,
+    ) -> Dict[str, float]:
+        defaults = {
+            'home_lineup_goals_per90': 0.0, 'away_lineup_goals_per90': 0.0,
+            'home_lineup_assists_per90': 0.0, 'away_lineup_assists_per90': 0.0,
+            'home_squad_continuity': 0.5, 'away_squad_continuity': 0.5,
+            'home_top_scorer_form': 0, 'away_top_scorer_form': 0,
+            'home_avg_player_exp': 0.0, 'away_avg_player_exp': 0.0,
+            'home_goals_diversity': 0.0, 'away_goals_diversity': 0.0,
+        }
+        conn = self.squad_engine.get_conn()
+        if conn is None:
+            return defaults
+
+        try:
+            from psycopg2.extras import RealDictCursor
+            result = {}
+            for prefix, lineup, team_id in [
+                ('home', home_lineup, home_team_id),
+                ('away', away_lineup, away_team_id),
+            ]:
+                if not lineup:
+                    for k in ('lineup_goals_per90', 'lineup_assists_per90',
+                              'squad_continuity', 'top_scorer_form',
+                              'avg_player_exp', 'goals_diversity'):
+                        result[f'{prefix}_{k}'] = defaults[f'{prefix}_{k}']
+                    continue
+
+                g90, a90, total_exp = 0.0, 0.0, 0
+                best_scorer_total, best_scorer_id = 0, None
+                scorers_in_lineup = 0
+
+                with conn.cursor(cursor_factory=RealDictCursor) as cur:
+                    for pid in lineup:
+                        cur.execute("""
+                            SELECT
+                                COUNT(*) as starts,
+                                COALESCE(SUM(CASE WHEN e.event_type = 'goal'
+                                    AND (e.event_subtype IS NULL OR e.event_subtype NOT ILIKE '%%penaltı kaçırma%%')
+                                    THEN 1 ELSE 0 END), 0) as goals,
+                                COALESCE((SELECT COUNT(*) FROM match_player_events
+                                    WHERE assist_player_id = %s), 0) as assists
+                            FROM match_player_participation mpp
+                            LEFT JOIN match_player_events e
+                                ON e.match_id = mpp.match_id AND e.player_id = mpp.player_id
+                            WHERE mpp.player_id = %s AND mpp.is_starting = true
+                        """, (pid, pid))
+                        row = cur.fetchone()
+                        if not row or not row['starts']:
+                            continue
+                        starts = row['starts']
+                        goals = row['goals'] or 0
+                        assists = row['assists'] or 0
+                        g90 += goals / starts
+                        a90 += assists / starts
+                        total_exp += starts
+                        if goals > 0:
+                            scorers_in_lineup += 1
+                        if goals > best_scorer_total:
+                            best_scorer_total = goals
+                            best_scorer_id = pid
+
+                    n_st = len(lineup) or 1
+
+                    # Top scorer recent form (goals in last 5 starts)
+                    top_scorer_form = 0
+                    if best_scorer_id:
+                        cur.execute("""
+                            SELECT COUNT(*) as goals
+                            FROM match_player_events mpe
+                            WHERE mpe.player_id = %s AND mpe.event_type = 'goal'
+                              AND mpe.match_id IN (
+                                  SELECT match_id FROM match_player_participation
+                                  WHERE player_id = %s AND is_starting = true
+                                  ORDER BY match_id DESC LIMIT 5
+                              )
+                        """, (best_scorer_id, best_scorer_id))
+                        tsf_row = cur.fetchone()
+                        if tsf_row:
+                            top_scorer_form = tsf_row['goals'] or 0
+
+                    # Squad continuity (overlap with previous match lineup)
+                    squad_continuity = 0.5
+                    cur.execute("""
+                        SELECT mpp.player_id
+                        FROM match_player_participation mpp
+                        JOIN matches m ON mpp.match_id = m.id
+                        WHERE mpp.team_id = %s AND mpp.is_starting = true
+                          AND m.status = 'FT'
+                        ORDER BY m.mst_utc DESC
+                        LIMIT 11
+                    """, (team_id,))
+                    prev_starters = {r['player_id'] for r in cur.fetchall()}
+                    if prev_starters:
+                        overlap = len(set(lineup) & prev_starters)
+                        squad_continuity = overlap / n_st
+
+                result[f'{prefix}_lineup_goals_per90'] = round(g90, 3)
+                result[f'{prefix}_lineup_assists_per90'] = round(a90, 3)
+                result[f'{prefix}_squad_continuity'] = round(squad_continuity, 3)
+                result[f'{prefix}_top_scorer_form'] = top_scorer_form
+                result[f'{prefix}_avg_player_exp'] = round(total_exp / n_st, 1)
+                result[f'{prefix}_goals_diversity'] = round(scorers_in_lineup / n_st, 3)
+
+            return result
+        except Exception as e:
+            print(f"[PlayerPredictor] Player-level features failed: {e}")
+            return defaults
+    
     def get_1x2_modifier(self, prediction: PlayerPrediction) -> Dict[str, float]:
         """
         Calculate 1X2 probability modifiers based on squad analysis.
         
         Returns modifiers to apply to base probabilities.
+        squad_diff is in training scale (~-33 to +33), normalize to -1..+1.
         """
-        diff = prediction.squad_diff / 100  # -1 to +1
+        diff = prediction.squad_diff / 33.0  # training-scale normalisation
+        diff = max(-1.0, min(1.0, diff))      # clamp
         
         return {
             "home_modifier": 1.0 + (diff * 0.3),  # Up to +/-30%
@@ -214,7 +349,7 @@ if __name__ == "__main__":
     print("=" * 50)
     
     pred = engine.predict(
-        match_id=None,
+        match_id="test_match",
         home_team_id="test_home",
         away_team_id="test_away"
     )

ai-engine/core/engines/referee_predictor.py

@@ -1,188 +0,0 @@
-"""
-Referee Predictor Engine - V20 Ensemble Component
-Analyzes referee patterns for cards, goals, and home bias.
-
-Weight: 15% in ensemble
-"""
-
-import os
-import sys
-from typing import Dict, Optional
-from dataclasses import dataclass
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-from features.referee_engine import get_referee_engine
-
-
-@dataclass
-class RefereePrediction:
-    """Referee engine prediction output."""
-    referee_name: str = ""
-    matches_officiated: int = 0
-    
-    # Card tendencies
-    avg_yellow_cards: float = 4.0
-    avg_red_cards: float = 0.2
-    is_card_heavy: bool = False  # Above average cards
-    
-    # Goal tendencies
-    avg_goals_per_match: float = 2.5
-    over_25_rate: float = 0.50
-    is_high_scoring: bool = False  # Above average goals
-    
-    # Home bias
-    home_win_rate: float = 0.45
-    home_bias: float = 0.0  # -1 to +1, positive = favors home
-    
-    # Penalty tendency
-    penalty_rate: float = 0.15
-    
-    confidence: float = 0.0
-    
-    def to_dict(self) -> dict:
-        return {
-            "referee_name": self.referee_name,
-            "matches_officiated": self.matches_officiated,
-            "avg_yellow_cards": round(self.avg_yellow_cards, 1),
-            "avg_red_cards": round(self.avg_red_cards, 2),
-            "is_card_heavy": self.is_card_heavy,
-            "avg_goals_per_match": round(self.avg_goals_per_match, 2),
-            "over_25_rate": round(self.over_25_rate * 100, 1),
-            "is_high_scoring": self.is_high_scoring,
-            "home_win_rate": round(self.home_win_rate * 100, 1),
-            "home_bias": round(self.home_bias, 2),
-            "penalty_rate": round(self.penalty_rate * 100, 1),
-            "confidence": round(self.confidence, 1)
-        }
-
-
-class RefereePredictorEngine:
-    """
-    Referee-based prediction engine.
-    
-    Analyzes:
-    - Card tendency (sarı/kırmızı kart ortalaması)
-    - Goal tendency (maç başına gol, 2.5 üst oranı)
-    - Home bias (ev sahibi lehine karar oranı)
-    - Penalty tendency (penaltı verme oranı)
-    """
-    
-    # League average benchmarks
-    LEAGUE_AVG_GOALS = 2.65
-    LEAGUE_AVG_YELLOW = 4.0
-    LEAGUE_HOME_WIN_RATE = 0.45
-    
-    def __init__(self):
-        self.referee_engine = get_referee_engine()
-        print("✅ RefereePredictorEngine initialized")
-    
-    def predict(self,
-                match_id: str = None,
-                referee_name: str = None,
-                league_id: str = None) -> RefereePrediction:
-        """
-        Generate referee-based prediction.
-        
-        Args:
-            match_id: Match ID to find referee
-            referee_name: Or provide referee name directly
-            league_id: League ID to scope stats (prevents name collisions)
-            
-        Returns:
-            RefereePrediction with referee analysis
-        """
-        
-        # Get referee features
-        if match_id:
-            features = self.referee_engine.get_features(match_id, league_id=league_id)
-            # Live flows may already have referee_name while match_officials table is sparse.
-            # Prefer the richer profile if direct-name lookup has more history.
-            if referee_name:
-                name_features = self.referee_engine.get_features_by_name(referee_name, league_id=league_id)
-                if (name_features.get("referee_matches", 0) or 0) > (features.get("referee_matches", 0) or 0):
-                    features = name_features
-        elif referee_name:
-            features = self.referee_engine.get_features_by_name(referee_name, league_id=league_id)
-        else:
-            # Return default
-            return RefereePrediction(confidence=10.0)
-        
-        ref_name = features.get("referee_name", "Unknown")
-        matches = features.get("referee_matches", 0)
-        
-        if matches < 5:
-            # Not enough data
-            return RefereePrediction(
-                referee_name=ref_name,
-                matches_officiated=matches,
-                confidence=20.0
-            )
-        
-        # Extract features
-        avg_yellow = features.get("referee_avg_yellow", 4.0)
-        avg_red = features.get("referee_avg_red", 0.2)
-        avg_goals = features.get("referee_avg_goals", 2.5)
-        over25_rate = features.get("referee_over25_rate", 0.5)
-        home_win_rate = features.get("referee_home_win_rate", 0.45) if "referee_home_win_rate" in features else 0.45
-        home_bias = features.get("referee_home_bias", 0.0)
-        penalty_rate = features.get("referee_penalty_rate", 0.15)
-        
-        # Determine tendencies
-        is_card_heavy = (avg_yellow + avg_red * 4) > (self.LEAGUE_AVG_YELLOW + 1)
-        is_high_scoring = avg_goals > self.LEAGUE_AVG_GOALS
-        
-        # Confidence based on matches officiated
-        confidence = min(90.0, 30.0 + matches * 2)
-        
-        return RefereePrediction(
-            referee_name=ref_name,
-            matches_officiated=matches,
-            avg_yellow_cards=avg_yellow,
-            avg_red_cards=avg_red,
-            is_card_heavy=is_card_heavy,
-            avg_goals_per_match=avg_goals,
-            over_25_rate=over25_rate,
-            is_high_scoring=is_high_scoring,
-            home_win_rate=home_win_rate,
-            home_bias=home_bias,
-            penalty_rate=penalty_rate,
-            confidence=confidence
-        )
-    
-    def get_modifiers(self, prediction: RefereePrediction) -> Dict[str, float]:
-        """
-        Get modifiers to apply to other predictions based on referee profile.
-        """
-        return {
-            # Home team gets slight boost if referee has home bias
-            "home_modifier": 1.0 + (prediction.home_bias * 0.05),
-            # O/U modifier
-            "over_25_modifier": 1.0 + (prediction.avg_goals_per_match - self.LEAGUE_AVG_GOALS) * 0.1,
-            # Card modifier for card markets
-            "cards_modifier": 1.0 + (prediction.avg_yellow_cards - self.LEAGUE_AVG_YELLOW) * 0.05
-        }
-
-
-# Singleton
-_engine: Optional[RefereePredictorEngine] = None
-
-
-def get_referee_predictor() -> RefereePredictorEngine:
-    global _engine
-    if _engine is None:
-        _engine = RefereePredictorEngine()
-    return _engine
-
-
-if __name__ == "__main__":
-    engine = get_referee_predictor()
-    
-    print("\n🧪 Referee Predictor Engine Test")
-    print("=" * 50)
-    
-    pred = engine.predict(referee_name="Cüneyt Çakır")
-    
-    print(f"\n📊 Prediction:")
-    for k, v in pred.to_dict().items():
-        print(f"   {k}: {v}")

ai-engine/core/engines/team_predictor.py

@@ -1,286 +0,0 @@
-"""
-Team Predictor Engine - V20 Ensemble Component
-Combines ELO ratings, form stats, H2H records and team statistics.
-
-Weight: 30% in ensemble
-"""
-
-import os
-import sys
-from typing import Dict, Optional, Tuple, Any
-from dataclasses import dataclass, field
-
-# Add parent to path
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-from features.elo_system import get_elo_system
-from features.h2h_engine import get_h2h_engine
-from features.momentum_engine import get_momentum_engine, MomentumData
-from features.team_stats_engine import get_team_stats_engine
-
-
-@dataclass
-class TeamPrediction:
-    """Team engine prediction output."""
-    home_win_prob: float = 0.33
-    draw_prob: float = 0.33
-    away_win_prob: float = 0.33
-    home_xg: float = 1.3
-    away_xg: float = 1.1
-    form_advantage: float = 0.0  # -1 to +1, positive = home advantage
-    h2h_advantage: float = 0.0   # -1 to +1
-    elo_diff: float = 0.0
-    confidence: float = 0.0
-    
-    def to_dict(self) -> dict:
-        return {
-            "home_win_prob": round(self.home_win_prob * 100, 1),
-            "draw_prob": round(self.draw_prob * 100, 1),
-            "away_win_prob": round(self.away_win_prob * 100, 1),
-            "home_xg": round(self.home_xg, 2),
-            "away_xg": round(self.away_xg, 2),
-            "form_advantage": round(self.form_advantage, 2),
-            "h2h_advantage": round(self.h2h_advantage, 2),
-            "elo_diff": round(self.elo_diff, 0),
-            "confidence": round(self.confidence, 1)
-        }
-    
-    raw_features: Dict[str, Any] = field(default_factory=dict)
-
-
-class TeamPredictorEngine:
-    """
-    Team-based prediction engine.
-    
-    Uses:
-    - ELO Rating System (venue-adjusted, league-weighted)
-    - H2H Engine (head-to-head history)
-    - Momentum Engine (recent form)
-    - Team Stats Engine (possession, shots, corners)
-    """
-    
-    def __init__(self):
-        self.elo_system = get_elo_system()
-        self.h2h_engine = get_h2h_engine()
-        self.momentum_engine = get_momentum_engine()
-        self.team_stats_engine = get_team_stats_engine()
-        
-        print("✅ TeamPredictorEngine initialized")
-    
-    def predict(self, 
-                home_team_id: str, 
-                away_team_id: str,
-                match_date_ms: int,
-                home_team_name: str = "",
-                away_team_name: str = "") -> TeamPrediction:
-        """
-        Generate team-based prediction.
-        
-        Args:
-            home_team_id: Home team ID
-            away_team_id: Away team ID  
-            match_date_ms: Match date in milliseconds
-            home_team_name: Home team name (for ELO)
-            away_team_name: Away team name (for ELO)
-            
-        Returns:
-            TeamPrediction with 1X2 probabilities and xG
-        """
-        
-        # 1. Get ELO predictions
-        elo_pred = self.elo_system.predict_match(home_team_id, away_team_id)
-        elo_features = self.elo_system.get_match_features(home_team_id, away_team_id)
-        
-        # 2. Get H2H features
-        try:
-            h2h_features = self.h2h_engine.get_features(
-                home_team_id, away_team_id, match_date_ms
-            )
-        except Exception:
-            h2h_features = {
-                "h2h_home_win_rate": 0.5,
-                "h2h_away_win_rate": 0.5,
-                "h2h_avg_goals": 2.5,
-                "h2h_btts_rate": 0.5
-            }
-        
-        # 3. Get Momentum/Form features
-        try:
-            # key: form_score should be 0-1 derived from momentum_score (-1 to 1)
-            home_mom_data = self.momentum_engine.calculate_momentum(home_team_id, match_date_ms)
-            away_mom_data = self.momentum_engine.calculate_momentum(away_team_id, match_date_ms)
-            
-            home_form_score = (home_mom_data.momentum_score + 1) / 2
-            away_form_score = (away_mom_data.momentum_score + 1) / 2
-        except Exception as e:
-            print(f"⚠️ MomentumEngine error: {e}")
-            home_mom_data = MomentumData()
-            away_mom_data = MomentumData()
-            home_form_score = 0.5
-            away_form_score = 0.5
-        
-        # 4. Get Team Stats
-        home_stats = self.team_stats_engine.get_features(home_team_id, match_date_ms)
-        away_stats = self.team_stats_engine.get_features(away_team_id, match_date_ms)
-        
-        # 5. Combine predictions
-        # ELO-based 1X2 (60% weight)
-        elo_home = elo_pred.get("home_win_prob", 0.33)
-        elo_draw = elo_pred.get("draw_prob", 0.33)
-        elo_away = elo_pred.get("away_win_prob", 0.33)
-        
-        # Adjust based on H2H (20% weight)
-        h2h_home_rate = h2h_features.get("h2h_home_win_rate", 0.5)
-        h2h_away_rate = h2h_features.get("h2h_away_win_rate", 0.5)
-        
-        # Adjust based on form (20% weight)
-        home_form = home_form_score
-        away_form = away_form_score
-        form_diff = (home_form - away_form)  # -1 to +1
-        
-        # Weighted combination
-        final_home = elo_home * 0.6 + h2h_home_rate * 0.2 + (0.5 + form_diff * 0.3) * 0.2
-        final_away = elo_away * 0.6 + h2h_away_rate * 0.2 + (0.5 - form_diff * 0.3) * 0.2
-        final_draw = 1.0 - final_home - final_away
-        
-        # Normalize
-        total = final_home + final_draw + final_away
-        if total > 0:
-            final_home /= total
-            final_draw /= total
-            final_away /= total
-        
-        # Calculate xG based on stats and form (conservative base)
-        home_conversion = home_stats.get("shot_conversion_rate", 0.1)
-        away_conversion = away_stats.get("shot_conversion_rate", 0.1)
-        
-        base_home_xg = 1.35 + (home_conversion * 3.0)
-        base_away_xg = 1.10 + (away_conversion * 2.5)
-        
-        # Defense weakness factor: opponent's defensive quality affects xG
-        # Higher shots on target against = weaker defense
-        away_def_weakness = away_stats.get("shot_accuracy", 0.35)  # opponent's shot accuracy as proxy
-        home_def_weakness = home_stats.get("shot_accuracy", 0.35)
-        
-        # Adjust xG: stronger opponent defense → lower xG
-        home_xg = base_home_xg * (1 + form_diff * 0.15) * (0.8 + away_def_weakness * 0.6)
-        away_xg = base_away_xg * (1 - form_diff * 0.15) * (0.8 + home_def_weakness * 0.6)
-        
-        # Apply xG Underperformance Penalty directly to calculated xG
-        # If a team chronically underperforms its xG, we subtract that historical difference here
-        if hasattr(home_mom_data, 'xg_underperformance') and home_mom_data.xg_underperformance > 0.2:
-            home_xg -= min(0.5, home_mom_data.xg_underperformance * 0.5)
-            
-        if hasattr(away_mom_data, 'xg_underperformance') and away_mom_data.xg_underperformance > 0.2:
-            away_xg -= min(0.5, away_mom_data.xg_underperformance * 0.5)
-        
-        # H2H adjustment (more conservative)
-        h2h_avg_goals = h2h_features.get("h2h_avg_goals", 2.5)
-        if h2h_avg_goals > 3.0:
-            home_xg *= 1.05
-            away_xg *= 1.05
-        elif h2h_avg_goals < 2.0:
-            home_xg *= 0.95
-            away_xg *= 0.95
-        
-        # Clamp xG to reasonable range
-        home_xg = max(0.5, min(3.5, home_xg))
-        away_xg = max(0.3, min(3.0, away_xg))
-        
-        # Calculate confidence
-        # Higher when ELO, H2H, and Form all agree
-        elo_winner = "H" if elo_home > max(elo_draw, elo_away) else ("A" if elo_away > elo_draw else "D")
-        h2h_winner = "H" if h2h_home_rate > h2h_away_rate else "A"
-        form_winner = "H" if form_diff > 0.1 else ("A" if form_diff < -0.1 else "D")
-        
-        agreement = sum([
-            elo_winner == h2h_winner,
-            elo_winner == form_winner,
-            h2h_winner == form_winner
-        ])
-        
-        max_prob = max(final_home, final_draw, final_away)
-        confidence = max_prob * 100 * (0.7 + agreement * 0.1)
-        
-        # Collect Raw Features for XGBoost
-        # Note: home_mom_data is an object now
-        def get_rate(val): return val if val is not None else 0.5
-        
-        raw_features = {
-            **elo_features,  # 8 features
-            
-            # Form Features (need key mapping to match extract_training_data.py)
-            "home_goals_avg": 1.5 + home_mom_data.goals_trend, # Proxy
-            "home_conceded_avg": 1.5 - home_mom_data.conceded_trend, # Proxy
-            "away_goals_avg": 1.5 + away_mom_data.goals_trend,
-            "away_conceded_avg": 1.5 - away_mom_data.conceded_trend,
-            
-            "home_clean_sheet_rate": 0.2, # Not in new MomentumData
-            "away_clean_sheet_rate": 0.2,
-            "home_scoring_rate": 0.8,
-            "away_scoring_rate": 0.8,
-            
-            "home_winning_streak": home_mom_data.winning_streak,
-            "away_winning_streak": away_mom_data.winning_streak,
-            "home_unbeaten_streak": home_mom_data.unbeaten_streak,
-            "away_unbeaten_streak": away_mom_data.unbeaten_streak,
-            
-            # H2H Features
-            **h2h_features,
-            
-            # Team Stats
-            "home_avg_possession": home_stats.get("avg_possession", 0.5),
-            "away_avg_possession": away_stats.get("avg_possession", 0.5),
-            "home_avg_shots_on_target": home_stats.get("avg_shots_on_target", 3.5),
-            "away_avg_shots_on_target": away_stats.get("avg_shots_on_target", 3.5),
-            "home_shot_conversion": home_stats.get("shot_conversion_rate", 0.1),
-            "away_shot_conversion": away_stats.get("shot_conversion_rate", 0.1),
-            "home_avg_corners": home_stats.get("avg_corners", 4.5),
-            "away_avg_corners": away_stats.get("avg_corners", 4.5),
-            
-            # Derived
-            "home_xga": 1.5 - home_mom_data.conceded_trend, # reusing as proxy
-            "away_xga": 1.5 - away_mom_data.conceded_trend
-        }
-        
-        return TeamPrediction(
-            home_win_prob=final_home,
-            draw_prob=final_draw,
-            away_win_prob=final_away,
-            home_xg=home_xg,
-            away_xg=away_xg,
-            form_advantage=form_diff,
-            h2h_advantage=h2h_home_rate - h2h_away_rate,
-            elo_diff=elo_features.get("elo_diff", 0),
-            confidence=confidence,
-            raw_features=raw_features
-        )
-
-
-# Singleton
-_engine: Optional[TeamPredictorEngine] = None
-
-
-def get_team_predictor() -> TeamPredictorEngine:
-    global _engine
-    if _engine is None:
-        _engine = TeamPredictorEngine()
-    return _engine
-
-
-if __name__ == "__main__":
-    engine = get_team_predictor()
-    
-    print("\n🧪 Team Predictor Engine Test")
-    print("=" * 50)
-    
-    # Test with sample IDs
-    pred = engine.predict(
-        home_team_id="test_home",
-        away_team_id="test_away",
-        match_date_ms=1707393600000
-    )
-    
-    print(f"\n📊 Prediction:")
-    for k, v in pred.to_dict().items():
-        print(f"   {k}: {v}")

ai-engine/data/__init__.py

@@ -0,0 +1 @@
+# data package

ai-engine/data/database.py

@@ -0,0 +1,97 @@
+"""
+Async Database Module — V2 Betting Engine
+==========================================
+Provides async SQLAlchemy sessions via asyncpg for the V2 router.
+
+Usage:
+    async with get_session() as session:
+        result = await session.execute(text("SELECT ..."))
+"""
+
+from __future__ import annotations
+
+import os
+from contextlib import asynccontextmanager
+from typing import AsyncGenerator
+
+from dotenv import load_dotenv
+from sqlalchemy.ext.asyncio import (
+    AsyncEngine,
+    AsyncSession,
+    async_sessionmaker,
+    create_async_engine,
+)
+
+load_dotenv()
+
+_engine: AsyncEngine | None = None
+_session_maker: async_sessionmaker[AsyncSession] | None = None
+
+
+def _get_async_dsn() -> str:
+    """
+    Convert DATABASE_URL to asyncpg-compatible format.
+
+    Handles:
+      1. Prisma's ``?schema=public`` suffix  → stripped
+      2. ``postgresql://`` driver prefix     → ``postgresql+asyncpg://``
+    """
+    dsn = os.getenv(
+        "DATABASE_URL",
+        "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db",
+    )
+
+    # Strip Prisma's ?schema= parameter
+    if "?" in dsn:
+        base, query = dsn.split("?", 1)
+        kept_parts = [
+            part for part in query.split("&") if part and not part.startswith("schema=")
+        ]
+        dsn = base if not kept_parts else f"{base}?{'&'.join(kept_parts)}"
+
+    # Convert driver prefix for asyncpg
+    if dsn.startswith("postgresql://"):
+        dsn = dsn.replace("postgresql://", "postgresql+asyncpg://", 1)
+    elif dsn.startswith("postgres://"):
+        dsn = dsn.replace("postgres://", "postgresql+asyncpg://", 1)
+
+    return dsn
+
+
+def _ensure_engine() -> AsyncEngine:
+    global _engine, _session_maker
+    if _engine is None:
+        _engine = create_async_engine(
+            _get_async_dsn(),
+            pool_size=5,
+            max_overflow=5,
+            pool_timeout=10,
+            pool_pre_ping=True,
+            echo=False,
+        )
+        _session_maker = async_sessionmaker(
+            bind=_engine,
+            class_=AsyncSession,
+            expire_on_commit=False,
+        )
+        print("✅ Async database engine created (asyncpg)")
+    return _engine
+
+
+@asynccontextmanager
+async def get_session() -> AsyncGenerator[AsyncSession, None]:
+    """Provide an async session context manager."""
+    _ensure_engine()
+    assert _session_maker is not None
+    async with _session_maker() as session:
+        yield session
+
+
+async def dispose_engine() -> None:
+    """Shut down the async engine cleanly."""
+    global _engine, _session_maker
+    if _engine is not None:
+        await _engine.dispose()
+        _engine = None
+        _session_maker = None
+        print("ℹ️  Async database engine disposed")

ai-engine/data/db.py

@@ -0,0 +1,92 @@
+"""
+Synchronous psycopg2 database helper for the AI Engine.
+Uses a thread-safe connection pool for legacy V20+ endpoints.
+"""
+
+from __future__ import annotations
+
+import os
+from contextlib import contextmanager
+from typing import Generator
+
+import psycopg2
+from psycopg2 import pool
+from psycopg2.extensions import connection as PgConnection
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Safe default with no credentials — will fail fast if not configured.
+_DEFAULT_DSN = "postgresql://postgres:postgres@localhost:15432/boilerplate_db"
+
+
+def get_clean_dsn() -> str:
+    """
+    Return a psycopg2-compatible DSN from DATABASE_URL.
+
+    Handles DSN cleanup issues that break raw usage:
+      1. Prisma appends '?schema=public' which psycopg2 cannot parse.
+    """
+    dsn: str = os.getenv("DATABASE_URL", _DEFAULT_DSN)
+    connect_timeout: str = os.getenv("PGCONNECT_TIMEOUT", "5").strip() or "5"
+
+    # Strip Prisma's ?schema= query parameter while preserving any other query args.
+    if "?" in dsn:
+        base, query = dsn.split("?", 1)
+        kept_parts: list[str] = [
+            part for part in query.split("&") if part and not part.startswith("schema=")
+        ]
+        dsn = base if not kept_parts else f"{base}?{'&'.join(kept_parts)}"
+
+    # Force bounded DB connect attempts so API calls do not hang indefinitely.
+    if "connect_timeout=" not in dsn:
+        separator = "&" if "?" in dsn else "?"
+        dsn = f"{dsn}{separator}connect_timeout={connect_timeout}"
+    return dsn
+
+
+class Database:
+    _pool: pool.ThreadedConnectionPool | None = None
+
+    @classmethod
+    def initialize(cls) -> None:
+        if cls._pool is None:
+            dsn: str = get_clean_dsn()
+            try:
+                cls._pool = pool.ThreadedConnectionPool(
+                    minconn=1,
+                    maxconn=10,
+                    dsn=dsn,
+                )
+                print("✅ Database connection pool created")
+            except Exception as e:
+                print(f"❌ Failed to create DB pool: {e}")
+                raise
+
+    @classmethod
+    def get_conn(cls) -> PgConnection:
+        if cls._pool is None:
+            cls.initialize()
+        assert cls._pool is not None  # guaranteed by initialize()
+        return cls._pool.getconn()
+
+    @classmethod
+    def return_conn(cls, conn: PgConnection) -> None:
+        if cls._pool:
+            cls._pool.putconn(conn)
+
+    @classmethod
+    @contextmanager
+    def connection(cls) -> Generator[PgConnection, None, None]:
+        """Context manager for safe connection handling."""
+        conn: PgConnection = cls.get_conn()
+        try:
+            yield conn
+        finally:
+            cls.return_conn(conn)
+
+    @classmethod
+    def close_all(cls) -> None:
+        if cls._pool:
+            cls._pool.closeall()
+            print("ℹ️  Database connection pool closed")

ai-engine/data/league_reliability.json

@@ -0,0 +1,726 @@
+{
+  "version": "v1",
+  "description": "Per-league odds reliability scores computed from Brier Score analysis",
+  "min_matches_threshold": 50,
+  "total_leagues": 265,
+  "default_reliability": 0.35,
+  "lookup": {
+    "bx57cmq1edfq53ckfk791supi": 0.9476,
+    "55hcphd1ccc6eai1ms77460on": 0.9445,
+    "d9eaigzyfnfiraqc3ius757tl": 0.9402,
+    "1gxlzw2ezkyeykhcaa5x8ozkk": 0.9259,
+    "5jd0k2txwnq69frs79eulba8j": 0.9233,
+    "6694fff47wqxl10lrd9tb91f8": 0.9193,
+    "4jg7he1n3rb5dniq6hf49xorq": 0.9061,
+    "59tpnfrwnvhnhzmnvfyug68hj": 0.8988,
+    "ac42gi3penartj88fe9l6plpk": 0.8937,
+    "3j81qr7yc4gdnakfwnxf95ovh": 0.8771,
+    "9z5643nd06afqu01ea2wt8y4g": 0.8734,
+    "482ofyysbdbeoxauk19yg7tdt": 0.8722,
+    "ahl3vljaignq9ebaos4uqkrvo": 0.8696,
+    "8x3sbh85gc8qir50utw39jl04": 0.865,
+    "agpweohvn9tugnyl6ry4rhivp": 0.8428,
+    "4c1nfi2j1m731hcay25fcgndq": 0.8425,
+    "1j4ehtrbry9depwt6oghaq3lu": 0.8299,
+    "40yjcbx2sq6oq736iqqqczwt1": 0.8237,
+    "145hkd59i6foieuwr4mwi6wlq": 0.823,
+    "34pl8szyvrbwcmfkuocjm3r6t": 0.8227,
+    "cse5oqqt2pzfcy8uz6yz3tkbj": 0.8212,
+    "zs18qaehvhg3w1208874zvfa": 0.8176,
+    "57nu0wygurzkp6fuy5hhrtaa2": 0.8099,
+    "1eruend45vd20g9hbrpiggs5u": 0.8083,
+    "595nsvo7ykvoe690b1e4u5n56": 0.7987,
+    "6vq8j5p3av14nr3iuyi4okhjt": 0.793,
+    "486rhdgz7yc0sygziht7hje65": 0.7901,
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+      "odds_reliability": 0.8696
+    },
+    {
+      "league_id": "8x3sbh85gc8qir50utw39jl04",
+      "league_name": "UEFA Kadınlar Euro 2025 Elemeleri",
+      "match_count": 88,
+      "brier_score": 0.3421,
+      "heavy_fav_win_pct": 75.5,
+      "fav_win_pct": 61.4,
+      "odds_reliability": 0.865
+    },
+    {
+      "league_id": "agpweohvn9tugnyl6ry4rhivp",
+      "league_name": "Eredivisie Kadınlar",
+      "match_count": 51,
+      "brier_score": 0.3356,
+      "heavy_fav_win_pct": 72.0,
+      "fav_win_pct": 56.9,
+      "odds_reliability": 0.8428
+    },
+    {
+      "league_id": "4c1nfi2j1m731hcay25fcgndq",
+      "league_name": "Avrupa Ligi",
+      "match_count": 242,
+      "brier_score": 0.3625,
+      "heavy_fav_win_pct": 77.6,
+      "fav_win_pct": 61.6,
+      "odds_reliability": 0.8425
+    },
+    {
+      "league_id": "1j4ehtrbry9depwt6oghaq3lu",
+      "league_name": "Süper Lig",
+      "match_count": 84,
+      "brier_score": 0.3201,
+      "heavy_fav_win_pct": 65.9,
+      "fav_win_pct": 60.7,
+      "odds_reliability": 0.8299
+    },
+    {
+      "league_id": "40yjcbx2sq6oq736iqqqczwt1",
+      "league_name": "DK Elemeler",
+      "match_count": 88,
+      "brier_score": 0.3383,
+      "heavy_fav_win_pct": 68.6,
+      "fav_win_pct": 55.7,
+      "odds_reliability": 0.8237
+    },
+    {
+      "league_id": "145hkd59i6foieuwr4mwi6wlq",
+      "league_name": "Pro Lig",
+      "match_count": 143,
+      "brier_score": 0.3546,
+      "heavy_fav_win_pct": 73.8,
+      "fav_win_pct": 60.1,
+      "odds_reliability": 0.823
+    },
+    {
+      "league_id": "34pl8szyvrbwcmfkuocjm3r6t",
+      "league_name": "LaLiga",
+      "match_count": 364,
+      "brier_score": 0.3773,
+      "heavy_fav_win_pct": 80.2,
+      "fav_win_pct": 56.6,
+      "odds_reliability": 0.8227
+    },
+    {
+      "league_id": "cse5oqqt2pzfcy8uz6yz3tkbj",
+      "league_name": "CAF Şampiyonlar Ligi",
+      "match_count": 91,
+      "brier_score": 0.3513,
+      "heavy_fav_win_pct": 73.9,
+      "fav_win_pct": 57.1,
+      "odds_reliability": 0.8212
+    },
+    {
+      "league_id": "zs18qaehvhg3w1208874zvfa",
+      "league_name": "1. Lig",
+      "match_count": 225,
+      "brier_score": 0.3744,
+      "heavy_fav_win_pct": 82.1,
+      "fav_win_pct": 59.6,
+      "odds_reliability": 0.8176
+    },
+    {
+      "league_id": "57nu0wygurzkp6fuy5hhrtaa2",
+      "league_name": "1. Lig",
+      "match_count": 286,
+      "brier_score": 0.3626,
+      "heavy_fav_win_pct": 72.9,
+      "fav_win_pct": 59.1,
+      "odds_reliability": 0.8099
+    },
+    {
+      "league_id": "1eruend45vd20g9hbrpiggs5u",
+      "league_name": "Botola Pro",
+      "match_count": 265,
+      "brier_score": 0.3625,
+      "heavy_fav_win_pct": 72.9,
+      "fav_win_pct": 50.2,
+      "odds_reliability": 0.8083
+    },
+    {
+      "league_id": "595nsvo7ykvoe690b1e4u5n56",
+      "league_name": "UEFA Uluslar Ligi",
+      "match_count": 67,
+      "brier_score": 0.3687,
+      "heavy_fav_win_pct": 83.3,
+      "fav_win_pct": 50.7,
+      "odds_reliability": 0.7987
+    },
+    {
+      "league_id": "6vq8j5p3av14nr3iuyi4okhjt",
+      "league_name": "Süper Lig Kadınlar",
+      "match_count": 70,
+      "brier_score": 0.356,
+      "heavy_fav_win_pct": 73.5,
+      "fav_win_pct": 58.6,
+      "odds_reliability": 0.793
+    },
+    {
+      "league_id": "486rhdgz7yc0sygziht7hje65",
+      "league_name": "Kupa",
+      "match_count": 62,
+      "brier_score": 0.3704,
+      "heavy_fav_win_pct": 81.1,
+      "fav_win_pct": 66.1,
+      "odds_reliability": 0.7901
+    },
+    {
+      "league_id": "9hh6n2f84k31zmlcxyvmc1w2y",
+      "league_name": "2. Lig",
+      "match_count": 204,
+      "brier_score": 0.357,
+      "heavy_fav_win_pct": 69.2,
+      "fav_win_pct": 62.3,
+      "odds_reliability": 0.789
+    },
+    {
+      "league_id": "3n5046abeu3x482ds3jwda238",
+      "league_name": "WE Lig Kadınlar",
+      "match_count": 102,
+      "brier_score": 0.3761,
+      "heavy_fav_win_pct": 85.4,
+      "fav_win_pct": 58.8,
+      "odds_reliability": 0.7863
+    },
+    {
+      "league_id": "8yi6ejjd1zudcqtbn07haahg6",
+      "league_name": "Premier Lig",
+      "match_count": 302,
+      "brier_score": 0.3712,
+      "heavy_fav_win_pct": 72.1,
+      "fav_win_pct": 56.3,
+      "odds_reliability": 0.7752
+    },
+    {
+      "league_id": "byhmntnl1b4lxw0zz21im3zkd",
+      "league_name": "Kupa",
+      "match_count": 96,
+      "brier_score": 0.3528,
+      "heavy_fav_win_pct": 68.2,
+      "fav_win_pct": 58.3,
+      "odds_reliability": 0.7719
+    },
+    {
+      "league_id": "2bmwykmdlcc2u1c40ytoc39vy",
+      "league_name": "Açık Kupası",
+      "match_count": 93,
+      "brier_score": 0.3807,
+      "heavy_fav_win_pct": 84.6,
+      "fav_win_pct": 66.7,
+      "odds_reliability": 0.7668
+    },
+    {
+      "league_id": "82jkgccg7phfjpd0mltdl3pat",
+      "league_name": "Süper Lig",
+      "match_count": 289,
+      "brier_score": 0.3782,
+      "heavy_fav_win_pct": 74.0,
+      "fav_win_pct": 57.4,
+      "odds_reliability": 0.7643
+    },
+    {
+      "league_id": "2nttcoriwf5co73vmz1vr8frm",
+      "league_name": "Nesine 2. Lig",
+      "match_count": 525,
+      "brier_score": 0.3782,
+      "heavy_fav_win_pct": 71.8,
+      "fav_win_pct": 55.2,
+      "odds_reliability": 0.7641
+    },
+    {
+      "league_id": "dr2xk7muj8aqcjdz2b3li1c0k",
+      "league_name": "Meistaradeildin",
+      "match_count": 129,
+      "brier_score": 0.3714,
+      "heavy_fav_win_pct": 73.6,
+      "fav_win_pct": 61.2,
+      "odds_reliability": 0.759
+    },
+    {
+      "league_id": "4yngyfinzd6bb1k7anqtqs0wt",
+      "league_name": "Premier Lig",
+      "match_count": 195,
+      "brier_score": 0.3772,
+      "heavy_fav_win_pct": 74.4,
+      "fav_win_pct": 57.4,
+      "odds_reliability": 0.7586
+    },
+    {
+      "league_id": "eog6knrkfei68si736fpquyzc",
+      "league_name": "Lig Kupası",
+      "match_count": 120,
+      "brier_score": 0.3632,
+      "heavy_fav_win_pct": 69.9,
+      "fav_win_pct": 66.7,
+      "odds_reliability": 0.756
+    },
+    {
+      "league_id": "eg6s9f1jj7jr6stmbosn0g6c8",
+      "league_name": "Süper Lig",
+      "match_count": 108,
+      "brier_score": 0.3657,
+      "heavy_fav_win_pct": 71.2,
+      "fav_win_pct": 55.6,
+      "odds_reliability": 0.7538
+    },
+    {
+      "league_id": "ae1wva3zrzcp2zd15gpvsntg6",
+      "league_name": "Ulusal Lig",
+      "match_count": 278,
+      "brier_score": 0.3783,
+      "heavy_fav_win_pct": 72.7,
+      "fav_win_pct": 55.0,
+      "odds_reliability": 0.7517
+    },
+    {
+      "league_id": "cesdwwnxbc5fmajgroc0hqzy2",
+      "league_name": "Hazırlık Maçları Ülkeler",
+      "match_count": 235,
+      "brier_score": 0.3669,
+      "heavy_fav_win_pct": 67.6,
+      "fav_win_pct": 56.2,
+      "odds_reliability": 0.7466
+    },
+    {
+      "league_id": "8k1xcsyvxapl4jlsluh3eomre",
+      "league_name": "Premier Lig",
+      "match_count": 328,
+      "brier_score": 0.385,
+      "heavy_fav_win_pct": 74.2,
+      "fav_win_pct": 45.7,
+      "odds_reliability": 0.7463
+    },
+    {
+      "league_id": "bdtat25m14jy85y484z3e6lf",
+      "league_name": "Kupa",
+      "match_count": 90,
+      "brier_score": 0.3772,
+      "heavy_fav_win_pct": 75.7,
+      "fav_win_pct": 55.6,
+      "odds_reliability": 0.7437
+    },
+    {
+      "league_id": "iu1vi94p4p28oozl1h9bvplr",
+      "league_name": "1. Lig",
+      "match_count": 158,
+      "brier_score": 0.3729,
+      "heavy_fav_win_pct": 71.2,
+      "fav_win_pct": 50.0,
+      "odds_reliability": 0.7411
+    },
+    {
+      "league_id": "1r097lpxe0xn03ihb7wi98kao",
+      "league_name": "Serie A",
+      "match_count": 359,
+      "brier_score": 0.3732,
+      "heavy_fav_win_pct": 67.8,
+      "fav_win_pct": 56.5,
+      "odds_reliability": 0.7391
+    },
+    {
+      "league_id": "2kwbbcootiqqgmrzs6o5inle5",
+      "league_name": "Premier Lig",
+      "match_count": 369,
+      "brier_score": 0.3791,
+      "heavy_fav_win_pct": 70.2,
+      "fav_win_pct": 54.2,
+      "odds_reliability": 0.7386
+    },
+    {
+      "league_id": "9fuwphq8kvugrlc3ckm7k8wes",
+      "league_name": "Ligler Kupası",
+      "match_count": 143,
+      "brier_score": 0.3934,
+      "heavy_fav_win_pct": 81.6,
+      "fav_win_pct": 50.3,
+      "odds_reliability": 0.7358
+    },
+    {
+      "league_id": "civf31q1inxohs4a03y8reetf",
+      "league_name": "Premier Lig",
+      "match_count": 320,
+      "brier_score": 0.3721,
+      "heavy_fav_win_pct": 67.2,
+      "fav_win_pct": 57.8,
+      "odds_reliability": 0.735
+    },
+    {
+      "league_id": "ili150pwfuf39f7yfdch9lhw",
+      "league_name": "UEFA U21 Şampiyonası Elemeler",
+      "match_count": 112,
+      "brier_score": 0.3715,
+      "heavy_fav_win_pct": 70.4,
+      "fav_win_pct": 67.9,
+      "odds_reliability": 0.7286
+    },
+    {
+      "league_id": "abs7n2ae3oydilk0tgmpnsj89",
+      "league_name": "Azadegan Ligi",
+      "match_count": 217,
+      "brier_score": 0.3801,
+      "heavy_fav_win_pct": 71.4,
+      "fav_win_pct": 45.2,
+      "odds_reliability": 0.7277
+    },
+    {
+      "league_id": "9nbpdi9q3ywcm4q0j5u0ekwcq",
+      "league_name": "Serie D",
+      "match_count": 232,
+      "brier_score": 0.3718,
+      "heavy_fav_win_pct": 67.2,
+      "fav_win_pct": 54.7,
+      "odds_reliability": 0.7254
+    }
+  ]
+}

ai-engine/features/rolling_features.py

@@ -0,0 +1,243 @@
+"""
+V27 Rolling Window Feature Calculator
+======================================
+Computes rolling averages over 5/10/20 match windows,
+with home/away splits and trend detection.
+"""
+from __future__ import annotations
+from typing import Dict, List, Tuple
+import math
+
+
+def calc_rolling_features(
+    team_matches: List[Tuple],  # [(mst, is_home, team_goals, opp_goals, opp_id), ...]
+    before_date: int,
+    team_is_home: bool,
+) -> Dict[str, float]:
+    """Calculate rolling window features for a team before a given date."""
+    valid = [m for m in team_matches if m[0] < before_date]
+
+    defaults = {
+        "rolling5_goals_avg": 1.3, "rolling5_conceded_avg": 1.2,
+        "rolling10_goals_avg": 1.3, "rolling10_conceded_avg": 1.2,
+        "rolling20_goals_avg": 1.3, "rolling20_conceded_avg": 1.2,
+        "rolling5_clean_sheets": 0.25,
+        "venue_goals_avg": 1.3, "venue_conceded_avg": 1.2,
+        "goal_trend": 0.0,
+    }
+
+    if len(valid) < 3:
+        return defaults
+
+    result = {}
+
+    for window in [5, 10, 20]:
+        recent = valid[-window:] if len(valid) >= window else valid
+        n = len(recent)
+        g_sum = sum(m[2] for m in recent)
+        c_sum = sum(m[3] for m in recent)
+        result[f"rolling{window}_goals_avg"] = g_sum / n
+        result[f"rolling{window}_conceded_avg"] = c_sum / n
+
+    # Clean sheet rate (last 5)
+    r5 = valid[-5:] if len(valid) >= 5 else valid
+    result["rolling5_clean_sheets"] = sum(1 for m in r5 if m[3] == 0) / len(r5)
+
+    # Venue-specific (home-only or away-only)
+    venue_matches = [m for m in valid if m[1] == team_is_home]
+    if venue_matches:
+        vm = venue_matches[-10:] if len(venue_matches) >= 10 else venue_matches
+        result["venue_goals_avg"] = sum(m[2] for m in vm) / len(vm)
+        result["venue_conceded_avg"] = sum(m[3] for m in vm) / len(vm)
+    else:
+        result["venue_goals_avg"] = defaults["venue_goals_avg"]
+        result["venue_conceded_avg"] = defaults["venue_conceded_avg"]
+
+    # Goal trend: compare last 3 vs previous 3
+    if len(valid) >= 6:
+        last3 = sum(m[2] for m in valid[-3:]) / 3
+        prev3 = sum(m[2] for m in valid[-6:-3]) / 3
+        result["goal_trend"] = last3 - prev3
+    else:
+        result["goal_trend"] = 0.0
+
+    return result
+
+
+def calc_league_quality(
+    all_matches: List[Tuple],  # all FT matches in this league
+) -> Dict[str, float]:
+    """Calculate league-level quality features."""
+    defaults = {
+        "league_home_win_rate": 0.45,
+        "league_draw_rate": 0.25,
+        "league_btts_rate": 0.50,
+        "league_ou25_rate": 0.50,
+        "league_reliability_score": 0.50,
+    }
+
+    if len(all_matches) < 20:
+        return defaults
+
+    n = len(all_matches)
+    home_wins = sum(1 for m in all_matches if m[2] > m[3])
+    draws = sum(1 for m in all_matches if m[2] == m[3])
+    btts = sum(1 for m in all_matches if m[2] > 0 and m[3] > 0)
+    ou25 = sum(1 for m in all_matches if (m[2] + m[3]) > 2.5)
+
+    hw_rate = home_wins / n
+    dr_rate = draws / n
+    btts_rate = btts / n
+    ou25_rate = ou25 / n
+
+    # Reliability: leagues closer to averages are more predictable
+    predictability = 1.0 - abs(hw_rate - 0.45) - abs(dr_rate - 0.27) * 0.5
+    reliability = max(0.2, min(0.95, predictability))
+
+    return {
+        "league_home_win_rate": round(hw_rate, 4),
+        "league_draw_rate": round(dr_rate, 4),
+        "league_btts_rate": round(btts_rate, 4),
+        "league_ou25_rate": round(ou25_rate, 4),
+        "league_reliability_score": round(reliability, 4),
+    }
+
+
+def calc_time_features(
+    team_matches: List[Tuple],
+    match_mst: int,
+) -> Dict[str, float]:
+    """Calculate time-based features."""
+    from datetime import datetime
+
+    # Days since last match
+    valid = [m for m in team_matches if m[0] < match_mst]
+    if valid:
+        last_mst = valid[-1][0]
+        days_rest = (match_mst - last_mst) / 86_400_000  # ms to days
+        days_rest = min(days_rest, 60.0)  # cap at 60 days
+    else:
+        days_rest = 14.0
+
+    # Month and season flags
+    try:
+        dt = datetime.utcfromtimestamp(match_mst / 1000)
+        month = dt.month
+        is_season_start = 1.0 if month in (7, 8) else 0.0
+        is_season_end = 1.0 if month in (5, 6) else 0.0
+    except Exception:
+        month = 6
+        is_season_start = 0.0
+        is_season_end = 0.0
+
+    return {
+        "days_rest": round(days_rest, 2),
+        "match_month": month,
+        "is_season_start": is_season_start,
+        "is_season_end": is_season_end,
+    }
+
+
+def calc_advanced_h2h(
+    team_matches: List[Tuple],
+    home_id: int,
+    away_id: int,
+    before_date: int,
+) -> Dict[str, float]:
+    """Calculate advanced H2H features."""
+    defaults = {
+        "h2h_home_goals_avg": 1.3,
+        "h2h_away_goals_avg": 1.1,
+        "h2h_recent_trend": 0.0,
+        "h2h_venue_advantage": 0.0,
+    }
+
+    h2h = [m for m in team_matches if m[4] == away_id and m[0] < before_date]
+    if not h2h:
+        return defaults
+
+    recent = h2h[-10:]
+    home_goals_total = 0
+    away_goals_total = 0
+    venue_home_wins = 0
+    venue_total = 0
+
+    for mst, is_home, team_goals, opp_goals, _ in recent:
+        if is_home:
+            home_goals_total += team_goals
+            away_goals_total += opp_goals
+            venue_total += 1
+            if team_goals > opp_goals:
+                venue_home_wins += 1
+        else:
+            home_goals_total += opp_goals
+            away_goals_total += team_goals
+
+    n = len(recent)
+    result = {
+        "h2h_home_goals_avg": home_goals_total / n,
+        "h2h_away_goals_avg": away_goals_total / n,
+        "h2h_venue_advantage": venue_home_wins / venue_total if venue_total > 0 else 0.5,
+    }
+
+    # Recent trend: last 3 vs overall
+    if len(h2h) >= 4:
+        last3_pts = sum(
+            1.0 if m[2] > m[3] else (0.5 if m[2] == m[3] else 0.0)
+            for m in h2h[-3:]
+        ) / 3
+        overall_pts = sum(
+            1.0 if m[2] > m[3] else (0.5 if m[2] == m[3] else 0.0)
+            for m in h2h
+        ) / len(h2h)
+        result["h2h_recent_trend"] = round(last3_pts - overall_pts, 4)
+    else:
+        result["h2h_recent_trend"] = 0.0
+
+    return result
+
+
+def calc_strength_diff(
+    home_form: Dict[str, float],
+    away_form: Dict[str, float],
+    home_elo: Dict[str, float],
+    away_elo: Dict[str, float],
+    home_momentum: float,
+    away_momentum: float,
+    upset_potential: float,
+) -> Dict[str, float]:
+    """Calculate strength differential features."""
+    # Attack vs Defense mismatches
+    h_attack = home_form.get("goals_avg", 1.3)
+    a_defense = away_form.get("conceded_avg", 1.2)
+    a_attack = away_form.get("goals_avg", 1.3)
+    h_defense = home_form.get("conceded_avg", 1.2)
+
+    atk_def_home = h_attack - a_defense  # positive = home attack > away defense
+    atk_def_away = a_attack - h_defense
+
+    # XG diff approximation
+    xg_diff = (h_attack + a_defense) / 2 - (a_attack + h_defense) / 2
+
+    # Form × Momentum interaction
+    form_mom = (home_momentum - away_momentum) * (
+        home_form.get("scoring_rate", 0.75) - away_form.get("scoring_rate", 0.75)
+    )
+
+    # ELO-Form consistency
+    elo_diff = home_elo.get("overall", 1500) - away_elo.get("overall", 1500)
+    form_diff = h_attack - a_attack
+    elo_form_consistency = 1.0 if (elo_diff > 0 and form_diff > 0) or (elo_diff < 0 and form_diff < 0) else 0.0
+
+    # Upset × ELO gap
+    elo_gap = abs(elo_diff)
+    upset_x_elo = upset_potential * (elo_gap / 400.0)
+
+    return {
+        "attack_vs_defense_home": round(atk_def_home, 4),
+        "attack_vs_defense_away": round(atk_def_away, 4),
+        "xg_diff": round(xg_diff, 4),
+        "form_momentum_interaction": round(form_mom, 4),
+        "elo_form_consistency": elo_form_consistency,
+        "upset_x_elo_gap": round(upset_x_elo, 4),
+    }

ai-engine/features/upset_engine_v2.py

@@ -15,13 +15,9 @@ Orijinal Faktörler:
 - Tarihsel upset pattern
 """
 
-import os
-import sys
 from typing import Dict, Any, Optional, Tuple, List
 from dataclasses import dataclass, field
 
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
 try:
     import psycopg2
     from psycopg2.extras import RealDictCursor

ai-engine/main.py

@@ -7,16 +7,24 @@ import time
 from contextlib import asynccontextmanager
 from typing import Any
 
+from datetime import datetime
+
 import uvicorn
 from dotenv import load_dotenv
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
+import subprocess
 from pydantic import BaseModel
 
+try:
     from models.basketball_v25 import get_basketball_v25_predictor
+    HAS_BASKETBALL = True
+except ImportError:
+    HAS_BASKETBALL = False
 from services.single_match_orchestrator import get_single_match_orchestrator
-from data.database import dispose_engine
+from services.v26_shadow_engine import get_v26_shadow_engine
+from models.league_model import get_league_model_loader
 
 load_dotenv()
 
@@ -33,12 +41,30 @@ class CouponRequest(BaseModel):
     min_confidence: float | None = None
 
 
+class RetrainRequest(BaseModel):
+    reason: str | None = "manual"
+    markets: str | None = None  # comma-separated, e.g. "MS,OU25,BTTS"
+    trials: int | None = 50
+
+
+# ─── Retrain state tracking ──────────────────────────────────
+_retrain_state: dict[str, Any] = {
+    "running": False,
+    "last_started": None,
+    "last_completed": None,
+    "last_status": None,
+    "last_error": None,
+    "pid": None,
+}
+
+
 @asynccontextmanager
 async def lifespan(_: FastAPI):
     try:
-        print("🚀 Initializing V25 orchestrator...", flush=True)
+        print("🚀 Initializing V28 orchestrator...", flush=True)
         get_single_match_orchestrator()
-        print("✅ V25 orchestrator ready", flush=True)
+        get_v26_shadow_engine()
+        print("✅ V28 orchestrator ready", flush=True)
     except Exception as error:
         print(f"❌ Failed to initialize orchestrator: {error}", flush=True)
         import traceback
@@ -47,14 +73,11 @@ async def lifespan(_: FastAPI):
 
     yield
 
-    # Cleanup async DB connections on shutdown
-    await dispose_engine()
-
 
 app = FastAPI(
     title="Suggest-Bet AI Engine",
-    version="25.0.0",
-    description="V25 Single Match Prediction Package API",
+    version="28.0.0",
+    description="V28 Single Match Prediction Package API",
     lifespan=lifespan,
 )
 
@@ -102,8 +125,9 @@ async def global_exception_handler(_: Request, exc: Exception):
 @app.get("/")
 def read_root() -> dict[str, Any]:
     return {
-        "status": "Suggest-Bet AI Engine v25",
-        "engine": "V25 Single Match Orchestrator",
+        "status": "Suggest-Bet AI Engine v28",
+        "engine": "V28 Single Match Orchestrator",
+        "mode": os.getenv("AI_ENGINE_MODE", "v28"),
         "routes": [
             "POST /v20plus/analyze/{match_id}",
             "GET /v20plus/analyze-htms/{match_id}",
@@ -111,6 +135,8 @@ def read_root() -> dict[str, Any]:
             "GET /v20plus/reversal-watchlist",
             "POST /v20plus/coupon",
             "GET /v20plus/daily-banker",
+            "POST /v1/admin/retrain",
+            "GET /v1/admin/retrain/status",
         ],
     }
 
@@ -118,33 +144,70 @@ def read_root() -> dict[str, Any]:
 @app.get("/health")
 def health_check() -> dict[str, Any]:
     try:
-        get_single_match_orchestrator()
+        orchestrator = get_single_match_orchestrator()
+        shadow_engine = get_v26_shadow_engine()
+
+        # Per-market V25 model status
+        v25_readiness: dict[str, Any] = {"fully_loaded": False}
+        try:
+            v25_predictor = orchestrator._get_v25_predictor()
+            v25_readiness = v25_predictor.readiness_summary()
+        except Exception as v25_err:
+            v25_readiness = {"fully_loaded": False, "error": str(v25_err)}
+
+        if HAS_BASKETBALL:
             basketball_predictor = get_basketball_v25_predictor()
             basketball_readiness = basketball_predictor.readiness_summary()
-        ready = bool(basketball_readiness["fully_loaded"])
+            ready = bool(basketball_readiness.get("fully_loaded", True))
+        else:
+            basketball_readiness = {"fully_loaded": False, "error": "Basketball module not found"}
+            ready = True
+
+        league_readiness = get_league_model_loader().readiness_summary()
+        overall_ready = ready and v25_readiness.get("fully_loaded", False)
         return {
-            "status": "healthy" if ready else "degraded",
-            "engine": "v25.main",
-            "ready": ready,
+            "status": "healthy" if overall_ready else "degraded",
+            "engine": "v28.main",
+            "mode": os.getenv("AI_ENGINE_MODE", "v28"),
+            "ready": overall_ready,
+            "v25_football": v25_readiness,
+            "league_specific": league_readiness,
             "basketball_v25": basketball_readiness,
+            "v26_shadow": shadow_engine.readiness_summary(),
+            "prediction_service_ready": True,
+            "model_loaded": overall_ready,
+            "orchestrator_mode": getattr(orchestrator, "engine_mode", "v28"),
         }
     except Exception as error:
         return {"status": "unhealthy", "ready": False, "error": str(error)}
 
 
+_REQUIRED_RESPONSE_FIELDS = ("match_info", "market_board", "main_pick", "bet_summary", "data_quality")
+
+
 @app.post("/v20plus/analyze/{match_id}")
 async def analyze_match_v20plus(match_id: str) -> dict[str, Any]:
+    started_at = time.time()
     orchestrator = get_single_match_orchestrator()
-    result = orchestrator.analyze_match(match_id)
+    result = await asyncio.to_thread(orchestrator.analyze_match, match_id)
+    elapsed_ms = int((time.time() - started_at) * 1000)
+
     if not result:
         raise HTTPException(status_code=404, detail=f"Match not found: {match_id}")
+
+    # Response validation: log missing required fields (non-fatal)
+    missing_fields = [f for f in _REQUIRED_RESPONSE_FIELDS if f not in result]
+    if missing_fields:
+        print(f"⚠️ [API] analyze/{match_id} response missing fields: {missing_fields} ({elapsed_ms}ms)")
+
+    result["timing_ms"] = elapsed_ms
     return result
 
 
 @app.get("/v20plus/analyze-htms/{match_id}")
 async def analyze_match_htms_v20plus(match_id: str) -> dict[str, Any]:
     orchestrator = get_single_match_orchestrator()
-    result = orchestrator.analyze_match_htms(match_id)
+    result = await asyncio.to_thread(orchestrator.analyze_match_htms, match_id)
     if not result:
         raise HTTPException(status_code=404, detail=f"Match not found: {match_id}")
     return result
@@ -196,7 +259,7 @@ async def analyze_match_htft_v20plus(match_id: str, timeout_sec: int = 30) -> di
             key=lambda item: float(item[1]),
         )
     return {
-        "engine": "v25.main",
+        "engine": "v28.main",
         "match_info": result.get("match_info", {}),
         "timing_ms": int((time.time() - started_at) * 1000),
         "ht_ft_probs": htft_probs,
@@ -215,11 +278,12 @@ async def analyze_match_htft_v20plus(match_id: str, timeout_sec: int = 30) -> di
 @app.post("/v20plus/coupon")
 async def generate_coupon_v20plus(request: CouponRequest) -> dict[str, Any]:
     orchestrator = get_single_match_orchestrator()
-    return orchestrator.build_coupon(
-        match_ids=request.match_ids,
-        strategy=request.strategy or "BALANCED",
-        max_matches=request.max_matches,
-        min_confidence=request.min_confidence,
+    return await asyncio.to_thread(
+        orchestrator.build_coupon,
+        request.match_ids,
+        request.strategy or "BALANCED",
+        request.max_matches,
+        request.min_confidence,
     )
 
 
@@ -229,7 +293,7 @@ async def get_daily_banker_v20plus(count: int = 3) -> dict[str, Any]:
         raise HTTPException(status_code=400, detail="count must be >= 1")
 
     orchestrator = get_single_match_orchestrator()
-    bankers = orchestrator.get_daily_bankers(count=count)
+    bankers = await asyncio.to_thread(orchestrator.get_daily_bankers, count)
     return {"count": len(bankers), "bankers": bankers}
 
 @app.get("/v20plus/reversal-watchlist")
@@ -247,14 +311,120 @@ async def get_reversal_watchlist_v20plus(
         raise HTTPException(status_code=400, detail="min_score must be between 0 and 100")
 
     orchestrator = get_single_match_orchestrator()
-    return orchestrator.get_reversal_watchlist(
-        count=count,
-        horizon_hours=horizon_hours,
-        min_score=min_score,
-        top_leagues_only=top_leagues_only,
+    return await asyncio.to_thread(
+        orchestrator.get_reversal_watchlist,
+        count,
+        horizon_hours,
+        min_score,
+        top_leagues_only,
     )
 
 
+# ─── ADMIN: Retrain Pipeline ─────────────────────────────────
+
+def _run_retrain_pipeline(markets: str | None, trials: int):
+    """Background function: extract data → train model → reload."""
+    global _retrain_state
+    ai_dir = os.path.dirname(os.path.abspath(__file__))
+    scripts_dir = os.path.join(ai_dir, "scripts")
+    python = os.path.join(ai_dir, "venv", "bin", "python3")
+    if not os.path.exists(python):
+        python = sys.executable  # fallback
+
+    try:
+        # Step 1: Extract training data
+        print("🔄 [RETRAIN] Step 1/3: Extracting training data...", flush=True)
+        result = subprocess.run(
+            [python, os.path.join(scripts_dir, "extract_training_data.py")],
+            capture_output=True, text=True, timeout=600, cwd=ai_dir,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(f"Extract failed:\n{result.stderr[-500:]}")
+        print(f"✅ [RETRAIN] Extract done", flush=True)
+
+        # Step 2: Train V25 Pro
+        print("🔄 [RETRAIN] Step 2/3: Training V25 Pro model...", flush=True)
+        train_cmd = [python, os.path.join(scripts_dir, "train_v25_pro.py")]
+        if markets:
+            train_cmd += ["--markets", markets]
+        train_cmd += ["--trials", str(trials)]
+
+        result = subprocess.run(
+            train_cmd, capture_output=True, text=True, timeout=3600, cwd=ai_dir,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(f"Training failed:\n{result.stderr[-500:]}")
+        print(f"✅ [RETRAIN] Training done", flush=True)
+
+        # Step 3: Reload models in memory
+        print("🔄 [RETRAIN] Step 3/3: Reloading models...", flush=True)
+        try:
+            orchestrator = get_single_match_orchestrator()
+            v25 = orchestrator._get_v25_predictor()
+            v25._loaded = False
+            v25.load_models()
+            print("✅ [RETRAIN] Models reloaded in memory", flush=True)
+        except Exception as reload_err:
+            print(f"⚠️ [RETRAIN] Hot reload failed (restart needed): {reload_err}", flush=True)
+
+        _retrain_state.update({
+            "running": False,
+            "last_completed": datetime.now().isoformat(),
+            "last_status": "success",
+            "last_error": None,
+        })
+        print("🎉 [RETRAIN] Pipeline complete!", flush=True)
+
+    except Exception as err:
+        _retrain_state.update({
+            "running": False,
+            "last_completed": datetime.now().isoformat(),
+            "last_status": "failed",
+            "last_error": str(err),
+        })
+        print(f"❌ [RETRAIN] Pipeline failed: {err}", flush=True)
+
+
+@app.post("/v1/admin/retrain")
+async def admin_retrain(request: RetrainRequest) -> dict[str, Any]:
+    """Trigger full retrain pipeline: extract → train → reload."""
+    if _retrain_state["running"]:
+        return {
+            "status": "already_running",
+            "message": f"Retrain in progress since {_retrain_state['last_started']}",
+        }
+
+    _retrain_state.update({
+        "running": True,
+        "last_started": datetime.now().isoformat(),
+        "last_status": "running",
+        "last_error": None,
+    })
+
+    # Run in background thread
+    import threading
+    thread = threading.Thread(
+        target=_run_retrain_pipeline,
+        args=(request.markets, request.trials or 50),
+        daemon=True,
+    )
+    thread.start()
+
+    return {
+        "status": "triggered",
+        "message": "Retrain pipeline started in background",
+        "reason": request.reason,
+        "markets": request.markets or "all",
+        "trials": request.trials or 50,
+    }
+
+
+@app.get("/v1/admin/retrain/status")
+async def admin_retrain_status() -> dict[str, Any]:
+    """Check retrain pipeline status."""
+    return {**_retrain_state}
+
+
 if __name__ == "__main__":
     port = int(os.getenv("PORT", "8000"))
     uvicorn.run("main:app", host="0.0.0.0", port=port, reload=True)

ai-engine/models/calibration.py

@@ -0,0 +1,456 @@
+"""
+Calibration Module for XGBoost Models
+=====================================
+Calibrates raw probabilities from XGBoost models using Isotonic Regression.
+Ensures that a predicted probability of 70% actually corresponds to a 70% win rate.
+
+Usage:
+    from ai_engine.models.calibration import Calibrator
+    calibrator = Calibrator()
+    calibrated_prob = calibrator.calibrate("ms", raw_prob)
+    
+    # Training new calibration models:
+    calibrator.train_calibration(valid_df, market="ms")
+"""
+
+import os
+import pickle
+import json
+import numpy as np
+import pandas as pd
+from datetime import datetime
+from typing import Dict, List, Optional, Tuple, Any
+from sklearn.isotonic import IsotonicRegression
+from sklearn.calibration import calibration_curve
+from sklearn.metrics import brier_score_loss
+
+AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+CALIBRATION_DIR = os.path.join(AI_ENGINE_DIR, "models", "calibration")
+
+os.makedirs(CALIBRATION_DIR, exist_ok=True)
+
+# Supported markets for calibration
+SUPPORTED_MARKETS = [
+    "ms",        # Match Result (1X2) - multi-class, calibrated per class
+    "ms_home",   # Standard Home win probability
+    "ms_home_heavy_fav", # Context: home odds <= 1.40
+    "ms_home_fav",       # Context: 1.40 < home odds <= 1.80
+    "ms_home_balanced",  # Context: 1.80 < home odds <= 2.50
+    "ms_home_underdog",  # Context: home odds > 2.50
+    "ms_draw",   # Draw probability  
+    "ms_away",   # Away win probability
+    "ou15",      # Over/Under 1.5
+    "ou25",      # Over/Under 2.5
+    "ou35",      # Over/Under 3.5
+    "btts",      # Both Teams to Score
+    "ht_ft",     # Half-Time/Full-Time
+    "dc",        # Double Chance
+    "ht",        # Half-Time Result
+    "ht_home",   # Half-Time Home win
+    "ht_draw",   # Half-Time Draw
+    "ht_away",   # Half-Time Away win
+]
+
+
+class CalibrationMetrics:
+    """Stores calibration quality metrics for a market."""
+    
+    def __init__(self):
+        self.brier_score: float = 0.0
+        self.calibration_error: float = 0.0
+        self.sample_count: int = 0
+        self.last_trained: str = ""
+        self.mean_predicted: float = 0.0
+        self.mean_actual: float = 0.0
+        
+    def to_dict(self) -> Dict:
+        return {
+            "brier_score": round(self.brier_score, 4),
+            "calibration_error": round(self.calibration_error, 4),
+            "sample_count": self.sample_count,
+            "last_trained": self.last_trained,
+            "mean_predicted": round(self.mean_predicted, 4),
+            "mean_actual": round(self.mean_actual, 4),
+        }
+
+
+class Calibrator:
+    """
+    Probability calibration using Isotonic Regression.
+    
+    Isotonic Regression is a non-parametric method that fits a piecewise
+    constant function that is monotonically increasing. It's ideal for
+    calibrating probabilities because:
+    
+    1. It preserves ranking (if P(A) > P(B) before, P(A) > P(B) after)
+    2. It doesn't assume a specific distribution shape
+    3. It can correct systematic over/under-confidence
+    
+    Example:
+        # Before calibration: model predicts 70% but actual win rate is 60%
+        # After calibration: model predicts 70% → calibrated to 60%
+    """
+    
+    def __init__(self):
+        self.calibrators: Dict[str, IsotonicRegression] = {}
+        self.metrics: Dict[str, CalibrationMetrics] = {}
+        # Less aggressive shrinkage — only meaningful overconfident bands are pulled.
+        # Default raised from ~0.85-0.90 to 0.95+ since the orchestrator and config
+        # already apply market-level multipliers; double-shrinkage was the root cause
+        # of 24-35pt avg calibrated-vs-raw drops in production traces.
+        self.heuristic_fallback: Dict[str, float] = {
+            "ms": 0.96,
+            "ms_home": 0.96,
+            "ms_home_heavy_fav": 0.98,
+            "ms_home_fav": 0.96,
+            "ms_home_balanced": 0.94,
+            "ms_home_underdog": 0.92,
+            "ms_draw": 0.94,
+            "ms_away": 0.96,
+            "ou15": 0.96,
+            "ou25": 0.96,
+            "ou35": 0.94,
+            "btts": 0.96,
+            "ht_ft": 0.92,
+            "dc": 0.97,
+            "ht": 0.92,
+            "ht_home": 0.92,
+            "ht_draw": 0.92,
+            "ht_away": 0.92,
+        }
+        self._load_calibrators()
+        
+    def _load_calibrators(self):
+        """Load trained calibrators for each market from disk."""
+        for market in SUPPORTED_MARKETS:
+            model_path = os.path.join(CALIBRATION_DIR, f"{market}_calibrator.pkl")
+            metrics_path = os.path.join(CALIBRATION_DIR, f"{market}_metrics.json")
+            
+            if os.path.exists(model_path):
+                try:
+                    with open(model_path, "rb") as f:
+                        self.calibrators[market] = pickle.load(f)
+                    print(f"[Calibrator] Loaded calibration model for {market}")
+                except Exception as e:
+                    print(f"[Calibrator] Warning: Failed to load {market}: {e}")
+                    
+            if os.path.exists(metrics_path):
+                try:
+                    with open(metrics_path, "r") as f:
+                        data = json.load(f)
+                        metrics = CalibrationMetrics()
+                        metrics.brier_score = data.get("brier_score", 0.0)
+                        metrics.calibration_error = data.get("calibration_error", 0.0)
+                        metrics.sample_count = data.get("sample_count", 0)
+                        metrics.last_trained = data.get("last_trained", "")
+                        metrics.mean_predicted = data.get("mean_predicted", 0.0)
+                        metrics.mean_actual = data.get("mean_actual", 0.0)
+                        self.metrics[market] = metrics
+                except Exception as e:
+                    print(f"[Calibrator] Warning: Failed to load metrics for {market}: {e}")
+
+    # Below this sample count, blend isotonic with raw_prob to dampen overfit jumps.
+    # Above this count, trust isotonic fully.
+    TRUSTED_SAMPLE_FLOOR = 30
+    TRUSTED_SAMPLE_CEILING = 200
+    # Hard cap on how far calibration can move probability in either direction.
+    MAX_DELTA = 0.20
+
+    def calibrate(self, market_type: str, raw_prob: float, odds_val: Optional[float] = None) -> float:
+        """
+        Calibrate a raw probability using Isotonic Regression with safeguards.
+
+        Args:
+            market_type (str): 'ms_home', 'ou25', 'btts', 'ht_ft', etc.
+            raw_prob (float): The raw probability from XGBoost (0.0 - 1.0)
+            odds_val (float, optional): The pre-match odds, used for context-aware bucket mapping
+
+        Returns:
+            float: Calibrated probability (0.0 - 1.0)
+
+        Safeguards:
+          * Low-sample trained models are blended with raw_prob to dampen overfit.
+          * MAX_DELTA caps the per-call adjustment (prevents 40pp swings).
+        """
+        # Normalize market type
+        market_key = market_type.lower().replace("-", "_")
+
+        # Route to bucket if ms_home and odds provided
+        if market_key == "ms_home" and odds_val is not None and odds_val > 1.0:
+            if odds_val <= 1.40:
+                bucket_key = "ms_home_heavy_fav"
+            elif odds_val <= 1.80:
+                bucket_key = "ms_home_fav"
+            elif odds_val <= 2.50:
+                bucket_key = "ms_home_balanced"
+            else:
+                bucket_key = "ms_home_underdog"
+
+            if bucket_key in self.calibrators:
+                market_key = bucket_key
+
+        # If we have a trained Isotonic Regression model, use it (with safeguards)
+        if market_key in self.calibrators:
+            try:
+                iso_pred = float(self.calibrators[market_key].predict([raw_prob])[0])
+
+                # Sample-count weighted blend with raw probability.
+                # Sparse models barely move probability; mature models dominate.
+                metrics = self.metrics.get(market_key)
+                n_samples = metrics.sample_count if metrics else 0
+                if n_samples >= self.TRUSTED_SAMPLE_CEILING:
+                    iso_weight = 1.0
+                elif n_samples <= self.TRUSTED_SAMPLE_FLOOR:
+                    # Very sparse: at least 30% trust to surface the signal
+                    iso_weight = max(0.30, n_samples / self.TRUSTED_SAMPLE_CEILING)
+                else:
+                    # Linearly ramp 30% → 100% between floor and ceiling
+                    span = self.TRUSTED_SAMPLE_CEILING - self.TRUSTED_SAMPLE_FLOOR
+                    iso_weight = 0.30 + 0.70 * (n_samples - self.TRUSTED_SAMPLE_FLOOR) / span
+                blended = iso_weight * iso_pred + (1.0 - iso_weight) * raw_prob
+
+                # Cap delta to avoid huge swings on noisy calibrators
+                delta = blended - raw_prob
+                if delta > self.MAX_DELTA:
+                    blended = raw_prob + self.MAX_DELTA
+                elif delta < -self.MAX_DELTA:
+                    blended = raw_prob - self.MAX_DELTA
+
+                return float(np.clip(blended, 0.01, 0.99))
+            except Exception as e:
+                print(f"[Calibrator] Warning: Isotonic failed for {market_key}: {e}")
+                # Fall through to heuristic
+
+        # Fallback to heuristic calibration
+        return self._heuristic_calibrate(market_key, raw_prob)
+    
+    def _heuristic_calibrate(self, market_type: str, raw_prob: float) -> float:
+        """
+        Heuristic calibration fallback when no trained model exists.
+        
+        This applies a conservative shrinkage towards the mean:
+        - Binary markets (OU, BTTS): shrink towards 0.5
+        - Multi-class (MS): shrink towards 0.33
+        - HT/FT: stronger shrinkage due to higher variance
+        """
+        # Get shrinkage factor for this market
+        shrinkage = self.heuristic_fallback.get(market_type, 0.90)
+        
+        if market_type in ["ms", "ms_home", "ms_home_heavy_fav", "ms_home_fav", "ms_home_balanced", "ms_home_underdog", "ms_draw", "ms_away"]:
+            # Pull towards 0.33 (uniform for 3-class)
+            return (raw_prob * shrinkage) + (0.33 * (1.0 - shrinkage))
+            
+        elif market_type in ["ou15", "ou25", "ou35", "btts"]:
+            # Pull towards 0.5 (uniform for binary)
+            return (raw_prob * shrinkage) + (0.5 * (1.0 - shrinkage))
+            
+        elif market_type in ["ht_ft", "ht"]:
+            # Stronger shrinkage for high-variance markets
+            return raw_prob * shrinkage
+            
+        elif market_type == "dc":
+            # Double chance is more reliable
+            return (raw_prob * shrinkage) + (0.66 * (1.0 - shrinkage))
+            
+        return raw_prob
+
+    def train_calibration(
+        self,
+        df: pd.DataFrame,
+        market: str,
+        prob_col: str,
+        actual_col: str,
+        min_samples: int = 100,
+        save: bool = True,
+    ) -> CalibrationMetrics:
+        """
+        Train an Isotonic Regression calibration model for a specific market.
+        
+        Args:
+            df: DataFrame with predictions and actual outcomes
+            market: Market identifier (e.g., 'ms_home', 'ou25', 'btts')
+            prob_col: Column name for raw probabilities
+            actual_col: Column name for actual outcomes (0 or 1)
+            min_samples: Minimum samples required to train
+            save: Whether to save the model to disk
+            
+        Returns:
+            CalibrationMetrics with quality metrics
+        """
+        # Filter valid data
+        valid_df = df[[prob_col, actual_col]].dropna()
+        n_samples = len(valid_df)
+        
+        if n_samples < min_samples:
+            print(f"[Calibrator] Warning: Only {n_samples} samples for {market}, "
+                  f"need at least {min_samples}")
+            metrics = CalibrationMetrics()
+            metrics.sample_count = n_samples
+            return metrics
+        
+        # Extract arrays
+        raw_probs = valid_df[prob_col].values
+        actuals = valid_df[actual_col].values
+        
+        # Train Isotonic Regression
+        iso = IsotonicRegression(out_of_bounds="clip", increasing=True)
+        iso.fit(raw_probs, actuals)
+        
+        # Calculate calibrated probabilities
+        calibrated_probs = iso.predict(raw_probs)
+        
+        # Calculate metrics
+        metrics = CalibrationMetrics()
+        metrics.sample_count = n_samples
+        metrics.last_trained = datetime.utcnow().isoformat()
+        metrics.brier_score = brier_score_loss(actuals, calibrated_probs)
+        metrics.mean_predicted = np.mean(raw_probs)
+        metrics.mean_actual = np.mean(actuals)
+        
+        # Calculate Expected Calibration Error (ECE)
+        metrics.calibration_error = self._calculate_ece(
+            calibrated_probs, actuals, n_bins=10
+        )
+        
+        # Store in memory
+        self.calibrators[market] = iso
+        self.metrics[market] = metrics
+        
+        # Save to disk
+        if save:
+            self._save_calibration(market, iso, metrics)
+        
+        print(f"[Calibrator] Trained {market}: "
+              f"Brier={metrics.brier_score:.4f}, "
+              f"ECE={metrics.calibration_error:.4f}, "
+              f"n={n_samples}")
+        
+        return metrics
+    
+    def train_all_markets(
+        self,
+        df: pd.DataFrame,
+        market_config: Dict[str, Tuple[str, str]],
+        min_samples: int = 100,
+    ) -> Dict[str, CalibrationMetrics]:
+        """
+        Train calibration models for multiple markets at once.
+        
+        Args:
+            df: DataFrame with all predictions and outcomes
+            market_config: Dict mapping market -> (prob_col, actual_col)
+                          e.g., {'ou25': ('ou25_over_prob', 'ou25_over_actual')}
+            min_samples: Minimum samples per market
+            
+        Returns:
+            Dict of market -> CalibrationMetrics
+        """
+        results = {}
+        
+        for market, (prob_col, actual_col) in market_config.items():
+            print(f"\n[Calibrator] Training {market}...")
+            try:
+                metrics = self.train_calibration(
+                    df=df,
+                    market=market,
+                    prob_col=prob_col,
+                    actual_col=actual_col,
+                    min_samples=min_samples,
+                    save=True,
+                )
+                results[market] = metrics
+            except Exception as e:
+                print(f"[Calibrator] Failed to train {market}: {e}")
+                
+        return results
+    
+    def _calculate_ece(
+        self, 
+        probs: np.ndarray, 
+        actuals: np.ndarray, 
+        n_bins: int = 10
+    ) -> float:
+        """
+        Calculate Expected Calibration Error (ECE).
+        
+        ECE = sum(|bin_accuracy - bin_confidence| * bin_weight)
+        
+        Lower is better. Perfect calibration = 0.
+        """
+        bin_boundaries = np.linspace(0, 1, n_bins + 1)
+        ece = 0.0
+        
+        for i in range(n_bins):
+            in_bin = (probs >= bin_boundaries[i]) & (probs < bin_boundaries[i + 1])
+            prop_in_bin = np.mean(in_bin)
+            
+            if prop_in_bin > 0:
+                accuracy_in_bin = np.mean(actuals[in_bin])
+                avg_confidence_in_bin = np.mean(probs[in_bin])
+                ece += np.abs(accuracy_in_bin - avg_confidence_in_bin) * prop_in_bin
+                
+        return ece
+    
+    def _save_calibration(
+        self, 
+        market: str, 
+        calibrator: IsotonicRegression,
+        metrics: CalibrationMetrics
+    ):
+        """Save calibration model and metrics to disk."""
+        # Save model
+        model_path = os.path.join(CALIBRATION_DIR, f"{market}_calibrator.pkl")
+        with open(model_path, "wb") as f:
+            pickle.dump(calibrator, f)
+            
+        # Save metrics
+        metrics_path = os.path.join(CALIBRATION_DIR, f"{market}_metrics.json")
+        with open(metrics_path, "w") as f:
+            json.dump(metrics.to_dict(), f, indent=2)
+            
+        print(f"[Calibrator] Saved {market} to {CALIBRATION_DIR}")
+    
+    def get_calibration_report(self) -> Dict[str, Any]:
+        """Generate a summary report of all calibration models."""
+        report = {
+            "trained_markets": list(self.calibrators.keys()),
+            "metrics": {},
+            "heuristic_only": [],
+        }
+        
+        for market in SUPPORTED_MARKETS:
+            if market in self.metrics:
+                report["metrics"][market] = self.metrics[market].to_dict()
+            elif market not in self.calibrators:
+                report["heuristic_only"].append(market)
+                
+        return report
+    
+    def get_calibrated_probabilities(
+        self, 
+        market: str, 
+        raw_probs: np.ndarray
+    ) -> np.ndarray:
+        """
+        Batch calibration for array of probabilities.
+        
+        Args:
+            market: Market type
+            raw_probs: Array of raw probabilities
+            
+        Returns:
+            Array of calibrated probabilities
+        """
+        return np.array([self.calibrate(market, p) for p in raw_probs])
+
+
+# Singleton instance
+_calibrator_instance: Optional[Calibrator] = None
+
+
+def get_calibrator() -> Calibrator:
+    """Get or create the global Calibrator instance."""
+    global _calibrator_instance
+    if _calibrator_instance is None:
+        _calibrator_instance = Calibrator()
+    return _calibrator_instance

ai-engine/models/league_model.py

@@ -0,0 +1,191 @@
+"""
+League-Specific Model Loader
+=============================
+Loads per-league XGBoost models + isotonic calibrators trained by
+scripts/train_league_models.py and provides a unified prediction interface.
+
+Falls back to general V25 for any market/league without a dedicated model.
+"""
+
+import os
+import json
+import pickle
+from functools import lru_cache
+from typing import Dict, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+
+AI_ENGINE_DIR    = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+LEAGUE_MODEL_DIR = os.path.join(AI_ENGINE_DIR, "models", "league_specific")
+
+# Market file name → (num_class, label_list)
+MARKET_META: Dict[str, Tuple[int, list]] = {
+    "ms":       (3, ["1", "X", "2"]),
+    "ou15":     (2, ["Over", "Under"]),
+    "ou25":     (2, ["Over", "Under"]),
+    "ou35":     (2, ["Over", "Under"]),
+    "btts":     (2, ["Yes", "No"]),
+    "ht":       (3, ["1", "X", "2"]),
+    "ht_ou05":  (2, ["Over", "Under"]),
+    "ht_ou15":  (2, ["Over", "Under"]),
+    "htft":     (9, ["1/1","1/X","1/2","X/1","X/X","X/2","2/1","2/X","2/2"]),
+    "oe":       (2, ["Odd", "Even"]),
+    "cards":    (2, ["Over", "Under"]),
+    "handicap": (3, ["1", "X", "2"]),
+}
+
+# Signal key map (file key → uppercase signal key used in _get_v25_signal)
+FILE_TO_SIGNAL = {
+    "ms": "MS", "ou15": "OU15", "ou25": "OU25", "ou35": "OU35",
+    "btts": "BTTS", "ht": "HT", "ht_ou05": "HT_OU05", "ht_ou15": "HT_OU15",
+    "htft": "HTFT", "oe": "OE", "cards": "CARDS", "handicap": "HCAP",
+}
+
+
+class LeagueModel:
+    """Holds XGBoost models + isotonic calibrators for one league."""
+
+    def __init__(self, league_id: str):
+        self.league_id = league_id
+        self.league_dir = os.path.join(LEAGUE_MODEL_DIR, league_id)
+        self.models: Dict[str, xgb.Booster] = {}       # market_key → booster
+        self.calibrators: Dict[str, object] = {}        # cal_key → isotonic
+        self.feature_cols: Optional[list] = None
+        self._loaded = False
+
+    def load(self) -> bool:
+        if not os.path.isdir(self.league_dir):
+            return False
+        try:
+            fc_path = os.path.join(self.league_dir, "feature_cols.json")
+            if os.path.exists(fc_path):
+                with open(fc_path) as f:
+                    self.feature_cols = json.load(f)
+
+            for mkey in MARKET_META:
+                xgb_path = os.path.join(self.league_dir, f"xgb_{mkey}.json")
+                if os.path.exists(xgb_path) and os.path.getsize(xgb_path) > 100:
+                    b = xgb.Booster()
+                    b.load_model(xgb_path)
+                    self.models[mkey] = b
+
+            for fname in os.listdir(self.league_dir):
+                if fname.startswith("cal_") and fname.endswith(".pkl"):
+                    cal_key = fname[4:-4]  # strip cal_ and .pkl
+                    with open(os.path.join(self.league_dir, fname), "rb") as f:
+                        self.calibrators[cal_key] = pickle.load(f)
+
+            self._loaded = bool(self.models or self.calibrators)
+            return self._loaded
+        except Exception as e:
+            print(f"[LeagueModel] Load failed for {self.league_id}: {e}")
+            return False
+
+    def has_market(self, mkey: str) -> bool:
+        return mkey in self.models
+
+    def predict_market(
+        self,
+        mkey: str,
+        feature_row: Dict[str, float],
+    ) -> Optional[Dict[str, float]]:
+        """
+        Predict one market using league-specific XGBoost + isotonic calibration.
+        Returns {label: prob} dict or None if no model available.
+        """
+        if mkey not in self.models:
+            return None
+
+        num_class, labels = MARKET_META[mkey]
+        fc = self.feature_cols
+        if fc is None:
+            # Fallback to whatever the booster expects (it knows its feature names)
+            fc = list(self.models[mkey].feature_names or [])
+
+        try:
+            X = pd.DataFrame([{col: feature_row.get(col, 0.0) for col in fc}])
+            dmat = xgb.DMatrix(X)
+            raw = self.models[mkey].predict(dmat)
+
+            if num_class > 2:
+                probs_arr = raw.reshape(-1, num_class)[0]
+                probs = {labels[i]: float(probs_arr[i]) for i in range(num_class)}
+                # Apply isotonic calibration per class
+                cal_total = 0.0
+                for i, label in enumerate(labels):
+                    cal_key = f"{mkey}_{i}"
+                    if cal_key in self.calibrators:
+                        p_cal = float(self.calibrators[cal_key].predict([probs_arr[i]])[0])
+                        probs[label] = max(0.01, min(0.99, p_cal))
+                    cal_total += probs[label]
+                if cal_total > 0:
+                    probs = {k: v / cal_total for k, v in probs.items()}
+            else:
+                p = float(raw[0])
+                cal_key = mkey
+                if cal_key in self.calibrators:
+                    p = float(self.calibrators[cal_key].predict([p])[0])
+                    p = max(0.01, min(0.99, p))
+                probs = {labels[0]: p, labels[1]: 1.0 - p}
+
+            return probs
+        except Exception as e:
+            print(f"[LeagueModel] predict_market({mkey}) failed for {self.league_id}: {e}")
+            return None
+
+
+class LeagueModelLoader:
+    """
+    In-memory cache for league-specific models.
+    Thread-safe for single-process async servers (FastAPI/uvicorn).
+    """
+
+    def __init__(self, max_cached: int = 80):
+        self._cache: Dict[str, Optional[LeagueModel]] = {}
+        self._max_cached = max_cached
+
+    def get(self, league_id: str) -> Optional[LeagueModel]:
+        """Return loaded LeagueModel for this league, or None if unavailable."""
+        if league_id in self._cache:
+            return self._cache[league_id]
+
+        # Evict oldest entry if cache is full
+        if len(self._cache) >= self._max_cached:
+            oldest = next(iter(self._cache))
+            del self._cache[oldest]
+
+        model = LeagueModel(league_id)
+        loaded = model.load()
+        self._cache[league_id] = model if loaded else None
+        if loaded:
+            n_models = len(model.models)
+            n_cals   = len(model.calibrators)
+            print(f"[LeagueModel] Loaded {league_id}: {n_models} XGB models, {n_cals} calibrators")
+        return self._cache[league_id]
+
+    def available_leagues(self) -> list:
+        if not os.path.isdir(LEAGUE_MODEL_DIR):
+            return []
+        return [d for d in os.listdir(LEAGUE_MODEL_DIR)
+                if os.path.isdir(os.path.join(LEAGUE_MODEL_DIR, d))]
+
+    def readiness_summary(self) -> dict:
+        leagues = self.available_leagues()
+        return {
+            "league_specific_dir": LEAGUE_MODEL_DIR,
+            "available_leagues": len(leagues),
+            "cached": len([v for v in self._cache.values() if v is not None]),
+        }
+
+
+# ── Singleton ──────────────────────────────────────────────────────
+_loader: Optional[LeagueModelLoader] = None
+
+
+def get_league_model_loader() -> LeagueModelLoader:
+    global _loader
+    if _loader is None:
+        _loader = LeagueModelLoader()
+    return _loader

ai-engine/models/v25/feature_cols.json

@@ -0,0 +1,154 @@
+[
+  "home_overall_elo",
+  "away_overall_elo",
+  "elo_diff",
+  "home_home_elo",
+  "away_away_elo",
+  "home_form_elo",
+  "away_form_elo",
+  "form_elo_diff",
+  "home_goals_avg",
+  "home_conceded_avg",
+  "away_goals_avg",
+  "away_conceded_avg",
+  "home_clean_sheet_rate",
+  "away_clean_sheet_rate",
+  "home_scoring_rate",
+  "away_scoring_rate",
+  "home_winning_streak",
+  "away_winning_streak",
+  "home_unbeaten_streak",
+  "away_unbeaten_streak",
+  "h2h_total_matches",
+  "h2h_home_win_rate",
+  "h2h_draw_rate",
+  "h2h_avg_goals",
+  "h2h_btts_rate",
+  "h2h_over25_rate",
+  "home_avg_possession",
+  "away_avg_possession",
+  "home_avg_shots_on_target",
+  "away_avg_shots_on_target",
+  "home_shot_conversion",
+  "away_shot_conversion",
+  "home_avg_corners",
+  "away_avg_corners",
+  "odds_ms_h",
+  "odds_ms_d",
+  "odds_ms_a",
+  "implied_home",
+  "implied_draw",
+  "implied_away",
+  "odds_ht_ms_h",
+  "odds_ht_ms_d",
+  "odds_ht_ms_a",
+  "odds_ou05_o",
+  "odds_ou05_u",
+  "odds_ou15_o",
+  "odds_ou15_u",
+  "odds_ou25_o",
+  "odds_ou25_u",
+  "odds_ou35_o",
+  "odds_ou35_u",
+  "odds_ht_ou05_o",
+  "odds_ht_ou05_u",
+  "odds_ht_ou15_o",
+  "odds_ht_ou15_u",
+  "odds_btts_y",
+  "odds_btts_n",
+  "odds_ms_h_present",
+  "odds_ms_d_present",
+  "odds_ms_a_present",
+  "odds_ht_ms_h_present",
+  "odds_ht_ms_d_present",
+  "odds_ht_ms_a_present",
+  "odds_ou05_o_present",
+  "odds_ou05_u_present",
+  "odds_ou15_o_present",
+  "odds_ou15_u_present",
+  "odds_ou25_o_present",
+  "odds_ou25_u_present",
+  "odds_ou35_o_present",
+  "odds_ou35_u_present",
+  "odds_ht_ou05_o_present",
+  "odds_ht_ou05_u_present",
+  "odds_ht_ou15_o_present",
+  "odds_ht_ou15_u_present",
+  "odds_btts_y_present",
+  "odds_btts_n_present",
+  "home_xga",
+  "away_xga",
+  "league_avg_goals",
+  "league_zero_goal_rate",
+  "upset_atmosphere",
+  "upset_motivation",
+  "upset_fatigue",
+  "upset_potential",
+  "referee_home_bias",
+  "referee_avg_goals",
+  "referee_cards_total",
+  "referee_avg_yellow",
+  "referee_experience",
+  "home_momentum_score",
+  "away_momentum_score",
+  "momentum_diff",
+  "home_squad_quality",
+  "away_squad_quality",
+  "squad_diff",
+  "home_key_players",
+  "away_key_players",
+  "home_missing_impact",
+  "away_missing_impact",
+  "home_goals_form",
+  "away_goals_form",
+  "home_lineup_goals_per90",
+  "away_lineup_goals_per90",
+  "home_lineup_assists_per90",
+  "away_lineup_assists_per90",
+  "home_squad_continuity",
+  "away_squad_continuity",
+  "home_top_scorer_form",
+  "away_top_scorer_form",
+  "home_avg_player_exp",
+  "away_avg_player_exp",
+  "home_goals_diversity",
+  "away_goals_diversity",
+  "h2h_home_goals_avg",
+  "h2h_away_goals_avg",
+  "h2h_recent_trend",
+  "h2h_venue_advantage",
+  "home_rolling5_goals",
+  "home_rolling5_conceded",
+  "home_rolling10_goals",
+  "home_rolling10_conceded",
+  "home_rolling20_goals",
+  "home_rolling20_conceded",
+  "away_rolling5_goals",
+  "away_rolling5_conceded",
+  "away_rolling10_goals",
+  "away_rolling10_conceded",
+  "home_rolling5_cs",
+  "away_rolling5_cs",
+  "home_venue_goals",
+  "home_venue_conceded",
+  "away_venue_goals",
+  "away_venue_conceded",
+  "home_goal_trend",
+  "away_goal_trend",
+  "home_days_rest",
+  "away_days_rest",
+  "match_month",
+  "is_season_start",
+  "is_season_end",
+  "attack_vs_defense_home",
+  "attack_vs_defense_away",
+  "xg_diff",
+  "form_momentum_interaction",
+  "elo_form_consistency",
+  "upset_x_elo_gap",
+  "league_home_win_rate",
+  "league_draw_rate",
+  "league_btts_rate",
+  "league_ou25_rate",
+  "league_reliability_score"
+]

ai-engine/models/v25/lgb_v25_ht_ou05.txt

@@ -0,0 +1,891 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=151
+objective=binary sigmoid:1
+feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151
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+
+
+Tree=31
+num_leaves=8
+num_cat=0
+split_feature=86 86 86 1 127 31 122
+split_gain=10.4188 10.4465 11.9432 10.3703 15.906 10.0392 7.58651
+threshold=2.6939799331103687 1.0000000180025095e-35 1.8603896103896107 1467.9553804385575 2.1340000000000003 0.22980739360049704 2.3515000000000006
+decision_type=2 2 2 2 2 2 2
+left_child=1 6 -3 5 -5 -2 -1
+right_child=3 2 -4 4 -6 -7 -8
+leaf_value=0.0014256124570572787 0.0018941270616234518 -0.058645654322910731 -0.0086447877444392647 0.02733676763309234 -0.12590863729503893 -0.082423971345859881 0.042831761918242235
+leaf_weight=1608.7813730537891 166.28092505782843 58.273459360003471 473.13907171785831 372.2649027556181 7.4961845725774756 16.887378051877022 49.535993173718452
+leaf_count=7823 854 251 2181 1963 43 94 277
+internal_value=0.00183871 -0.00141235 -0.0141287 0.0144848 0.0243088 -0.0058835 0.0026627
+internal_weight=2752.66 2189.73 531.413 562.929 379.761 183.168 1658.32
+internal_count=13486 10532 2432 2954 2006 948 8100
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=32
+num_leaves=8
+num_cat=0
+split_feature=93 144 112 94 104 93 120
+split_gain=10.1721 16.1941 11.2364 13.1765 9.78364 8.96284 8.48134
+threshold=20.550000000000004 0.013450000000000002 0.22750000000000004 5.5500000000000007 1.2865000000000002 25.050000000000004 2.0500000000000003
+decision_type=2 2 2 2 2 2 2
+left_child=2 4 6 -4 -2 -3 -1
+right_child=1 5 3 -5 -6 -7 -8
+leaf_value=0.0034609471658556285 0.020050548549636428 -0.009082334773132807 -0.11004659616173273 -0.007594432301873808 -0.021486441758403186 -0.13291664093499478 0.038221762386595907
+leaf_weight=820.33589915931225 594.67776323109865 13.120999380946161 13.791079476475714 1142.1561977639794 68.70460732281208 12.293409973382948 84.046657353639603
+leaf_count=3903 3054 71 65 5495 382 70 446
+internal_value=0.0016558 0.0126202 -0.00200979 -0.00881763 0.0157484 -0.0690103 0.00669173
+internal_weight=2749.13 688.797 2060.33 1155.95 663.382 25.4144 904.383
+internal_count=13486 3577 9909 5560 3436 141 4349
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=33
+num_leaves=5
+num_cat=0
+split_feature=91 141 123 7
+split_gain=9.64033 9.54985 10.2025 8.96435
+threshold=0.46333333333333332 -0.77399999999999991 2.2565000000000004 33.104396552584838
+decision_type=2 2 2 2
+left_child=1 3 -3 -1
+right_child=-2 2 -4 -5
+leaf_value=-0.015732035707484552 -0.12133869515037762 0.0038526132779362976 0.057630556320633561 0.031446003016811871
+leaf_weight=450.80555958300829 6.9056807309389105 2188.2216669544578 38.992827542126179 48.440718069672585
+leaf_count=2162 31 10769 213 250
+internal_value=0.00156231 0.00187402 0.00479437 -0.011154
+internal_weight=2733.37 2726.46 2227.21 499.246
+internal_count=13425 13394 10982 2412
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=34
+num_leaves=5
+num_cat=0
+split_feature=45 98 80 123
+split_gain=8.19305 8.37131 12.6079 10.3403
+threshold=1.6150000000000004 0.58114035087719318 0.043836402184014855 2.1715000000000004
+decision_type=2 2 2 2
+left_child=1 3 -3 -1
+right_child=-2 2 -4 -5
+leaf_value=0.008356015193296689 -0.14816646007318571 -0.033602776052007143 0.00019618273212945178 0.070940775625300892
+leaf_weight=749.49989224970341 3.9628616422414771 128.32899699360132 1819.463518589735 29.807201541960239
+leaf_count=3713 16 704 8830 162
+internal_value=0.00140384 0.0016217 -0.00203079 0.0107506
+internal_weight=2731.06 2727.1 1947.79 779.307
+internal_count=13425 13409 9534 3875
+is_linear=0
+shrinkage=0.104222
+
+
+end of trees
+
+feature_importances:
+Column_86=18
+Column_49=9
+Column_53=8
+Column_41=6
+Column_54=6
+Column_120=6
+Column_141=6
+Column_45=5
+Column_91=5
+Column_104=5
+Column_1=4
+Column_94=4
+Column_96=4
+Column_132=4
+Column_136=4
+Column_148=4
+Column_3=3
+Column_4=3
+Column_6=3
+Column_11=3
+Column_30=3
+Column_35=3
+Column_38=3
+Column_48=3
+Column_50=3
+Column_85=3
+Column_92=3
+Column_105=3
+Column_115=3
+Column_123=3
+Column_127=3
+Column_130=3
+Column_133=3
+Column_149=3
+Column_150=3
+Column_0=2
+Column_7=2
+Column_10=2
+Column_16=2
+Column_29=2
+Column_40=2
+Column_44=2
+Column_51=2
+Column_52=2
+Column_55=2
+Column_56=2
+Column_89=2
+Column_93=2
+Column_98=2
+Column_100=2
+Column_102=2
+Column_106=2
+Column_110=2
+Column_111=2
+Column_122=2
+Column_134=2
+Column_135=2
+Column_137=2
+Column_143=2
+Column_144=2
+Column_147=2
+Column_8=1
+Column_12=1
+Column_14=1
+Column_17=1
+Column_21=1
+Column_27=1
+Column_28=1
+Column_31=1
+Column_34=1
+Column_37=1
+Column_39=1
+Column_46=1
+Column_47=1
+Column_78=1
+Column_80=1
+Column_84=1
+Column_95=1
+Column_99=1
+Column_107=1
+Column_112=1
+Column_119=1
+Column_121=1
+Column_125=1
+Column_126=1
+Column_145=1
+
+parameters:
+[boosting: gbdt]
+[objective: binary]
+[metric: binary_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1500]
+[learning_rate: 0.104222]
+[num_leaves: 8]
+[num_threads: 4]
+[seed: 42]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 3]
+[min_data_in_leaf: 17]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.709098]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 3]
+[bagging_seed: 400]
+[bagging_by_query: 0]
+[feature_fraction: 0.58888]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 30056]
+[extra_trees: 0]
+[extra_seed: 12879]
+[early_stopping_round: 0]
+[early_stopping_min_delta: 0]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 7.81041e-07]
+[lambda_l2: 0.00942891]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 17869]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: -1]
+[saved_feature_importance_type: 0]
+[use_quantized_grad: 0]
+[num_grad_quant_bins: 4]
+[quant_train_renew_leaf: 0]
+[stochastic_rounding: 1]
+[linear_tree: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 175]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[parser_config_file: ]
+[objective_seed: 16083]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[lambdarank_position_bias_regularization: 0]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:null

ai-engine/models/v25_ensemble.py

@@ -0,0 +1,735 @@
+"""
+V25 Ensemble Predictor - NO TARGET LEAKAGE
+===========================================
+Multi-model ensemble for match prediction using XGBoost and LightGBM.
+
+Features:
+- 73 engineered features (NO target leakage)
+- Market-specific models (MS, OU25, BTTS)
+- Weighted ensemble predictions
+- Value bet detection
+"""
+
+import os
+import json
+import numpy as np
+import pandas as pd
+from typing import Dict, List, Optional, Any
+from dataclasses import dataclass, field
+
+import xgboost as xgb
+import lightgbm as lgb
+
+import sys
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+try:
+    from config.config_loader import get_config as _get_cfg
+except ImportError:
+    _get_cfg = None  # type: ignore[assignment]
+
+# CatBoost is optional
+try:
+    from catboost import CatBoostClassifier
+    CATBOOST_AVAILABLE = True
+except ImportError:
+    CatBoostClassifier = None
+    CATBOOST_AVAILABLE = False
+
+# Paths
+MODELS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'v25')
+
+
+@dataclass
+class MarketPrediction:
+    """Prediction for a single betting market."""
+    market_type: str
+    pick: str
+    probability: float
+    confidence: float
+    odds: float = 0.0
+    is_value_bet: bool = False
+    edge: float = 0.0
+
+    def to_dict(self) -> dict:
+        return {
+            'market_type': self.market_type,
+            'pick': self.pick,
+            'probability': round(self.probability * 100, 1),
+            'confidence': round(self.confidence, 1),
+            'odds': self.odds,
+            'is_value_bet': self.is_value_bet,
+            'edge': round(self.edge * 100, 1),
+        }
+
+
+@dataclass
+class ValueBet:
+    """Detected value bet opportunity."""
+    market_type: str
+    pick: str
+    probability: float
+    odds: float
+    edge: float
+    confidence: float
+
+    def to_dict(self) -> dict:
+        return {
+            'market_type': self.market_type,
+            'pick': self.pick,
+            'probability': round(self.probability * 100, 1),
+            'odds': self.odds,
+            'edge': round(self.edge * 100, 1),
+            'confidence': round(self.confidence, 1),
+        }
+
+
+@dataclass
+class MatchPrediction:
+    """Complete match prediction with all markets."""
+    match_id: str
+    home_team: str
+    away_team: str
+    
+    # MS predictions
+    home_prob: float = 0.0
+    draw_prob: float = 0.0
+    away_prob: float = 0.0
+    ms_pick: str = ''
+    ms_confidence: float = 0.0
+    
+    # OU25 predictions
+    over_prob: float = 0.0
+    under_prob: float = 0.0
+    ou25_pick: str = ''
+    ou25_confidence: float = 0.0
+    
+    # BTTS predictions
+    btts_yes_prob: float = 0.0
+    btts_no_prob: float = 0.0
+    btts_pick: str = ''
+    btts_confidence: float = 0.0
+    
+    # Value bets
+    value_bets: List[ValueBet] = field(default_factory=list)
+    
+    def to_dict(self) -> dict:
+        return {
+            'match_id': self.match_id,
+            'home_team': self.home_team,
+            'away_team': self.away_team,
+            'ms': {
+                'home_prob': round(self.home_prob * 100, 1),
+                'draw_prob': round(self.draw_prob * 100, 1),
+                'away_prob': round(self.away_prob * 100, 1),
+                'pick': self.ms_pick,
+                'confidence': round(self.ms_confidence, 1),
+            },
+            'ou25': {
+                'over_prob': round(self.over_prob * 100, 1),
+                'under_prob': round(self.under_prob * 100, 1),
+                'pick': self.ou25_pick,
+                'confidence': round(self.ou25_confidence, 1),
+            },
+            'btts': {
+                'yes_prob': round(self.btts_yes_prob * 100, 1),
+                'no_prob': round(self.btts_no_prob * 100, 1),
+                'pick': self.btts_pick,
+                'confidence': round(self.btts_confidence, 1),
+            },
+            'value_bets': [vb.to_dict() for vb in self.value_bets],
+        }
+
+
+class V25Predictor:
+    """
+    V25 Ensemble Predictor - NO TARGET LEAKAGE
+    
+    Uses market-specific XGBoost and LightGBM models.
+    Each market (MS, OU25, BTTS) has its own trained models.
+    """
+    
+    # Feature columns — loaded dynamically from feature_cols.json to stay
+    # in sync with the trained models.  The hardcoded list below is only a
+    # fallback in case the JSON file is missing.
+    _FALLBACK_FEATURE_COLS = [
+        # ELO Features (8)
+        'home_overall_elo', 'away_overall_elo', 'elo_diff',
+        'home_home_elo', 'away_away_elo',
+        'home_form_elo', 'away_form_elo', 'form_elo_diff',
+        
+        # Form Features (12)
+        'home_goals_avg', 'home_conceded_avg',
+        'away_goals_avg', 'away_conceded_avg',
+        'home_clean_sheet_rate', 'away_clean_sheet_rate',
+        'home_scoring_rate', 'away_scoring_rate',
+        'home_winning_streak', 'away_winning_streak',
+        'home_unbeaten_streak', 'away_unbeaten_streak',
+        
+        # H2H Features (6)
+        'h2h_total_matches', 'h2h_home_win_rate', 'h2h_draw_rate',
+        'h2h_avg_goals', 'h2h_btts_rate', 'h2h_over25_rate',
+        
+        # Team Stats Features (8)
+        'home_avg_possession', 'away_avg_possession',
+        'home_avg_shots_on_target', 'away_avg_shots_on_target',
+        'home_shot_conversion', 'away_shot_conversion',
+        'home_avg_corners', 'away_avg_corners',
+        
+        # Odds Features (24)
+        'odds_ms_h', 'odds_ms_d', 'odds_ms_a',
+        'implied_home', 'implied_draw', 'implied_away',
+        'odds_ht_ms_h', 'odds_ht_ms_d', 'odds_ht_ms_a',
+        'odds_ou05_o', 'odds_ou05_u',
+        'odds_ou15_o', 'odds_ou15_u',
+        'odds_ou25_o', 'odds_ou25_u',
+        'odds_ou35_o', 'odds_ou35_u',
+        'odds_ht_ou05_o', 'odds_ht_ou05_u',
+        'odds_ht_ou15_o', 'odds_ht_ou15_u',
+        'odds_btts_y', 'odds_btts_n',
+        
+        # Odds Presence Flags (20)
+        'odds_ms_h_present', 'odds_ms_d_present', 'odds_ms_a_present',
+        'odds_ht_ms_h_present', 'odds_ht_ms_d_present', 'odds_ht_ms_a_present',
+        'odds_ou05_o_present', 'odds_ou05_u_present',
+        'odds_ou15_o_present', 'odds_ou15_u_present',
+        'odds_ou25_o_present', 'odds_ou25_u_present',
+        'odds_ou35_o_present', 'odds_ou35_u_present',
+        'odds_ht_ou05_o_present', 'odds_ht_ou05_u_present',
+        'odds_ht_ou15_o_present', 'odds_ht_ou15_u_present',
+        'odds_btts_y_present', 'odds_btts_n_present',
+        
+        # League Features (4)
+        'home_xga', 'away_xga',
+        'league_avg_goals', 'league_zero_goal_rate',
+        
+        # Upset Engine (4)
+        'upset_atmosphere', 'upset_motivation', 'upset_fatigue', 'upset_potential',
+        
+        # Referee Engine (5)
+        'referee_home_bias', 'referee_avg_goals', 'referee_cards_total',
+        'referee_avg_yellow', 'referee_experience',
+        
+        # Momentum Engine (3)
+        'home_momentum_score', 'away_momentum_score', 'momentum_diff',
+
+        # Squad Features (9)
+        'home_squad_quality', 'away_squad_quality', 'squad_diff',
+        'home_key_players', 'away_key_players',
+        'home_missing_impact', 'away_missing_impact',
+        'home_goals_form', 'away_goals_form',
+    ]
+
+    @staticmethod
+    def _load_feature_cols() -> list:
+        """Load feature columns from feature_cols.json, falling back to hardcoded list."""
+        feature_json = os.path.join(MODELS_DIR, 'feature_cols.json')
+        try:
+            if os.path.exists(feature_json):
+                with open(feature_json, 'r', encoding='utf-8') as f:
+                    cols = json.load(f)
+                if isinstance(cols, list) and len(cols) > 0:
+                    print(f"[V25] Loaded {len(cols)} feature columns from feature_cols.json")
+                    return cols
+        except Exception as e:
+            print(f"[V25] Warning: could not load feature_cols.json: {e}")
+        print(f"[V25] Using fallback feature columns ({len(V25Predictor._FALLBACK_FEATURE_COLS)} features)")
+        return V25Predictor._FALLBACK_FEATURE_COLS
+
+    # Model weights for ensemble (overridden from config in __init__)
+    DEFAULT_WEIGHTS = {
+        'xgb': 0.50,
+        'lgb': 0.50,
+    }
+
+    def __init__(self, models_dir: Optional[str] = None):
+        """
+        Initialize V25 Predictor.
+        
+        Args:
+            models_dir: Directory containing model files. Defaults to v25/ directory.
+        """
+        self.models_dir = models_dir or MODELS_DIR
+        self.models = {}  # market -> {'xgb': model, 'lgb': model}
+        self._loaded = False
+        self.FEATURE_COLS = self._load_feature_cols()
+        # Load weights from config (falls back to class default 0.50/0.50)
+        if _get_cfg is not None:
+            try:
+                cfg = _get_cfg()
+                self.DEFAULT_WEIGHTS = {
+                    'xgb': float(cfg.get('model_ensemble.xgb_weight', 0.50)),
+                    'lgb': float(cfg.get('model_ensemble.lgb_weight', 0.50)),
+                }
+            except Exception:
+                pass  # keep class-level defaults
+    
+    # All trained market models available in V25
+    ALL_MARKETS = [
+        'ms', 'ou25', 'btts',           # Core markets
+        'ou15', 'ou35',                  # Additional OU lines
+        'ht_result', 'ht_ou05', 'ht_ou15',  # HT markets
+        'htft',                          # HT/FT combo
+        'cards_ou45',                    # Cards market
+        'handicap_ms',                   # Handicap
+        'odd_even',                      # Odd/Even goals
+    ]
+    
+    # Multi-class markets (output > 2 classes)
+    MULTICLASS_MARKETS = {'ms', 'ht_result', 'htft', 'handicap_ms'}
+    
+    def load_models(self) -> bool:
+        """Load all market-specific models from disk."""
+        try:
+            loaded_count = 0
+            
+            for market in self.ALL_MARKETS:
+                self.models[market] = {}
+                
+                # Load XGBoost (read content in Python to avoid non-ASCII path issues)
+                xgb_path = os.path.join(self.models_dir, f'xgb_v25_{market}.json')
+                if os.path.exists(xgb_path) and os.path.getsize(xgb_path) > 0:
+                    with open(xgb_path, 'r', encoding='utf-8') as f:
+                        xgb_content = f.read()
+                    booster = xgb.Booster()
+                    booster.load_model(bytearray(xgb_content, 'utf-8'))
+                    # Corruption detection: verify model can run a dummy prediction
+                    try:
+                        _dummy = pd.DataFrame([{col: 0.0 for col in self.FEATURE_COLS}])
+                        booster.predict(xgb.DMatrix(_dummy))
+                        self.models[market]['xgb'] = booster
+                        loaded_count += 1
+                    except Exception as _ce:
+                        print(f"[V25] ⚠️ XGB model for {market} failed integrity check: {_ce} — skipping")
+
+                # Load LightGBM (read content in Python to avoid non-ASCII path issues)
+                lgb_path = os.path.join(self.models_dir, f'lgb_v25_{market}.txt')
+                if os.path.exists(lgb_path) and os.path.getsize(lgb_path) > 0:
+                    with open(lgb_path, 'r', encoding='utf-8') as f:
+                        model_str = f.read()
+                    lgb_model = lgb.Booster(model_str=model_str)
+                    # Corruption detection: verify model can run a dummy prediction
+                    try:
+                        _dummy = pd.DataFrame([{col: 0.0 for col in self.FEATURE_COLS}])
+                        lgb_model.predict(_dummy)
+                        self.models[market]['lgb'] = lgb_model
+                        loaded_count += 1
+                    except Exception as _ce:
+                        print(f"[V25] ⚠️ LGB model for {market} failed integrity check: {_ce} — skipping")
+
+                # Remove empty entries
+                if not self.models[market]:
+                    del self.models[market]
+
+            print(f"[V25] Loaded {loaded_count} model files across {len(self.models)} markets: {list(self.models.keys())}")
+            self._loaded = loaded_count > 0
+            return self._loaded
+            
+        except Exception as e:
+            print(f"[ERROR] Error loading models: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+    
+    def _ensure_loaded(self):
+        """Ensure models are loaded before prediction."""
+        if not self._loaded:
+            if not self.load_models():
+                raise RuntimeError("Failed to load V25 models")
+
+    def readiness_summary(self) -> Dict[str, Any]:
+        """Return per-market model status for health check endpoint."""
+        if not self._loaded:
+            self.load_models()
+        market_status = {}
+        for market in self.ALL_MARKETS:
+            m = self.models.get(market, {})
+            market_status[market] = {
+                "xgb": "xgb" in m,
+                "lgb": "lgb" in m,
+                "ready": bool(m),
+            }
+        loaded_markets = [k for k, v in market_status.items() if v["ready"]]
+        return {
+            "fully_loaded": len(loaded_markets) == len(self.ALL_MARKETS),
+            "loaded_markets": loaded_markets,
+            "missing_markets": [m for m in self.ALL_MARKETS if m not in loaded_markets],
+            "weights": self.DEFAULT_WEIGHTS,
+        }
+
+    def _prepare_features(self, features: Dict[str, float]) -> pd.DataFrame:
+        """Prepare feature vector for prediction."""
+        X = pd.DataFrame([{col: features.get(col, 0.0) for col in self.FEATURE_COLS}])
+        return X
+    
+    def predict_ms(self, features: Dict[str, float]) -> tuple:
+        """
+        Predict match result (1X2).
+        
+        Returns:
+            (home_prob, draw_prob, away_prob)
+        """
+        self._ensure_loaded()
+        
+        X = self._prepare_features(features)
+        probs = []
+        
+        # XGBoost
+        if 'xgb' in self.models.get('ms', {}):
+            dmat = xgb.DMatrix(X)
+            xgb_proba = self.models['ms']['xgb'].predict(dmat)
+            if len(xgb_proba.shape) == 1:
+                xgb_proba = np.array([xgb_proba])
+            probs.append(xgb_proba[0] * self.DEFAULT_WEIGHTS['xgb'])
+        
+        # LightGBM
+        if 'lgb' in self.models.get('ms', {}):
+            lgb_proba = self.models['ms']['lgb'].predict(X)
+            if len(lgb_proba.shape) == 2:
+                probs.append(lgb_proba[0] * self.DEFAULT_WEIGHTS['lgb'])
+        
+        if not probs:
+            return 0.33, 0.33, 0.33
+        
+        ensemble_proba = np.sum(probs, axis=0)
+        ensemble_proba = ensemble_proba / ensemble_proba.sum()
+        
+        return float(ensemble_proba[0]), float(ensemble_proba[1]), float(ensemble_proba[2])
+    
+    def predict_ou25(self, features: Dict[str, float]) -> tuple:
+        """
+        Predict Over/Under 2.5 goals.
+        
+        Returns:
+            (over_prob, under_prob)
+        """
+        self._ensure_loaded()
+        
+        X = self._prepare_features(features)
+        probs = []
+        
+        # XGBoost
+        if 'xgb' in self.models.get('ou25', {}):
+            dmat = xgb.DMatrix(X)
+            xgb_proba = self.models['ou25']['xgb'].predict(dmat)
+            if isinstance(xgb_proba, np.ndarray) and len(xgb_proba.shape) == 1:
+                probs.append(xgb_proba[0])
+        
+        # LightGBM
+        if 'lgb' in self.models.get('ou25', {}):
+            lgb_proba = self.models['ou25']['lgb'].predict(X)
+            if isinstance(lgb_proba, np.ndarray):
+                probs.append(lgb_proba[0])
+        
+        if not probs:
+            return 0.5, 0.5
+        
+        # Average probability
+        avg_prob = np.mean(probs)
+        
+        return float(avg_prob), float(1 - avg_prob)
+    
+    def predict_btts(self, features: Dict[str, float]) -> tuple:
+        """
+        Predict Both Teams To Score.
+        
+        Returns:
+            (yes_prob, no_prob)
+        """
+        self._ensure_loaded()
+        
+        X = self._prepare_features(features)
+        probs = []
+        
+        # XGBoost
+        if 'xgb' in self.models.get('btts', {}):
+            dmat = xgb.DMatrix(X)
+            xgb_proba = self.models['btts']['xgb'].predict(dmat)
+            if isinstance(xgb_proba, np.ndarray) and len(xgb_proba.shape) == 1:
+                probs.append(xgb_proba[0])
+        
+        # LightGBM
+        if 'lgb' in self.models.get('btts', {}):
+            lgb_proba = self.models['btts']['lgb'].predict(X)
+            if isinstance(lgb_proba, np.ndarray):
+                probs.append(lgb_proba[0])
+        
+        if not probs:
+            return 0.5, 0.5
+        
+        # Average probability
+        avg_prob = np.mean(probs)
+        
+        return float(avg_prob), float(1 - avg_prob)
+    
+    def predict_market(self, market: str, features: Dict[str, float]) -> Optional[np.ndarray]:
+        """
+        Generic prediction for any loaded market.
+        
+        Args:
+            market: Market key (e.g. 'ht_result', 'htft', 'cards_ou45')
+            features: Feature dictionary.
+            
+        Returns:
+            numpy array of probabilities.
+            For binary markets: [positive_prob]
+            For multi-class markets: [class0_prob, class1_prob, ...]
+        """
+        self._ensure_loaded()
+        
+        if market not in self.models:
+            return None
+        
+        X = self._prepare_features(features)
+        probs = []
+        weights = []
+        is_multiclass = market in self.MULTICLASS_MARKETS
+        
+        # XGBoost
+        if 'xgb' in self.models[market]:
+            dmat = xgb.DMatrix(X)
+            xgb_proba = self.models[market]['xgb'].predict(dmat)
+            if isinstance(xgb_proba, np.ndarray):
+                if is_multiclass and len(xgb_proba.shape) == 2:
+                    probs.append(xgb_proba[0])
+                elif is_multiclass and len(xgb_proba.shape) == 1:
+                    probs.append(xgb_proba)
+                else:
+                    probs.append(np.array([xgb_proba[0]]))
+            weights.append(self.DEFAULT_WEIGHTS['xgb'])
+        
+        # LightGBM
+        if 'lgb' in self.models[market]:
+            lgb_proba = self.models[market]['lgb'].predict(X)
+            if isinstance(lgb_proba, np.ndarray):
+                if is_multiclass and len(lgb_proba.shape) == 2:
+                    probs.append(lgb_proba[0])
+                elif is_multiclass and len(lgb_proba.shape) == 1:
+                    probs.append(lgb_proba)
+                else:
+                    probs.append(np.array([lgb_proba[0]]))
+            weights.append(self.DEFAULT_WEIGHTS['lgb'])
+        
+        if not probs:
+            return None
+        
+        # Weighted average
+        if len(probs) == 1:
+            return probs[0]
+        
+        total_w = sum(weights[:len(probs)])
+        result = np.zeros_like(probs[0])
+        for p, w in zip(probs, weights):
+            result += p * (w / total_w)
+        
+        # Normalize multi-class
+        if is_multiclass and result.sum() > 0:
+            result = result / result.sum()
+        
+        return result
+    
+    def has_market(self, market: str) -> bool:
+        """Check if a specific market model is loaded."""
+        return market in self.models
+    
+    def predict_match(
+        self,
+        match_id: str,
+        home_team: str,
+        away_team: str,
+        features: Dict[str, float],
+        odds: Optional[Dict[str, float]] = None,
+    ) -> MatchPrediction:
+        """
+        Predict all markets for a match.
+        
+        Args:
+            match_id: Match identifier.
+            home_team: Home team name.
+            away_team: Away team name.
+            features: Feature dictionary.
+            odds: Optional odds dictionary for value bet detection.
+            
+        Returns:
+            MatchPrediction object.
+        """
+        # Get predictions for each market
+        home_prob, draw_prob, away_prob = self.predict_ms(features)
+        over_prob, under_prob = self.predict_ou25(features)
+        btts_yes_prob, btts_no_prob = self.predict_btts(features)
+        
+        # Determine picks
+        ms_probs = {'1': home_prob, 'X': draw_prob, '2': away_prob}
+        ms_pick = max(ms_probs, key=ms_probs.__getitem__)
+        ms_confidence = ms_probs[ms_pick] * 100
+        
+        ou25_probs = {'Over': over_prob, 'Under': under_prob}
+        ou25_pick = max(ou25_probs, key=ou25_probs.__getitem__)
+        ou25_confidence = ou25_probs[ou25_pick] * 100
+        
+        btts_probs = {'Yes': btts_yes_prob, 'No': btts_no_prob}
+        btts_pick = max(btts_probs, key=btts_probs.__getitem__)
+        btts_confidence = btts_probs[btts_pick] * 100
+        
+        # Create prediction
+        prediction = MatchPrediction(
+            match_id=match_id,
+            home_team=home_team,
+            away_team=away_team,
+            home_prob=home_prob,
+            draw_prob=draw_prob,
+            away_prob=away_prob,
+            ms_pick=ms_pick,
+            ms_confidence=ms_confidence,
+            over_prob=over_prob,
+            under_prob=under_prob,
+            ou25_pick=ou25_pick,
+            ou25_confidence=ou25_confidence,
+            btts_yes_prob=btts_yes_prob,
+            btts_no_prob=btts_no_prob,
+            btts_pick=btts_pick,
+            btts_confidence=btts_confidence,
+        )
+        
+        # Detect value bets
+        if odds:
+            prediction.value_bets = self._detect_value_bets(
+                prediction, odds, home_prob, draw_prob, away_prob,
+                over_prob, under_prob, btts_yes_prob, btts_no_prob
+            )
+        
+        return prediction
+    
+    def _detect_value_bets(
+        self,
+        prediction: MatchPrediction,
+        odds: Dict[str, float],
+        home_prob: float,
+        draw_prob: float,
+        away_prob: float,
+        over_prob: float,
+        under_prob: float,
+        btts_yes_prob: float,
+        btts_no_prob: float,
+    ) -> List[ValueBet]:
+        """Detect value bets based on model vs market odds."""
+        value_bets = []
+        # Market-specific minimum edge thresholds
+        # MS: higher variance → require more edge
+        # OU/BTTS: binary markets → tighter edge acceptable
+        EDGE_THRESHOLDS = {
+            'MS': 0.06,
+            'OU25': 0.04,
+            'BTTS': 0.04,
+        }
+        ms_edge = EDGE_THRESHOLDS['MS']
+        ou_edge = EDGE_THRESHOLDS['OU25']
+        btts_edge = EDGE_THRESHOLDS['BTTS']
+
+        # MS value bets
+        if 'ms_h' in odds and odds['ms_h'] > 0:
+            implied = 1 / odds['ms_h']
+            edge = home_prob - implied
+            if edge > ms_edge:
+                value_bets.append(ValueBet(
+                    market_type='MS',
+                    pick='1',
+                    probability=home_prob,
+                    odds=odds['ms_h'],
+                    edge=edge,
+                    confidence=home_prob * 100,
+                ))
+        
+        if 'ms_d' in odds and odds['ms_d'] > 0:
+            implied = 1 / odds['ms_d']
+            edge = draw_prob - implied
+            if edge > ms_edge:
+                value_bets.append(ValueBet(
+                    market_type='MS',
+                    pick='X',
+                    probability=draw_prob,
+                    odds=odds['ms_d'],
+                    edge=edge,
+                    confidence=draw_prob * 100,
+                ))
+        
+        if 'ms_a' in odds and odds['ms_a'] > 0:
+            implied = 1 / odds['ms_a']
+            edge = away_prob - implied
+            if edge > ms_edge:
+                value_bets.append(ValueBet(
+                    market_type='MS',
+                    pick='2',
+                    probability=away_prob,
+                    odds=odds['ms_a'],
+                    edge=edge,
+                    confidence=away_prob * 100,
+                ))
+        
+        # OU25 value bets
+        if 'ou25_o' in odds and odds['ou25_o'] > 0:
+            implied = 1 / odds['ou25_o']
+            edge = over_prob - implied
+            if edge > ou_edge:
+                value_bets.append(ValueBet(
+                    market_type='OU25',
+                    pick='Over',
+                    probability=over_prob,
+                    odds=odds['ou25_o'],
+                    edge=edge,
+                    confidence=over_prob * 100,
+                ))
+        
+        if 'ou25_u' in odds and odds['ou25_u'] > 0:
+            implied = 1 / odds['ou25_u']
+            edge = under_prob - implied
+            if edge > ou_edge:
+                value_bets.append(ValueBet(
+                    market_type='OU25',
+                    pick='Under',
+                    probability=under_prob,
+                    odds=odds['ou25_u'],
+                    edge=edge,
+                    confidence=under_prob * 100,
+                ))
+        
+        # BTTS value bets
+        if 'btts_y' in odds and odds['btts_y'] > 0:
+            implied = 1 / odds['btts_y']
+            edge = btts_yes_prob - implied
+            if edge > btts_edge:
+                value_bets.append(ValueBet(
+                    market_type='BTTS',
+                    pick='Yes',
+                    probability=btts_yes_prob,
+                    odds=odds['btts_y'],
+                    edge=edge,
+                    confidence=btts_yes_prob * 100,
+                ))
+        
+        if 'btts_n' in odds and odds['btts_n'] > 0:
+            implied = 1 / odds['btts_n']
+            edge = btts_no_prob - implied
+            if edge > btts_edge:
+                value_bets.append(ValueBet(
+                    market_type='BTTS',
+                    pick='No',
+                    probability=btts_no_prob,
+                    odds=odds['btts_n'],
+                    edge=edge,
+                    confidence=btts_no_prob * 100,
+                ))
+        
+        return value_bets
+
+
+# Singleton instance
+_v25_predictor: Optional[V25Predictor] = None
+
+
+def get_v25_predictor() -> V25Predictor:
+    """Get or create V25 predictor instance."""
+    global _v25_predictor
+    if _v25_predictor is None:
+        _v25_predictor = V25Predictor()
+        _v25_predictor.load_models()
+    return _v25_predictor

ai-engine/models/v27_predictor.py

@@ -0,0 +1,343 @@
+"""
+V27 Pro Predictor — Odds-Free Fundamentals + Value Edge Detection
+
+This module loads V27 ensemble models (XGBoost, LightGBM, CatBoost)
+and produces market-independent probability estimates.
+
+The key insight: V27 is trained WITHOUT odds features, so it produces
+"true" probabilities unbiased by market pricing. The divergence between
+V25 (odds-aware) and V27 (odds-free) predictions signals market mispricing.
+"""
+
+import json
+import logging
+import os
+import pickle
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+V27_DIR = Path(__file__).parent / "v27"
+
+
+class V27Predictor:
+    """
+    Loads V27 ensemble models and provides predictions using the
+    82-feature odds-free vector.
+    """
+
+    MARKETS = ['ms', 'ou25', 'btts']
+
+    def __init__(self):
+        self.models: Dict[str, Dict[str, object]] = {}
+        self.feature_cols: List[str] = []
+        self._loaded = False
+
+    def load_models(self) -> bool:
+        """Load all V27 ensemble models and feature column spec."""
+        if self._loaded:
+            return True
+
+        # Feature columns
+        cols_path = V27_DIR / "v27_feature_cols.json"
+        if not cols_path.exists():
+            logger.error("[V27] Feature columns file not found: %s", cols_path)
+            return False
+
+        try:
+            with open(cols_path, "r", encoding="utf-8") as f:
+                self.feature_cols = json.load(f)
+            logger.info("[V27] Loaded %d feature columns", len(self.feature_cols))
+        except Exception as e:
+            logger.error("[V27] Failed to load feature columns: %s", e)
+            return False
+
+        # Load models per market
+        model_types = {"xgb": "xgb", "lgb": "lgb"}
+
+        for market in self.MARKETS:
+            self.models[market] = {}
+            for short, label in model_types.items():
+                # Try market-specific file first: v27_ms_xgb.pkl
+                path = V27_DIR / f"v27_{market}_{short}.pkl"
+                if not path.exists():
+                    # Fallback to generic: v27_xgboost.pkl (for MS only)
+                    generic_names = {"xgb": "v27_xgboost.pkl", "lgb": "v27_lightgbm.pkl", "cb": "v27_catboost.pkl"}
+                    path = V27_DIR / generic_names.get(short, "")
+                    if not path.exists():
+                        logger.warning("[V27] Model file not found for %s/%s", market, short)
+                        continue
+
+                try:
+                    with open(path, "rb") as f:
+                        model = pickle.load(f)
+                    self.models[market][label] = model
+                    logger.info("[V27] ✓ Loaded %s/%s from %s", market, label, path.name)
+                except Exception as e:
+                    logger.error("[V27] ✗ Failed to load %s/%s: %s", market, label, e)
+
+        loaded_count = sum(len(v) for v in self.models.values())
+        if loaded_count == 0:
+            logger.error("[V27] No models loaded!")
+            return False
+
+        self._loaded = True
+        logger.info("[V27] Total models loaded: %d across %d markets", loaded_count, len(self.models))
+        return True
+
+    def _build_feature_array(self, features: Dict[str, float]) -> np.ndarray:
+        """
+        Build ordered feature array from the full feature dict.
+        V27 uses only its 82 features (odds-free subset).
+        """
+        row = []
+        for col in self.feature_cols:
+            row.append(float(features.get(col, 0.0)))
+        return np.array([row])
+
+    def _predict_with_model(self, model, X: np.ndarray, label: str, expected_classes: int) -> Optional[np.ndarray]:
+        """
+        Predict probabilities from a model, handling both sklearn wrappers
+        (predict_proba) and raw Booster objects (predict).
+
+        For raw XGBoost Boosters, DMatrix is created WITH feature_names
+        to match the training schema.
+        """
+        import xgboost as xgb
+        import lightgbm as lgbm
+        import pandas as pd
+
+        # 1. Try sklearn-style predict_proba first
+        if hasattr(model, 'predict_proba'):
+            try:
+                proba = model.predict_proba(X)[0]
+                if len(proba) == expected_classes:
+                    return proba
+                logger.warning("[V27] %s predict_proba returned %d classes, expected %d", label, len(proba), expected_classes)
+            except Exception:
+                pass  # Fall through to raw predict
+
+        # 2. Raw xgboost.Booster — MUST pass feature_names
+        if isinstance(model, xgb.Booster):
+            try:
+                feature_names = self.feature_cols if self.feature_cols else None
+                dmat = xgb.DMatrix(X, feature_names=feature_names)
+                raw = model.predict(dmat)
+                if isinstance(raw, np.ndarray):
+                    if raw.ndim == 2 and raw.shape[1] == expected_classes:
+                        return raw[0]
+                    elif raw.ndim == 1 and expected_classes == 2:
+                        p = float(raw[0])
+                        return np.array([1.0 - p, p])
+                    elif raw.ndim == 1 and len(raw) == expected_classes:
+                        return raw
+            except Exception as e:
+                logger.warning("[V27] %s xgb.Booster predict failed: %s", label, e)
+                return None
+
+        # 3. Raw lightgbm.Booster — pass as DataFrame with column names
+        if isinstance(model, lgbm.Booster):
+            try:
+                if self.feature_cols:
+                    X_named = pd.DataFrame(X, columns=self.feature_cols)
+                    raw = model.predict(X_named)
+                else:
+                    raw = model.predict(X)
+                if isinstance(raw, np.ndarray):
+                    if raw.ndim == 2 and raw.shape[1] == expected_classes:
+                        return raw[0]
+                    elif raw.ndim == 1 and expected_classes == 2:
+                        p = float(raw[0])
+                        return np.array([1.0 - p, p])
+                    elif raw.ndim == 1 and len(raw) == expected_classes:
+                        return raw
+            except Exception as e:
+                logger.warning("[V27] %s lgb.Booster predict failed: %s", label, e)
+                return None
+
+        # 4. Generic fallback (CatBoost, etc.)
+        try:
+            if hasattr(model, 'predict'):
+                raw = model.predict(X)
+                if isinstance(raw, np.ndarray):
+                    if raw.ndim == 2 and raw.shape[1] == expected_classes:
+                        return raw[0]
+                    elif raw.ndim == 1 and expected_classes == 2:
+                        p = float(raw[0])
+                        return np.array([1.0 - p, p])
+                    elif raw.ndim == 1 and len(raw) == expected_classes:
+                        return raw
+        except Exception as e:
+            logger.warning("[V27] %s generic predict failed: %s", label, e)
+
+        return None
+
+    def predict_ms(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Match Score probabilities (Home/Draw/Away).
+        Returns dict with keys: home, draw, away.
+        """
+        if not self._loaded or "ms" not in self.models or not self.models["ms"]:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models["ms"].items():
+            proba = self._predict_with_model(model, X, f"MS/{label}", expected_classes=3)
+            if proba is not None and len(proba) == 3:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        # Ensemble average
+        avg = np.mean(probs_list, axis=0)
+        return {
+            "home": float(avg[0]),
+            "draw": float(avg[1]),
+            "away": float(avg[2]),
+        }
+
+    def predict_ou25(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Over/Under 2.5 probabilities.
+        Returns dict with keys: under, over.
+        """
+        if not self._loaded or "ou25" not in self.models or not self.models["ou25"]:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models["ou25"].items():
+            proba = self._predict_with_model(model, X, f"OU25/{label}", expected_classes=2)
+            if proba is not None and len(proba) == 2:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        avg = np.mean(probs_list, axis=0)
+        return {
+            "under": float(avg[0]),
+            "over": float(avg[1]),
+        }
+
+    def predict_btts(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Both Teams To Score probabilities.
+        Returns dict with keys: no, yes.
+        """
+        if not self._loaded or 'btts' not in self.models or not self.models['btts']:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models['btts'].items():
+            proba = self._predict_with_model(model, X, f'BTTS/{label}', expected_classes=2)
+            if proba is not None and len(proba) == 2:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        avg = np.mean(probs_list, axis=0)
+        return {
+            'no': float(avg[0]),
+            'yes': float(avg[1]),
+        }
+
+    def predict_dc(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Double Chance probabilities.
+
+        DC is algebraically derived from MS predictions:
+          1X = home + draw
+          X2 = draw + away
+          12 = home + away
+
+        This gives an odds-free DC estimate for divergence detection.
+        """
+        ms_probs = self.predict_ms(features)
+        if not ms_probs:
+            return None
+
+        home = ms_probs['home']
+        draw = ms_probs['draw']
+        away = ms_probs['away']
+
+        return {
+            '1x': round(home + draw, 4),
+            'x2': round(draw + away, 4),
+            '12': round(home + away, 4),
+        }
+
+    def predict_all(self, features: Dict[str, float]) -> Dict[str, Optional[Dict[str, float]]]:
+        """Run predictions for all supported markets."""
+        return {
+            'ms': self.predict_ms(features),
+            'ou25': self.predict_ou25(features),
+            'btts': self.predict_btts(features),
+            'dc': self.predict_dc(features),
+        }
+
+
+def compute_divergence(
+    v25_probs: Dict[str, float],
+    v27_probs: Dict[str, float],
+) -> Dict[str, float]:
+    """
+    Compute the divergence signal between V25 (odds-aware) and V27 (odds-free).
+
+    Positive divergence = V27 thinks it's MORE likely than the market → VALUE BET
+    Negative divergence = V27 thinks it's LESS likely than the market → PASS
+
+    Returns per-outcome divergence values.
+    """
+    divergence = {}
+    for key in v27_probs:
+        v25_val = v25_probs.get(key, 0.33)
+        v27_val = v27_probs.get(key, 0.33)
+        divergence[key] = round(v27_val - v25_val, 4)
+    return divergence
+
+
+def compute_value_edge(
+    v25_probs: Dict[str, float],
+    v27_probs: Dict[str, float],
+    odds: Dict[str, float],
+) -> Dict[str, Dict]:
+    """
+    Detect value bets by combining V25/V27 divergence with odds.
+
+    A value bet exists when:
+    1. V27 (odds-free) probability > implied odds probability  (model says it's underpriced)
+    2. V27 and V25 divergence is positive  (V27 sees more signal than the market)
+
+    Returns per-outcome: { probability, implied_prob, edge, is_value }
+    """
+    results = {}
+    for key in v27_probs:
+        v27_p = v27_probs[key]
+        v25_p = v25_probs.get(key, 0.33)
+        odds_val = odds.get(key, 0.0)
+
+        implied_p = (1.0 / odds_val) if odds_val > 1.01 else 0.0
+        divergence = v27_p - v25_p
+        edge = v27_p - implied_p if implied_p > 0 else 0.0
+
+        results[key] = {
+            "v27_prob": round(v27_p, 4),
+            "v25_prob": round(v25_p, 4),
+            "implied_prob": round(implied_p, 4),
+            "divergence": round(divergence, 4),
+            "edge": round(edge, 4),
+            "is_value": edge > 0.05 and divergence > 0.02,  # 5% edge + 2% divergence
+        }
+
+    return results

ai-engine/reports/backtest_consistency.json

@@ -0,0 +1,160 @@
+{
+  "total_test": 23039,
+  "thresholds": {
+    "0.0": {
+      "n_matches": 22227,
+      "pct": 96.5,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.5363,
+          "avg_roi": -0.0046,
+          "total_roi": -103.02
+        },
+        "ou15": {
+          "hit_rate": 0.7463,
+          "avg_roi": 0.0144,
+          "total_roi": 319.02
+        },
+        "ou25": {
+          "hit_rate": 0.6111,
+          "avg_roi": -0.006,
+          "total_roi": -134.41
+        },
+        "ou35": {
+          "hit_rate": 0.7302,
+          "avg_roi": -0.014,
+          "total_roi": -310.51
+        },
+        "btts": {
+          "hit_rate": 0.5848,
+          "avg_roi": 0.0031,
+          "total_roi": 69.5
+        }
+      }
+    },
+    "0.1": {
+      "n_matches": 23033,
+      "pct": 100.0,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.546,
+          "avg_roi": -0.0045,
+          "total_roi": -104.38
+        },
+        "ou15": {
+          "hit_rate": 0.7533,
+          "avg_roi": 0.0145,
+          "total_roi": 335.02
+        },
+        "ou25": {
+          "hit_rate": 0.6193,
+          "avg_roi": -0.0042,
+          "total_roi": -96.97
+        },
+        "ou35": {
+          "hit_rate": 0.7277,
+          "avg_roi": -0.0147,
+          "total_roi": -338.57
+        },
+        "btts": {
+          "hit_rate": 0.5886,
+          "avg_roi": 0.0025,
+          "total_roi": 57.21
+        }
+      }
+    },
+    "0.2": {
+      "n_matches": 23034,
+      "pct": 100.0,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.5459,
+          "avg_roi": -0.0046,
+          "total_roi": -105.38
+        },
+        "ou15": {
+          "hit_rate": 0.7533,
+          "avg_roi": 0.0146,
+          "total_roi": 335.26
+        },
+        "ou25": {
+          "hit_rate": 0.6193,
+          "avg_roi": -0.0043,
+          "total_roi": -97.97
+        },
+        "ou35": {
+          "hit_rate": 0.7276,
+          "avg_roi": -0.0147,
+          "total_roi": -339.57
+        },
+        "btts": {
+          "hit_rate": 0.5887,
+          "avg_roi": 0.0025,
+          "total_roi": 57.62
+        }
+      }
+    },
+    "0.3": {
+      "n_matches": 23039,
+      "pct": 100.0,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.546,
+          "avg_roi": -0.0045,
+          "total_roi": -103.45
+        },
+        "ou15": {
+          "hit_rate": 0.7534,
+          "avg_roi": 0.0146,
+          "total_roi": 335.6
+        },
+        "ou25": {
+          "hit_rate": 0.6194,
+          "avg_roi": -0.0042,
+          "total_roi": -97.44
+        },
+        "ou35": {
+          "hit_rate": 0.7277,
+          "avg_roi": -0.0147,
+          "total_roi": -339.26
+        },
+        "btts": {
+          "hit_rate": 0.5887,
+          "avg_roi": 0.0025,
+          "total_roi": 58.61
+        }
+      }
+    },
+    "0.5": {
+      "n_matches": 23039,
+      "pct": 100.0,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.546,
+          "avg_roi": -0.0045,
+          "total_roi": -103.45
+        },
+        "ou15": {
+          "hit_rate": 0.7534,
+          "avg_roi": 0.0146,
+          "total_roi": 335.6
+        },
+        "ou25": {
+          "hit_rate": 0.6194,
+          "avg_roi": -0.0042,
+          "total_roi": -97.44
+        },
+        "ou35": {
+          "hit_rate": 0.7277,
+          "avg_roi": -0.0147,
+          "total_roi": -339.26
+        },
+        "btts": {
+          "hit_rate": 0.5887,
+          "avg_roi": 0.0025,
+          "total_roi": 58.61
+        }
+      }
+    }
+  }
+}

ai-engine/reports/backtest_real_odds.json

@@ -0,0 +1,5 @@
+[
+  {
+    "market": "MS-Ev",
+    "min_edge": 0.02,
+    "n": 

ai-engine/reports/backtest_results.json

@@ -0,0 +1,267 @@
+{
+  "generated_at": "2026-05-15T21:40:57.995899",
+  "matches_processed": 3000,
+  "matches_skipped": 0,
+  "markets": {
+    "MS": {
+      "overall_accuracy": 54.97,
+      "total_matches": 3000,
+      "by_confidence_band": {
+        "<50%": {
+          "accuracy": 38.87,
+          "count": 759,
+          "mean_confidence": 45.58
+        },
+        "50-65%": {
+          "accuracy": 52.62,
+          "count": 1300,
+          "mean_confidence": 57.19
+        },
+        "65-75%": {
+          "accuracy": 66.99,
+          "count": 624,
+          "mean_confidence": 69.49
+        },
+        "75%+": {
+          "accuracy": 79.5,
+          "count": 317,
+          "mean_confidence": 80.69
+        }
+      },
+      "by_league": {
+        "Bundesliga": {
+          "accuracy": 46.77,
+          "count": 62
+        },
+        "Ligue 1": {
+          "accuracy": 58.73,
+          "count": 63
+        },
+        "Serie A": {
+          "accuracy": 56.25,
+          "count": 64
+        },
+        "Other": {
+          "accuracy": 55.03,
+          "count": 2811
+        }
+      },
+      "by_pick_direction": {
+        "1": {
+          "accuracy": 58.38,
+          "count": 1946,
+          "mean_confidence": 60.84
+        },
+        "2": {
+          "accuracy": 48.72,
+          "count": 1053,
+          "mean_confidence": 56.44
+        },
+        "X": {
+          "accuracy": 0.0,
+          "count": 1,
+          "mean_confidence": 56.07
+        }
+      }
+    },
+    "OU15": {
+      "overall_accuracy": 74.4,
+      "total_matches": 3000,
+      "by_confidence_band": {
+        "50-65%": {
+          "accuracy": 70.97,
+          "count": 62,
+          "mean_confidence": 59.63
+        },
+        "65-75%": {
+          "accuracy": 68.0,
+          "count": 275,
+          "mean_confidence": 71.1
+        },
+        "75%+": {
+          "accuracy": 75.14,
+          "count": 2663,
+          "mean_confidence": 89.44
+        }
+      },
+      "by_league": {
+        "Bundesliga": {
+          "accuracy": 67.74,
+          "count": 62
+        },
+        "Ligue 1": {
+          "accuracy": 76.19,
+          "count": 63
+        },
+        "Serie A": {
+          "accuracy": 70.31,
+          "count": 64
+        },
+        "Other": {
+          "accuracy": 74.6,
+          "count": 2811
+        }
+      },
+      "by_pick_direction": {
+        "Over": {
+          "accuracy": 74.4,
+          "count": 3000,
+          "mean_confidence": 87.14
+        }
+      }
+    },
+    "OU25": {
+      "overall_accuracy": 51.77,
+      "total_matches": 3000,
+      "by_confidence_band": {
+        "50-65%": {
+          "accuracy": 49.33,
+          "count": 1267,
+          "mean_confidence": 57.13
+        },
+        "65-75%": {
+          "accuracy": 54.53,
+          "count": 453,
+          "mean_confidence": 69.42
+        },
+        "75%+": {
+          "accuracy": 53.2,
+          "count": 1280,
+          "mean_confidence": 90.2
+        }
+      },
+      "by_league": {
+        "Bundesliga": {
+          "accuracy": 41.94,
+          "count": 62
+        },
+        "Ligue 1": {
+          "accuracy": 50.79,
+          "count": 63
+        },
+        "Serie A": {
+          "accuracy": 43.75,
+          "count": 64
+        },
+        "Other": {
+          "accuracy": 52.19,
+          "count": 2811
+        }
+      },
+      "by_pick_direction": {
+        "Over": {
+          "accuracy": 51.03,
+          "count": 2432,
+          "mean_confidence": 76.11
+        },
+        "Under": {
+          "accuracy": 54.93,
+          "count": 568,
+          "mean_confidence": 60.17
+        }
+      }
+    },
+    "BTTS": {
+      "overall_accuracy": 51.83,
+      "total_matches": 3000,
+      "by_confidence_band": {
+        "50-65%": {
+          "accuracy": 48.74,
+          "count": 2214,
+          "mean_confidence": 58.66
+        },
+        "65-75%": {
+          "accuracy": 60.42,
+          "count": 758,
+          "mean_confidence": 68.19
+        },
+        "75%+": {
+          "accuracy": 64.29,
+          "count": 28,
+          "mean_confidence": 77.44
+        }
+      },
+      "by_league": {
+        "Bundesliga": {
+          "accuracy": 54.84,
+          "count": 62
+        },
+        "Ligue 1": {
+          "accuracy": 50.79,
+          "count": 63
+        },
+        "Serie A": {
+          "accuracy": 57.81,
+          "count": 64
+        },
+        "Other": {
+          "accuracy": 51.65,
+          "count": 2811
+        }
+      },
+      "by_pick_direction": {
+        "No": {
+          "accuracy": 50.26,
+          "count": 2099,
+          "mean_confidence": 61.56
+        },
+        "Yes": {
+          "accuracy": 55.49,
+          "count": 901,
+          "mean_confidence": 60.51
+        }
+      }
+    }
+  },
+  "calibration": {
+    "ms_home": {
+      "brier_score": 0.2054,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.026574",
+      "mean_predicted": 0.4942,
+      "mean_actual": 0.46
+    },
+    "ms_draw": {
+      "brier_score": 0.1846,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.030886",
+      "mean_predicted": 0.149,
+      "mean_actual": 0.2493
+    },
+    "ms_away": {
+      "brier_score": 0.1726,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.033980",
+      "mean_predicted": 0.3567,
+      "mean_actual": 0.2907
+    },
+    "ou15": {
+      "brier_score": 0.1884,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.037204",
+      "mean_predicted": 0.8714,
+      "mean_actual": 0.744
+    },
+    "ou25": {
+      "brier_score": 0.247,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.041152",
+      "mean_predicted": 0.6924,
+      "mean_actual": 0.499
+    },
+    "btts": {
+      "brier_score": 0.2453,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.044344",
+      "mean_predicted": 0.4506,
+      "mean_actual": 0.5147
+    }
+  },
+  "runtime_seconds": 94.1
+}

ai-engine/reports/training_v25/v25_market_metrics.json

@@ -1,8 +1,8 @@
 {
-  "trained_at": "2026-04-14 17:20:03",
+  "trained_at": "2026-05-06 15:53:36",
   "market_results": {
     "MS": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -107,19 +107,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
-      "val_samples": 1469,
-      "test_samples": 1469,
-      "xgb_accuracy": 0.8938,
-      "xgb_logloss": 0.2263,
-      "lgb_accuracy": 0.8938,
-      "lgb_logloss": 0.2214,
-      "ensemble_accuracy": 0.8945,
-      "ensemble_logloss": 0.2226,
+      "train_samples": 74499,
+      "val_samples": 15964,
+      "test_samples": 15965,
+      "xgb_accuracy": 0.5437,
+      "xgb_logloss": 0.9429,
+      "lgb_accuracy": 0.5436,
+      "lgb_logloss": 0.9423,
+      "ensemble_accuracy": 0.5442,
+      "ensemble_logloss": 0.9418,
       "class_count": 3
     },
     "OU15": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -224,19 +224,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
-      "val_samples": 1469,
-      "test_samples": 1469,
-      "xgb_accuracy": 0.9088,
-      "xgb_logloss": 0.1758,
-      "lgb_accuracy": 0.9067,
-      "lgb_logloss": 0.1783,
-      "ensemble_accuracy": 0.9108,
-      "ensemble_logloss": 0.1753,
+      "train_samples": 74499,
+      "val_samples": 15964,
+      "test_samples": 15965,
+      "xgb_accuracy": 0.753,
+      "xgb_logloss": 0.5256,
+      "lgb_accuracy": 0.7523,
+      "lgb_logloss": 0.5262,
+      "ensemble_accuracy": 0.7533,
+      "ensemble_logloss": 0.5254,
       "class_count": 2
     },
     "OU25": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -341,19 +341,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
-      "val_samples": 1469,
-      "test_samples": 1469,
-      "xgb_accuracy": 0.9204,
-      "xgb_logloss": 0.1535,
-      "lgb_accuracy": 0.9224,
-      "lgb_logloss": 0.1523,
-      "ensemble_accuracy": 0.9217,
-      "ensemble_logloss": 0.1518,
+      "train_samples": 74499,
+      "val_samples": 15964,
+      "test_samples": 15965,
+      "xgb_accuracy": 0.6253,
+      "xgb_logloss": 0.635,
+      "lgb_accuracy": 0.6246,
+      "lgb_logloss": 0.6347,
+      "ensemble_accuracy": 0.6262,
+      "ensemble_logloss": 0.6343,
       "class_count": 2
     },
     "OU35": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -458,19 +458,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
-      "val_samples": 1469,
-      "test_samples": 1469,
-      "xgb_accuracy": 0.9578,
-      "xgb_logloss": 0.1171,
-      "lgb_accuracy": 0.9564,
-      "lgb_logloss": 0.1144,
-      "ensemble_accuracy": 0.9571,
-      "ensemble_logloss": 0.1149,
+      "train_samples": 74499,
+      "val_samples": 15964,
+      "test_samples": 15965,
+      "xgb_accuracy": 0.7283,
+      "xgb_logloss": 0.5463,
+      "lgb_accuracy": 0.7304,
+      "lgb_logloss": 0.546,
+      "ensemble_accuracy": 0.7297,
+      "ensemble_logloss": 0.5456,
       "class_count": 2
     },
     "BTTS": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -575,19 +575,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
-      "val_samples": 1469,
-      "test_samples": 1469,
-      "xgb_accuracy": 0.9238,
-      "xgb_logloss": 0.1439,
-      "lgb_accuracy": 0.9265,
-      "lgb_logloss": 0.143,
-      "ensemble_accuracy": 0.9265,
-      "ensemble_logloss": 0.1424,
+      "train_samples": 74499,
+      "val_samples": 15964,
+      "test_samples": 15965,
+      "xgb_accuracy": 0.5894,
+      "xgb_logloss": 0.6636,
+      "lgb_accuracy": 0.5928,
+      "lgb_logloss": 0.6633,
+      "ensemble_accuracy": 0.5897,
+      "ensemble_logloss": 0.6628,
       "class_count": 2
     },
     "HT_RESULT": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -692,19 +692,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
-      "val_samples": 1468,
-      "test_samples": 1468,
-      "xgb_accuracy": 0.5627,
-      "xgb_logloss": 0.8712,
-      "lgb_accuracy": 0.5715,
-      "lgb_logloss": 0.8649,
-      "ensemble_accuracy": 0.5811,
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@@ -926,19 +926,19 @@
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@@ -1160,19 +1160,19 @@
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@@ -1394,15 +1394,15 @@
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ai-engine/reports/training_v25/v25_pro_metrics.json

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+      "test_xgb_raw": {
+        "accuracy": 0.6381,
+        "logloss": 0.6445
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6381,
+        "logloss": 0.6525
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.6419,
+        "logloss": 0.6439
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6402,
+        "logloss": 0.7399
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.6404,
+        "logloss": 0.6439
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6394,
+        "logloss": 0.6495
+      }
+    },
+    "HTFT": {
+      "market": "HTFT",
+      "samples": 31774,
+      "train": 19064,
+      "val": 4766,
+      "cal": 3177,
+      "test": 4767,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.013345313595371834,
+        "subsample": 0.9479953293996353,
+        "colsample_bytree": 0.6615608030428078,
+        "min_child_weight": 9,
+        "gamma": 7.455111012483033e-07,
+        "reg_lambda": 8.89911456381185,
+        "reg_alpha": 5.090564000963078e-06
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.013089530126848842,
+        "feature_fraction": 0.7906053881599783,
+        "bagging_fraction": 0.7440871339717291,
+        "bagging_freq": 4,
+        "min_child_samples": 44,
+        "lambda_l1": 2.0129746481016167e-05,
+        "lambda_l2": 9.879192474391198
+      },
+      "xgb_best_iteration": 366,
+      "lgb_best_iteration": 252,
+      "xgb_optuna_best_logloss": 1.8581,
+      "lgb_optuna_best_logloss": 1.858,
+      "test_xgb_raw": {
+        "accuracy": 0.3168,
+        "logloss": 1.8817
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.3168,
+        "logloss": 1.9133
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.317,
+        "logloss": 1.882
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.3178,
+        "logloss": 1.9643
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.3172,
+        "logloss": 1.8815
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.3184,
+        "logloss": 1.9473
+      }
+    },
+    "ODD_EVEN": {
+      "market": "ODD_EVEN",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.0229001065742241,
+        "subsample": 0.6062940462474563,
+        "colsample_bytree": 0.6480436065939664,
+        "min_child_weight": 3,
+        "gamma": 0.0030457541200722615,
+        "reg_lambda": 7.476931580627848e-05,
+        "reg_alpha": 0.0009042146623162754
+      },
+      "lgb_best_params": {
+        "max_depth": 7,
+        "learning_rate": 0.04326506132261678,
+        "feature_fraction": 0.5682671758256829,
+        "bagging_fraction": 0.7270328719038639,
+        "bagging_freq": 3,
+        "min_child_samples": 16,
+        "lambda_l1": 8.217437741607322e-08,
+        "lambda_l2": 0.3516451122877441
+      },
+      "xgb_best_iteration": 83,
+      "lgb_best_iteration": 68,
+      "xgb_optuna_best_logloss": 0.6925,
+      "lgb_optuna_best_logloss": 0.6922,
+      "test_xgb_raw": {
+        "accuracy": 0.5017,
+        "logloss": 0.6938
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.5053,
+        "logloss": 0.6944
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5069,
+        "logloss": 0.6962
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.5048,
+        "logloss": 0.722
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5003,
+        "logloss": 0.6944
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.5055,
+        "logloss": 0.6942
+      }
+    },
+    "CARDS_OU45": {
+      "market": "CARDS_OU45",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.05954573029858921,
+        "subsample": 0.9384590363195691,
+        "colsample_bytree": 0.7061263810064461,
+        "min_child_weight": 3,
+        "gamma": 0.12648985601647333,
+        "reg_lambda": 0.0005539202839193393,
+        "reg_alpha": 5.4993381294041956e-08
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.03822726574649208,
+        "feature_fraction": 0.5171942605576092,
+        "bagging_fraction": 0.9637281608315128,
+        "bagging_freq": 2,
+        "min_child_samples": 35,
+        "lambda_l1": 3.1166541263980415e-06,
+        "lambda_l2": 0.0004793052550782129
+      },
+      "xgb_best_iteration": 185,
+      "lgb_best_iteration": 338,
+      "xgb_optuna_best_logloss": 0.5583,
+      "lgb_optuna_best_logloss": 0.5582,
+      "test_xgb_raw": {
+        "accuracy": 0.5917,
+        "logloss": 0.6455
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6014,
+        "logloss": 0.6466
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5925,
+        "logloss": 0.6454
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6034,
+        "logloss": 0.6677
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5917,
+        "logloss": 0.6451
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6038,
+        "logloss": 0.6463
+      }
+    },
+    "HANDICAP_MS": {
+      "market": "HANDICAP_MS",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.016879778725504085,
+        "subsample": 0.6294239018550948,
+        "colsample_bytree": 0.6694819528702206,
+        "min_child_weight": 5,
+        "gamma": 2.9804488909930113e-05,
+        "reg_lambda": 0.2590856513676408,
+        "reg_alpha": 7.468057455332845e-06
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.03267347269820124,
+        "feature_fraction": 0.5009468855100953,
+        "bagging_fraction": 0.7336123380897622,
+        "bagging_freq": 1,
+        "min_child_samples": 10,
+        "lambda_l1": 1.3880195780862006e-06,
+        "lambda_l2": 0.7568094447483327
+      },
+      "xgb_best_iteration": 406,
+      "lgb_best_iteration": 231,
+      "xgb_optuna_best_logloss": 0.907,
+      "lgb_optuna_best_logloss": 0.9064,
+      "test_xgb_raw": {
+        "accuracy": 0.5838,
+        "logloss": 0.9235
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.5865,
+        "logloss": 0.9347
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5797,
+        "logloss": 0.9257
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.5832,
+        "logloss": 0.9467
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5818,
+        "logloss": 0.9244
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.5836,
+        "logloss": 0.9387
+      }
+    }
+  }
+}

ai-engine/reports/training_v26_shadow.json

@@ -0,0 +1,19 @@
+{
+  "version": "v26.shadow.0",
+  "calibration_version": "v26.shadow.calib.0",
+  "train_rows": 6853,
+  "validation_rows": 1469,
+  "label_priors": {
+    "MS": 0.4404,
+    "OU25": 0.5214,
+    "BTTS": 0.5398,
+    "HT": 0.4275,
+    "HTFT": 0.26,
+    "CARDS": 0.6052
+  },
+  "artifact_path": "/Users/piton/Documents/GitHub/iddaai/iddaai-be/ai-engine/models/v26_shadow/market_profiles.json",
+  "notes": [
+    "v26.shadow runtime currently uses artifact-based calibration and ROI gating",
+    "market profile JSON remains the source of truth for runtime thresholds"
+  ]
+}

ai-engine/requirements.txt

@@ -17,3 +17,4 @@ pyyaml>=6.0
 # V2 async database
 asyncpg>=0.29.0
 pydantic>=2.5.0
+pytest>=8.0.0

ai-engine/schemas/match_data.py

@@ -0,0 +1,40 @@
+"""
+MatchData dataclass — core data transfer object used throughout the engine.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class MatchData:
+    match_id: str
+    home_team_id: str
+    away_team_id: str
+    home_team_name: str
+    away_team_name: str
+    match_date_ms: int
+    sport: str
+    league_id: Optional[str]
+    league_name: str
+    referee_name: Optional[str]
+    odds_data: Dict[str, float]
+    home_lineup: Optional[List[str]]
+    away_lineup: Optional[List[str]]
+    sidelined_data: Optional[Dict[str, Any]]
+    home_goals_avg: float
+    home_conceded_avg: float
+    away_goals_avg: float
+    away_conceded_avg: float
+    home_position: int
+    away_position: int
+    lineup_source: str
+    status: str = ""
+    state: Optional[str] = None
+    substate: Optional[str] = None
+    current_score_home: Optional[int] = None
+    current_score_away: Optional[int] = None
+    lineup_confidence: float = 0.0
+    source_table: str = "matches"

ai-engine/schemas/prediction.py

@@ -0,0 +1,292 @@
+"""
+Shared prediction dataclasses used across the AI engine.
+
+These were originally defined in models/v20_ensemble.py and are extracted here
+so they can be used without importing the full V20 ensemble.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+from core.calculators.score_calculator import ScorePrediction
+
+
+@dataclass
+class MarketPrediction:
+    """Prediction for a single betting market."""
+    market_type: str
+    pick: str
+    probability: float
+    confidence: float
+    odds: float = 0.0
+    is_recommended: bool = False
+    is_value_bet: bool = False
+    edge: float = 0.0  # Expected edge over market
+
+    def to_dict(self) -> dict:
+        return {
+            "market_type": self.market_type,
+            "pick": self.pick,
+            "probability": round(self.probability * 100, 1),
+            "confidence": round(self.confidence, 1),
+            "odds": self.odds,
+            "is_recommended": self.is_recommended,
+            "is_value_bet": self.is_value_bet,
+            "edge": round(self.edge, 1)
+        }
+
+
+@dataclass
+class FullMatchPrediction:
+    """Complete prediction for a match with ALL markets."""
+    match_id: str
+    home_team: str
+    away_team: str
+    match_date: str = ""
+
+    # === MAÇ SONUCU (1X2) ===
+    ms_home_prob: float = 0.33
+    ms_draw_prob: float = 0.33
+    ms_away_prob: float = 0.33
+    ms_pick: str = ""
+    ms_confidence: float = 0.0
+
+    # === ÇİFTE ŞANS ===
+    dc_1x_prob: float = 0.66
+    dc_x2_prob: float = 0.66
+    dc_12_prob: float = 0.66
+    dc_pick: str = ""
+    dc_confidence: float = 0.0
+
+    # === ALT/ÜST GOLLER ===
+    # 1.5
+    over_15_prob: float = 0.70
+    under_15_prob: float = 0.30
+    ou15_pick: str = ""
+    ou15_confidence: float = 0.0
+
+    # 2.5
+    over_25_prob: float = 0.50
+    under_25_prob: float = 0.50
+    ou25_pick: str = ""
+    ou25_confidence: float = 0.0
+
+    # 3.5
+    over_35_prob: float = 0.30
+    under_35_prob: float = 0.70
+    ou35_pick: str = ""
+    ou35_confidence: float = 0.0
+
+    # === KARŞILIKLI GOL (BTTS) ===
+    btts_yes_prob: float = 0.50
+    btts_no_prob: float = 0.50
+    btts_pick: str = ""
+    btts_confidence: float = 0.0
+
+    # === İLK YARI SONUCU ===
+    ht_home_prob: float = 0.30
+    ht_draw_prob: float = 0.40
+    ht_away_prob: float = 0.30
+    ht_pick: str = ""
+    ht_confidence: float = 0.0
+
+    # === SKOR TAHMİNLERİ ===
+    score: Optional[ScorePrediction] = None
+    predicted_ft_score: str = "1-1"
+    predicted_ht_score: str = "0-0"
+    ft_scores_top5: List[Dict] = field(default_factory=list)
+
+    # === xG (Expected Goals) ===
+    home_xg: float = 1.3
+    away_xg: float = 1.1
+    total_xg: float = 2.4
+
+    # === RISK DEĞERLENDİRMESİ ===
+    risk_level: str = "MEDIUM"  # LOW, MEDIUM, HIGH, EXTREME
+    risk_score: float = 0.0
+    is_surprise_risk: bool = False
+    surprise_type: str = ""
+    risk_warnings: List[str] = field(default_factory=list)
+    ht_ft_probs: Dict[str, float] = field(default_factory=dict)
+
+    # === GLM-5 SÜRPRİZ SKORU ===
+    upset_score: int = 0  # 0-100 arası sürpriz skoru
+    upset_level: str = "LOW"  # LOW, MEDIUM, HIGH, EXTREME
+    upset_reasons: List[str] = field(default_factory=list)
+
+    # === SÜRPRİZ PROFİLİ ===
+    surprise_score: float = 0.0        # 0-100 overall surprise risk score
+    surprise_comment: str = ""         # Human-readable surprise commentary
+    surprise_reasons: List[str] = field(default_factory=list)  # Flagged risk reasons
+    surprise_breakdown: List[Dict[str, Any]] = field(default_factory=list)  # Per-factor {code, points, label}
+
+    # === ENGINE KATKILARI ===
+    team_confidence: float = 0.0
+    player_confidence: float = 0.0
+    odds_confidence: float = 0.0
+    referee_confidence: float = 0.0
+
+    # === KORNER & KART & DİĞER ===
+    total_corners_pred: float = 9.5
+    corner_pick: str = "9.5 Üst"
+
+    total_cards_pred: float = 4.5
+    card_pick: str = "4.5 Alt"
+    cards_over_prob: float = 0.50
+    cards_under_prob: float = 0.50
+    cards_confidence: float = 0.0
+
+    handicap_pick: str = ""
+    handicap_home_prob: float = 0.33
+    handicap_draw_prob: float = 0.34
+    handicap_away_prob: float = 0.33
+    handicap_confidence: float = 0.0
+
+    ht_over_05_prob: float = 0.65
+    ht_under_05_prob: float = 0.35
+    ht_over_15_prob: float = 0.30
+    ht_under_15_prob: float = 0.70
+    ht_ou_pick: str = "İY 0.5 Üst"
+    ht_ou15_pick: str = "İY 1.5 Alt"
+
+    odd_even_pick: str = "Çift"
+    odd_prob: float = 0.50   # Tek olasılığı
+    even_prob: float = 0.50  # Çift olasılığı
+
+    # === TAVSİYELER (RECOMMENDATIONS) ===
+    best_bet: Optional[MarketPrediction] = None
+    recommended_bets: List[MarketPrediction] = field(default_factory=list)
+    alternative_bet: Optional[MarketPrediction] = None
+    expert_recommendation: Dict[str, Any] = field(default_factory=dict)
+
+    # === DETAILED ANALYSIS ===
+    analysis_details: Dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict:
+        return {
+            "match_info": {
+                "match_id": self.match_id,
+                "home_team": self.home_team,
+                "away_team": self.away_team,
+                "match_date": self.match_date
+            },
+            "predictions": {
+                "match_result": {
+                    "1": round(self.ms_home_prob * 100, 1),
+                    "X": round(self.ms_draw_prob * 100, 1),
+                    "2": round(self.ms_away_prob * 100, 1),
+                    "pick": self.ms_pick,
+                    "confidence": round(self.ms_confidence, 1)
+                },
+                "double_chance": {
+                    "1X": round(self.dc_1x_prob * 100, 1),
+                    "X2": round(self.dc_x2_prob * 100, 1),
+                    "12": round(self.dc_12_prob * 100, 1),
+                    "pick": self.dc_pick,
+                    "confidence": round(self.dc_confidence, 1)
+                },
+                "over_under": {
+                    "1.5": {
+                        "over": round(self.over_15_prob * 100, 1),
+                        "under": round(self.under_15_prob * 100, 1),
+                        "pick": self.ou15_pick,
+                        "confidence": round(self.ou15_confidence, 1)
+                    },
+                    "2.5": {
+                        "over": round(self.over_25_prob * 100, 1),
+                        "under": round(self.under_25_prob * 100, 1),
+                        "pick": self.ou25_pick,
+                        "confidence": round(self.ou25_confidence, 1)
+                    },
+                    "3.5": {
+                        "over": round(self.over_35_prob * 100, 1),
+                        "under": round(self.under_35_prob * 100, 1),
+                        "pick": self.ou35_pick,
+                        "confidence": round(self.ou35_confidence, 1)
+                    }
+                },
+                "btts": {
+                    "yes": round(self.btts_yes_prob * 100, 1),
+                    "no": round(self.btts_no_prob * 100, 1),
+                    "pick": self.btts_pick,
+                    "confidence": round(self.btts_confidence, 1)
+                },
+                "first_half": {
+                    "1": round(self.ht_home_prob * 100, 1),
+                    "X": round(self.ht_draw_prob * 100, 1),
+                    "2": round(self.ht_away_prob * 100, 1),
+                    "pick": self.ht_pick,
+                    "confidence": round(self.ht_confidence, 1),
+                    "over_under_05": {
+                        "over": round(self.ht_over_05_prob * 100, 1),
+                        "under": round(self.ht_under_05_prob * 100, 1),
+                        "pick": self.ht_ou_pick
+                    },
+                    "over_under_15": {
+                        "over": round(self.ht_over_15_prob * 100, 1),
+                        "under": round(self.ht_under_15_prob * 100, 1),
+                        "pick": self.ht_ou15_pick
+                    }
+                },
+                "scores": {
+                    "predicted_ft": self.predicted_ft_score,
+                    "predicted_ht": self.predicted_ht_score,
+                    "top_5_ft_scores": self.ft_scores_top5
+                },
+                "others": {
+                    "handicap": {
+                        "pick": self.handicap_pick,
+                        "confidence": round(self.handicap_confidence, 1),
+                        "home": round(self.handicap_home_prob * 100, 1),
+                        "draw": round(self.handicap_draw_prob * 100, 1),
+                        "away": round(self.handicap_away_prob * 100, 1)
+                    },
+                    "corners": {
+                        "total": round(self.total_corners_pred, 1),
+                        "pick": self.corner_pick
+                    },
+                    "cards": {
+                        "total": round(self.total_cards_pred, 1),
+                        "pick": self.card_pick,
+                        "confidence": round(self.cards_confidence, 1),
+                        "over": round(self.cards_over_prob * 100, 1),
+                        "under": round(self.cards_under_prob * 100, 1)
+                    },
+                    "odd_even": {
+                        "pick": self.odd_even_pick,
+                        "tek": round(self.odd_prob * 100, 1),
+                        "cift": round(self.even_prob * 100, 1)
+                    }
+                },
+                "xg": {
+                    "home": round(self.home_xg, 2),
+                    "away": round(self.away_xg, 2),
+                    "total": round(self.total_xg, 2)
+                }
+            },
+            "risk": {
+                "level": self.risk_level,
+                "score": round(self.risk_score, 1),
+                "is_surprise_risk": self.is_surprise_risk,
+                "surprise_type": self.surprise_type,
+                "ht_ft_probs": {k: round(v * 100, 1) for k, v in self.ht_ft_probs.items()} if self.ht_ft_probs else {},
+                "warnings": self.risk_warnings
+            },
+            "upset_analysis": {
+                "score": self.upset_score,
+                "level": self.upset_level,
+                "reasons": self.upset_reasons
+            },
+            "engine_breakdown": {
+                "team_engine": round(self.team_confidence, 1),
+                "player_engine": round(self.player_confidence, 1),
+                "odds_engine": round(self.odds_confidence, 1),
+                "referee_engine": round(self.referee_confidence, 1)
+            },
+            "recommendations": {
+                "best_bet": self.best_bet.to_dict() if self.best_bet else None,
+                "all_recommended": [b.to_dict() for b in self.recommended_bets] if self.recommended_bets else [],
+                "alternative_bet": self.alternative_bet.to_dict() if self.alternative_bet else None
+            },
+            "analysis_details": self.analysis_details
+        }

ai-engine/scripts/backfill_calibration.py

@@ -0,0 +1,510 @@
+"""
+Calibration Backfill Script
+============================
+Runs V25 model against historical matches (using pre-computed ai_features + odds)
+to generate calibration training data, then trains isotonic calibration models.
+
+Usage:
+    python ai-engine/scripts/backfill_calibration.py
+    python ai-engine/scripts/backfill_calibration.py --limit 5000
+    python ai-engine/scripts/backfill_calibration.py --min-samples 50
+"""
+
+import argparse
+import json
+import os
+import sys
+import time
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+import psycopg2
+from psycopg2.extras import RealDictCursor
+from dotenv import load_dotenv
+
+AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, AI_ENGINE_DIR)
+
+from models.v25_ensemble import V25Predictor
+from models.calibration import get_calibrator
+
+load_dotenv()
+
+
+def _normalize_pick(pick) -> str:
+    return str(pick or "").strip().casefold()
+
+
+def resolve_actual(market, pick, score_home, score_away, ht_home, ht_away):
+    if score_home is None or score_away is None:
+        return None
+    market = (market or "").upper()
+    p = _normalize_pick(pick)
+    total = score_home + score_away
+    ht_total = (ht_home or 0) + (ht_away or 0) if ht_home is not None else None
+
+    if market == "MS":
+        if p == "1": return int(score_home > score_away)
+        if p in {"x", "0"}: return int(score_home == score_away)
+        if p == "2": return int(score_away > score_home)
+        return None
+    if market in {"OU15", "OU25", "OU35"}:
+        line = {"OU15": 1.5, "OU25": 2.5, "OU35": 3.5}[market]
+        if "over" in p or "üst" in p or "ust" in p: return int(total > line)
+        if "under" in p or "alt" in p: return int(total < line)
+        return None
+    if market == "BTTS":
+        both = score_home > 0 and score_away > 0
+        if "yes" in p or "var" in p: return int(both)
+        if "no" in p or "yok" in p: return int(not both)
+        return None
+    if market == "HT":
+        if ht_home is None or ht_away is None: return None
+        if p == "1": return int(ht_home > ht_away)
+        if p in {"x", "0"}: return int(ht_home == ht_away)
+        if p == "2": return int(ht_away > ht_home)
+        return None
+    if market == "HTFT":
+        if ht_home is None or ht_away is None or "/" not in p: return None
+        ht_p, ft_p = p.split("/")
+        ht_actual = "1" if ht_home > ht_away else "2" if ht_away > ht_home else "x"
+        ft_actual = "1" if score_home > score_away else "2" if score_away > score_home else "x"
+        return int(ht_p.strip() == ht_actual and ft_p.strip() == ft_actual)
+    if market == "DC":
+        norm = p.replace("-", "").upper()
+        if norm == "1X": return int(score_home >= score_away)
+        if norm == "X2": return int(score_away >= score_home)
+        if norm == "12": return int(score_home != score_away)
+        return None
+    return None
+
+
+def calibrator_key(market, pick):
+    m = (market or "").upper()
+    p = _normalize_pick(pick)
+    if m == "MS":
+        if p == "1": return "ms_home"
+        if p in {"x", "0"}: return "ms_draw"
+        if p == "2": return "ms_away"
+        return None
+    if m == "DC": return "dc"
+    if m == "OU15" and ("over" in p or "üst" in p): return "ou15"
+    if m == "OU25" and ("over" in p or "üst" in p): return "ou25"
+    if m == "OU35" and ("over" in p or "üst" in p): return "ou35"
+    if m == "BTTS" and ("yes" in p or "var" in p): return "btts"
+    if m == "HT":
+        if p == "1": return "ht_home"
+        if p in {"x", "0"}: return "ht_draw"
+        if p == "2": return "ht_away"
+        return None
+    if m == "HTFT": return "ht_ft"
+    return None
+
+
+def get_conn():
+    db_url = os.getenv("DATABASE_URL", "")
+    if "?schema=" in db_url:
+        db_url = db_url.split("?schema=")[0]
+    if not db_url:
+        raise ValueError("DATABASE_URL not set")
+    return psycopg2.connect(db_url, cursor_factory=RealDictCursor)
+
+
+ODD_CAT_MAP = {
+    "maç sonucu": {"1": "ms_h", "0": "ms_d", "x": "ms_d", "2": "ms_a"},
+    "1. yarı sonucu": {"1": "ht_ms_h", "0": "ht_ms_d", "x": "ht_ms_d", "2": "ht_ms_a"},
+}
+
+ODD_CAT_KEYWORD_MAP = {
+    "karşılıklı gol": {"var": "btts_y", "yok": "btts_n"},
+    "0,5 alt/üst": {"alt": "ou05_u", "üst": "ou05_o"},
+    "1,5 alt/üst": {"alt": "ou15_u", "üst": "ou15_o"},
+    "2,5 alt/üst": {"alt": "ou25_u", "üst": "ou25_o"},
+    "3,5 alt/üst": {"alt": "ou35_u", "üst": "ou35_o"},
+    "ilk yarı 0,5 alt/üst": {"alt": "ht_ou05_u", "üst": "ht_ou05_o"},
+    "ilk yarı 1,5 alt/üst": {"alt": "ht_ou15_u", "üst": "ht_ou15_o"},
+}
+
+
+def load_matches(cur, limit: int) -> List[Dict]:
+    cur.execute("""
+        SELECT m.id, m.score_home, m.score_away,
+               m.ht_score_home, m.ht_score_away
+        FROM matches m
+        JOIN football_ai_features f ON f.match_id = m.id
+        WHERE m.status = 'FT'
+          AND m.sport = 'football'
+          AND m.score_home IS NOT NULL
+          AND m.score_away IS NOT NULL
+        ORDER BY m.mst_utc DESC
+        LIMIT %s
+    """, (limit,))
+    return cur.fetchall()
+
+
+def load_ai_features_batch(cur, match_ids: List[str]) -> Dict[str, Dict]:
+    if not match_ids:
+        return {}
+    ph = ",".join(["%s"] * len(match_ids))
+    cur.execute(f"""
+        SELECT match_id,
+               home_elo AS home_overall_elo,
+               away_elo AS away_overall_elo,
+               elo_diff,
+               home_home_elo, away_away_elo,
+               home_form_elo, away_form_elo,
+               (home_form_elo - away_form_elo) AS form_elo_diff,
+               home_goals_avg_5 AS home_goals_avg,
+               home_conceded_avg_5 AS home_conceded_avg,
+               away_goals_avg_5 AS away_goals_avg,
+               away_conceded_avg_5 AS away_conceded_avg,
+               home_clean_sheet_rate, away_clean_sheet_rate,
+               home_scoring_rate, away_scoring_rate,
+               home_win_streak AS home_winning_streak,
+               away_win_streak AS away_winning_streak,
+               0 AS home_unbeaten_streak,
+               0 AS away_unbeaten_streak,
+               h2h_total AS h2h_total_matches,
+               h2h_home_win_rate,
+               (1.0 - h2h_home_win_rate - 0.33) AS h2h_draw_rate,
+               h2h_avg_goals,
+               h2h_btts_rate, h2h_over25_rate,
+               home_avg_possession, away_avg_possession,
+               home_avg_shots_on_target, away_avg_shots_on_target,
+               home_shot_conversion, away_shot_conversion,
+               0.0 AS home_avg_corners, 0.0 AS away_avg_corners,
+               implied_home, implied_draw, implied_away,
+               league_avg_goals,
+               0.0 AS league_zero_goal_rate,
+               0.0 AS home_xga, 0.0 AS away_xga,
+               0.0 AS upset_atmosphere, 0.0 AS upset_motivation,
+               0.0 AS upset_fatigue, 0.0 AS upset_potential,
+               referee_home_bias, referee_avg_goals,
+               referee_avg_cards AS referee_cards_total,
+               0.0 AS referee_avg_yellow,
+               0.0 AS referee_experience,
+               0.0 AS home_momentum_score, 0.0 AS away_momentum_score,
+               0.0 AS momentum_diff,
+               0.0 AS home_squad_quality, 0.0 AS away_squad_quality,
+               0.0 AS squad_diff,
+               0 AS home_key_players, 0 AS away_key_players,
+               missing_players_impact AS home_missing_impact,
+               0.0 AS away_missing_impact,
+               home_goals_avg_5 AS home_goals_form,
+               away_goals_avg_5 AS away_goals_form
+        FROM football_ai_features
+        WHERE match_id IN ({ph})
+    """, match_ids)
+    return {str(row["match_id"]): dict(row) for row in cur.fetchall()}
+
+
+def load_odds_batch(cur, match_ids: List[str]) -> Dict[str, Dict[str, float]]:
+    if not match_ids:
+        return {}
+    ph = ",".join(["%s"] * len(match_ids))
+    cur.execute(f"""
+        SELECT oc.match_id, oc.name AS cat_name,
+               os.name AS sel_name, os.odd_value
+        FROM odd_selections os
+        JOIN odd_categories oc ON os.odd_category_db_id = oc.db_id
+        WHERE oc.match_id IN ({ph})
+    """, match_ids)
+
+    odds: Dict[str, Dict[str, float]] = {}
+    for row in cur.fetchall():
+        mid = str(row["match_id"])
+        cat = (row["cat_name"] or "").lower().strip()
+        sel = (row["sel_name"] or "").strip()
+        val = float(row["odd_value"]) if row["odd_value"] else 0
+        if val <= 0:
+            continue
+        if mid not in odds:
+            odds[mid] = {}
+
+        if cat in ODD_CAT_MAP:
+            key = ODD_CAT_MAP[cat].get(sel.lower())
+            if key:
+                odds[mid][key] = val
+        else:
+            for cat_pattern, kw_map in ODD_CAT_KEYWORD_MAP.items():
+                if cat == cat_pattern:
+                    for keyword, key in kw_map.items():
+                        if keyword in sel.lower():
+                            odds[mid][key] = val
+                    break
+    return odds
+
+
+MARKETS_TO_PREDICT = [
+    ("MS", "1", lambda p: p[0]),
+    ("MS", "X", lambda p: p[1]),
+    ("MS", "2", lambda p: p[2]),
+    ("OU25", "Over 2.5", lambda p: p[0]),
+    ("BTTS", "Yes", lambda p: p[0]),
+    ("OU15", "Over 1.5", lambda p: p[0]),
+    ("OU35", "Over 3.5", lambda p: p[0]),
+    ("HT", "1", lambda p: p[0]),
+    ("HT", "X", lambda p: p[1]),
+    ("HT", "2", lambda p: p[2]),
+]
+
+
+def run_backfill(args):
+    print("=" * 70)
+    print("CALIBRATION BACKFILL")
+    print("=" * 70)
+
+    conn = get_conn()
+    cur = conn.cursor(cursor_factory=RealDictCursor)
+
+    t0 = time.time()
+    print(f"Loading matches (limit={args.limit})...")
+    matches = load_matches(cur, args.limit)
+    print(f"  Found {len(matches)} finished matches with ai_features")
+
+    match_ids = [str(m["id"]) for m in matches]
+    match_map = {str(m["id"]): m for m in matches}
+
+    print("Loading ai_features...")
+    features_map = load_ai_features_batch(cur, match_ids)
+    print(f"  Loaded features for {len(features_map)} matches")
+
+    print("Loading odds...")
+    odds_map = load_odds_batch(cur, match_ids)
+    print(f"  Loaded odds for {len(odds_map)} matches")
+
+    print(f"Data loading: {time.time() - t0:.1f}s")
+
+    print("\nLoading V25 model...")
+    predictor = V25Predictor()
+    predictor.load_models()
+
+    feature_cols = predictor.FEATURE_COLS
+
+    samples: List[Dict[str, Any]] = []
+    skipped = 0
+    processed = 0
+
+    print(f"\nRunning predictions on {len(match_ids)} matches...")
+    t1 = time.time()
+
+    for i, mid in enumerate(match_ids):
+        if mid not in features_map:
+            skipped += 1
+            continue
+
+        feat_row = features_map[mid]
+        odds_row = odds_map.get(mid, {})
+        match_row = match_map[mid]
+
+        feat_dict = {}
+        for col in feature_cols:
+            if col in feat_row and feat_row[col] is not None:
+                feat_dict[col] = float(feat_row[col])
+            elif col.startswith("odds_") and not col.endswith("_present"):
+                odds_key = col.replace("odds_", "")
+                feat_dict[col] = float(odds_row.get(odds_key, 0))
+            elif col.endswith("_present"):
+                base = col.replace("_present", "")
+                odds_key = base.replace("odds_", "")
+                feat_dict[col] = 1.0 if odds_row.get(odds_key, 0) > 0 else 0.0
+            else:
+                feat_dict[col] = 0.0
+
+        if odds_row.get("ms_h", 0) > 0:
+            feat_dict["odds_ms_h"] = odds_row["ms_h"]
+        if odds_row.get("ms_d", 0) > 0:
+            feat_dict["odds_ms_d"] = odds_row["ms_d"]
+        if odds_row.get("ms_a", 0) > 0:
+            feat_dict["odds_ms_a"] = odds_row["ms_a"]
+
+        ms_h = feat_dict.get("odds_ms_h", 0)
+        ms_d = feat_dict.get("odds_ms_d", 0)
+        ms_a = feat_dict.get("odds_ms_a", 0)
+        if ms_h > 0 and ms_d > 0 and ms_a > 0:
+            raw_sum = 1/ms_h + 1/ms_d + 1/ms_a
+            feat_dict["implied_home"] = (1/ms_h) / raw_sum
+            feat_dict["implied_draw"] = (1/ms_d) / raw_sum
+            feat_dict["implied_away"] = (1/ms_a) / raw_sum
+
+        sh = match_row["score_home"]
+        sa = match_row["score_away"]
+        ht_h = match_row.get("ht_score_home")
+        ht_a = match_row.get("ht_score_away")
+
+        try:
+            X = pd.DataFrame([{c: feat_dict.get(c, 0.0) for c in feature_cols}])
+
+            for market_name, model_key, market_list in [
+                ("ms", "ms", ["MS"]),
+                ("ou25", "ou25", ["OU25"]),
+                ("btts", "btts", ["BTTS"]),
+                ("ou15", "ou15", ["OU15"]),
+                ("ou35", "ou35", ["OU35"]),
+                ("ht_result", "ht_result", ["HT"]),
+            ]:
+                if model_key not in predictor.models:
+                    continue
+
+                probs = predictor.predict_market(model_key, feat_dict)
+                if probs is None:
+                    continue
+
+                if model_key == "ms":
+                    for pick, prob in [("1", probs[0]), ("X", probs[1]), ("2", probs[2])]:
+                        actual = resolve_actual("MS", pick, sh, sa, ht_h, ht_a)
+                        key = calibrator_key("MS", pick)
+                        if actual is not None and key:
+                            samples.append({
+                                "match_id": mid,
+                                "market": "MS",
+                                "pick": pick,
+                                "key": key,
+                                "raw_prob": float(prob),
+                                "actual": int(actual),
+                            })
+
+                elif model_key == "ht_result":
+                    if ht_h is None or ht_a is None:
+                        continue
+                    for pick, prob in [("1", probs[0]), ("X", probs[1]), ("2", probs[2])]:
+                        actual = resolve_actual("HT", pick, sh, sa, ht_h, ht_a)
+                        key = calibrator_key("HT", pick)
+                        if actual is not None and key:
+                            samples.append({
+                                "match_id": mid,
+                                "market": "HT",
+                                "pick": pick,
+                                "key": key,
+                                "raw_prob": float(prob),
+                                "actual": int(actual),
+                            })
+
+                elif model_key in ("ou25", "ou15", "ou35"):
+                    market_upper = model_key.upper()
+                    over_prob = float(probs[0]) if len(probs) > 0 else 0.5
+                    pick = f"Over"
+                    actual = resolve_actual(market_upper, "Over", sh, sa, ht_h, ht_a)
+                    key = calibrator_key(market_upper, "Over")
+                    if actual is not None and key:
+                        samples.append({
+                            "match_id": mid,
+                            "market": market_upper,
+                            "pick": pick,
+                            "key": key,
+                            "raw_prob": over_prob,
+                            "actual": int(actual),
+                        })
+
+                elif model_key == "btts":
+                    yes_prob = float(probs[0]) if len(probs) > 0 else 0.5
+                    actual = resolve_actual("BTTS", "Yes", sh, sa, ht_h, ht_a)
+                    key = calibrator_key("BTTS", "Yes")
+                    if actual is not None and key:
+                        samples.append({
+                            "match_id": mid,
+                            "market": "BTTS",
+                            "pick": "Yes",
+                            "key": key,
+                            "raw_prob": yes_prob,
+                            "actual": int(actual),
+                        })
+
+            processed += 1
+
+        except Exception as e:
+            skipped += 1
+            if skipped <= 5:
+                print(f"  Error on {mid}: {e}")
+
+        if (i + 1) % 5000 == 0:
+            elapsed = time.time() - t1
+            rate = (i + 1) / elapsed
+            print(f"  Processed {i+1}/{len(match_ids)} ({rate:.0f} matches/s)")
+
+    elapsed = time.time() - t1
+    print(f"\nPrediction complete: {processed} matches, {skipped} skipped, {elapsed:.1f}s")
+
+    if not samples:
+        print("No calibration samples generated!")
+        cur.close()
+        conn.close()
+        return
+
+    df = pd.DataFrame(samples)
+    print(f"\nTotal calibration samples: {len(df)}")
+    print(f"Unique matches: {df['match_id'].nunique()}")
+    print(f"\nPer-key counts:")
+    for key, count in df["key"].value_counts().items():
+        print(f"  {key:<14} {count}")
+
+    print(f"\nTraining isotonic calibration models (min_samples={args.min_samples})...")
+    calibrator = get_calibrator()
+    results: Dict[str, Any] = {}
+    keys = sorted(df["key"].unique())
+
+    for key in keys:
+        sub = df[df["key"] == key].copy()
+        sub = sub.drop_duplicates(subset=["match_id", "key"], keep="first")
+        sub = sub.dropna(subset=["raw_prob", "actual"])
+        sub = sub[(sub["raw_prob"] > 0.0) & (sub["raw_prob"] < 1.0)]
+
+        n = len(sub)
+        if n < args.min_samples:
+            results[key] = {"status": "skipped", "samples": n}
+            continue
+
+        metrics = calibrator.train_calibration(
+            df=sub,
+            market=key,
+            prob_col="raw_prob",
+            actual_col="actual",
+            min_samples=args.min_samples,
+            save=True,
+        )
+        results[key] = {
+            "status": "trained",
+            "samples": metrics.sample_count,
+            "brier": round(metrics.brier_score, 4),
+            "ece": round(metrics.calibration_error, 4),
+            "mean_predicted": round(metrics.mean_predicted, 4),
+            "mean_actual": round(metrics.mean_actual, 4),
+        }
+
+    print("\n" + "=" * 70)
+    print("CALIBRATION RESULTS")
+    print("=" * 70)
+    print(f"{'market':<14} {'status':<10} {'n':<8} {'brier':<9} {'ece':<8} {'pred_avg':<9} {'actual_avg'}")
+    print("-" * 70)
+    for key, info in sorted(results.items()):
+        if info["status"] == "trained":
+            print(
+                f"{key:<14} {'OK':<10} {info['samples']:<8} "
+                f"{info['brier']:<9.4f} {info['ece']:<8.4f} "
+                f"{info['mean_predicted']:<9.4f} {info['mean_actual']}"
+            )
+        else:
+            print(f"{key:<14} {'SKIP':<10} {info['samples']:<8}")
+    print("=" * 70)
+
+    total_time = time.time() - t0
+    print(f"\nTotal time: {total_time:.1f}s")
+    print(f"Calibration models saved to: {os.path.join(AI_ENGINE_DIR, 'models', 'calibration')}/")
+
+    cur.close()
+    conn.close()
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Backfill calibration from historical matches")
+    parser.add_argument("--limit", type=int, default=50000,
+                        help="Max matches to process (default: 50000)")
+    parser.add_argument("--min-samples", type=int, default=100,
+                        help="Min samples per market for calibration (default: 100)")
+    args = parser.parse_args()
+    run_backfill(args)
+
+
+if __name__ == "__main__":
+    main()

ai-engine/scripts/backtest_consistency.py

@@ -0,0 +1,352 @@
+"""
+Tutarsızlık Bazlı Backtest
+============================
+Modeller arası tutarsızlığı ölçer, tutarlı maçlarda bahis açılsaydı
+ROI ne olurdu hesaplar.
+
+Mantık:
+- Her maç için market'ler arası çelişkileri tespit et
+- Tutarsız maçları filtrele
+- Tutarlı maçlarda hit rate ve ROI hesapla
+
+Usage:
+  python scripts/backtest_consistency.py
+"""
+
+import os, sys, json
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+from sklearn.metrics import accuracy_score
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+                         'data', 'training_data.csv')
+MODELS_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+                          'models', 'v25')
+
+SKIP_COLS = {
+    'match_id','home_team_id','away_team_id','league_id','mst_utc',
+    'score_home','score_away','total_goals','ht_score_home','ht_score_away','ht_total_goals',
+    'label_ms','label_ou05','label_ou15','label_ou25','label_ou35','label_btts',
+    'label_ht_result','label_ht_ou05','label_ht_ou15','label_ht_ft',
+    'label_odd_even','label_yellow_cards','label_cards_ou45','label_handicap_ms',
+}
+
+
+def load_model(market: str):
+    path = os.path.join(MODELS_DIR, f'xgb_v25_{market}.json')
+    if not os.path.exists(path):
+        return None
+    b = xgb.Booster()
+    b.load_model(path)
+    return b
+
+
+def predict_proba(model, X: np.ndarray, feature_cols: list, n_class: int):
+    dmat = xgb.DMatrix(pd.DataFrame(X, columns=feature_cols))
+    raw = model.predict(dmat)
+    if n_class > 2:
+        return raw.reshape(-1, n_class)
+    return np.column_stack([1 - raw, raw])
+
+
+def consistency_score(probs: dict) -> tuple[float, list]:
+    """
+    Market'ler arası tutarsızlığı hesapla.
+    0 = tamamen tutarlı, 1 = tamamen çelişkili.
+
+    Kontrol edilen çelişkiler:
+    1. OU15 üst yüksek ama OU25 üst de yüksek → ok
+       OU15 üst yüksek ama OU25 alt yüksek → ÇELISKI (1 gol bekleniyor ama 2.5+ da bekleniyor?)
+
+    2. HT_OU05 üst yüksek ama HT sonucu draw yüksek → ÇELISKI
+
+    3. OU35 üst yüksek ama BTTS düşük → şüpheli
+
+    4. MS home yüksek ama HT away yüksek → çelişkili
+    """
+    conflicts = []
+    total_weight = 0
+    total_conflict = 0
+
+    # OU tutarlılığı: P(OU25>0.5) <= P(OU15>0.5) matematiksel zorunluluk
+    ou15_over = probs.get('ou15_over', 0.5)
+    ou25_over = probs.get('ou25_over', 0.5)
+    ou35_over = probs.get('ou35_over', 0.5)
+
+    # OU hiyerarşisi: ou35 <= ou25 <= ou15 olmalı
+    if ou25_over > ou15_over + 0.05:
+        gap = ou25_over - ou15_over
+        conflicts.append(f'OU25>{ou25_over:.0%} > OU15>{ou15_over:.0%} (imkansız)')
+        total_conflict += gap * 2
+    total_weight += 1
+
+    if ou35_over > ou25_over + 0.05:
+        gap = ou35_over - ou25_over
+        conflicts.append(f'OU35>{ou35_over:.0%} > OU25>{ou25_over:.0%} (imkansız)')
+        total_conflict += gap * 2
+    total_weight += 1
+
+    # HT_OU05 ve HT sonuç tutarlılığı
+    ht_ou05_over = probs.get('ht_ou05_over', 0.5)
+    ht_draw_prob = probs.get('ht_draw', 0.34)
+
+    # İlk yarıda gol bekleniyor ama beraberlik de bekleniyor (0-0 draw?)
+    # HT_OU05 >%70 ama HT draw >%50 → çelişkili (0-0 berabere çok?)
+    if ht_ou05_over > 0.70 and ht_draw_prob > 0.50:
+        conflict = min(ht_ou05_over - 0.5, ht_draw_prob - 0.4)
+        conflicts.append(f'HT_OU05>{ht_ou05_over:.0%} ama HT_Draw>{ht_draw_prob:.0%}')
+        total_conflict += conflict
+    total_weight += 1
+
+    # HT_OU05 ve HT_OU15 tutarlılığı
+    ht_ou15_over = probs.get('ht_ou15_over', 0.3)
+    if ht_ou15_over > ht_ou05_over + 0.05:
+        gap = ht_ou15_over - ht_ou05_over
+        conflicts.append(f'HT_OU15>{ht_ou15_over:.0%} > HT_OU05>{ht_ou05_over:.0%} (imkansız)')
+        total_conflict += gap * 2
+    total_weight += 1
+
+    # MS ve OU tutarlılığı
+    ms_home = probs.get('ms_home', 0.33)
+    ms_away = probs.get('ms_away', 0.33)
+    btts_yes = probs.get('btts_yes', 0.5)
+
+    # Tek takım galibiyeti kuvvetli ama BTTS yüksek → şüpheli
+    dominant = max(ms_home, ms_away)
+    if dominant > 0.65 and btts_yes > 0.65:
+        conflict = (dominant - 0.5) * (btts_yes - 0.5)
+        conflicts.append(f'MS dominant>{dominant:.0%} ama BTTS_Yes>{btts_yes:.0%}')
+        total_conflict += conflict * 0.5
+    total_weight += 1
+
+    # OU25 ve BTTS tutarlılığı
+    # BTTS yüksekse en az 2 gol → OU25 üst de yüksek olmalı
+    if btts_yes > 0.65 and ou25_over < 0.45:
+        conflict = btts_yes - ou25_over
+        conflicts.append(f'BTTS_Yes>{btts_yes:.0%} ama OU25>{ou25_over:.0%} düşük')
+        total_conflict += conflict
+    total_weight += 1
+
+    # OU35 üst yüksek ama BTTS düşük → şüpheli (3+ gol ama tek takım mı?)
+    if ou35_over > 0.45 and btts_yes < 0.40:
+        conflict = (ou35_over - 0.35) * (0.5 - btts_yes)
+        conflicts.append(f'OU35>{ou35_over:.0%} ama BTTS_Yes<{btts_yes:.0%}')
+        total_conflict += conflict
+    total_weight += 1
+
+    score = min(1.0, total_conflict / max(total_weight * 0.3, 0.1))
+    return score, conflicts
+
+
+def main():
+    print('Loading data...')
+    df = pd.read_csv(DATA_PATH, low_memory=False)
+
+    # Son %20 = test seti (kronolojik)
+    df = df.sort_values('mst_utc')
+    n_test = int(len(df) * 0.20)
+    df_test = df.tail(n_test).copy()
+    print(f'Test seti: {len(df_test):,} maç')
+
+    feature_cols = [c for c in df.columns if c not in SKIP_COLS]
+
+    # Modelleri yükle
+    print('Modeller yükleniyor...')
+    models = {
+        'ms':       (load_model('ms'),        3),
+        'ou15':     (load_model('ou15'),       2),
+        'ou25':     (load_model('ou25'),       2),
+        'ou35':     (load_model('ou35'),       2),
+        'btts':     (load_model('btts'),       2),
+        'ht_result':(load_model('ht_result'),  3),
+        'ht_ou05':  (load_model('ht_ou05'),    2),
+        'ht_ou15':  (load_model('ht_ou15'),    2),
+    }
+    models = {k: v for k, v in models.items() if v[0] is not None}
+    print(f'Yüklenen model: {list(models.keys())}')
+
+    X = df_test[feature_cols].fillna(0).values
+
+    # Tüm tahminleri al
+    print('Tahminler yapılıyor...')
+    preds = {}
+    for mkey, (model, n_class) in models.items():
+        p = predict_proba(model, X, feature_cols, n_class)
+        preds[mkey] = p
+
+    # Her maç için tutarsızlık skoru ve tahmin kararı
+    results = []
+    for i in range(len(df_test)):
+        row = df_test.iloc[i]
+
+        # Olasılıkları topla
+        probs = {}
+        if 'ms' in preds:
+            probs['ms_home']    = preds['ms'][i][0]
+            probs['ms_draw']    = preds['ms'][i][1]
+            probs['ms_away']    = preds['ms'][i][2]
+        if 'ou15' in preds:
+            probs['ou15_over']  = preds['ou15'][i][1]
+        if 'ou25' in preds:
+            probs['ou25_over']  = preds['ou25'][i][1]
+        if 'ou35' in preds:
+            probs['ou35_over']  = preds['ou35'][i][1]
+        if 'btts' in preds:
+            probs['btts_yes']   = preds['btts'][i][1]
+        if 'ht_result' in preds:
+            probs['ht_home']    = preds['ht_result'][i][0]
+            probs['ht_draw']    = preds['ht_result'][i][1]
+            probs['ht_away']    = preds['ht_result'][i][2]
+        if 'ht_ou05' in preds:
+            probs['ht_ou05_over'] = preds['ht_ou05'][i][1]
+        if 'ht_ou15' in preds:
+            probs['ht_ou15_over'] = preds['ht_ou15'][i][1]
+
+        c_score, conflicts = consistency_score(probs)
+
+        # Gerçek sonuçlar
+        actual = {
+            'ms':    int(row.get('label_ms', -1)),
+            'ou15':  int(row.get('label_ou15', -1)),
+            'ou25':  int(row.get('label_ou25', -1)),
+            'ou35':  int(row.get('label_ou35', -1)),
+            'btts':  int(row.get('label_btts', -1)),
+        }
+
+        # Her market için tahmin ve doğruluk
+        market_results = {}
+        for mkt, label_key in [('ms','ms'),('ou15','ou15'),('ou25','ou25'),
+                                ('ou35','ou35'),('btts','btts')]:
+            if mkt not in preds or actual[label_key] < 0:
+                continue
+            pred_class = int(np.argmax(preds[mkt][i]))
+            correct = int(pred_class == actual[label_key])
+
+            # Odds (implied prob → odds = 1/prob)
+            pred_prob = float(preds[mkt][i][pred_class])
+            implied_odds = 1 / pred_prob if pred_prob > 0.01 else 10.0
+            # ROI hesabı: 1 birim bahis, kazanırsa (odds-1) kazanç, kaybederse -1
+            roi = (implied_odds - 1) * correct - (1 - correct)
+
+            market_results[mkt] = {
+                'pred': pred_class,
+                'actual': actual[label_key],
+                'correct': correct,
+                'prob': pred_prob,
+                'roi': roi,
+            }
+
+        results.append({
+            'idx': i,
+            'consistency_score': c_score,
+            'conflicts': conflicts,
+            'probs': probs,
+            'market_results': market_results,
+        })
+
+    df_results = pd.DataFrame([{
+        'consistency_score': r['consistency_score'],
+        'n_conflicts': len(r['conflicts']),
+        **{f'{m}_correct': r['market_results'].get(m, {}).get('correct', None)
+           for m in ['ms','ou15','ou25','ou35','btts']},
+        **{f'{m}_roi': r['market_results'].get(m, {}).get('roi', None)
+           for m in ['ms','ou15','ou25','ou35','btts']},
+    } for r in results])
+
+    # ── Analiz ──────────────────────────────────────────────────────────
+    print(f'\n{"="*70}')
+    print('TUTARSIZLIK ANALİZİ')
+    print(f'{"="*70}')
+
+    thresholds = [0.0, 0.1, 0.2, 0.3, 0.5]
+    markets = ['ms', 'ou15', 'ou25', 'ou35', 'btts']
+
+    for t in thresholds:
+        mask = df_results['consistency_score'] <= t
+        n = mask.sum()
+        if n < 50:
+            continue
+
+        print(f'\n[Tutarsızlık <= {t:.1f}] → {n:,} maç ({n/len(df_results)*100:.0f}%)')
+        print(f'  {"Market":<8} {"HitRate":>8} {"ROI/bahis":>10} {"Toplam ROI":>12}')
+        print(f'  {"-"*42}')
+        for m in markets:
+            col_c = f'{m}_correct'
+            col_r = f'{m}_roi'
+            if col_c not in df_results.columns:
+                continue
+            sub = df_results[mask][col_c].dropna()
+            roi_sub = df_results[mask][col_r].dropna()
+            if len(sub) < 20:
+                continue
+            hit = sub.mean()
+            avg_roi = roi_sub.mean()
+            total_roi = roi_sub.sum()
+            print(f'  {m:<8} {hit:>7.1%}  {avg_roi:>+9.3f}  {total_roi:>+11.1f}')
+
+    # Çelişki türlerine göre breakdown
+    print(f'\n{"="*70}')
+    print('EN SIK ÇELIŞKILER')
+    print(f'{"="*70}')
+    all_conflicts = [c for r in results for c in r['conflicts']]
+    from collections import Counter
+    for conflict, cnt in Counter(all_conflicts).most_common(10):
+        print(f'  {cnt:>5}x  {conflict}')
+
+    # Tutarsızlık dağılımı
+    print(f'\n{"="*70}')
+    print('TUTARSIZLIK DAĞILIMI')
+    print(f'{"="*70}')
+    for label, lo, hi in [
+        ('Tamamen tutarlı', 0.0, 0.05),
+        ('Çok tutarlı',     0.05, 0.15),
+        ('Orta',            0.15, 0.30),
+        ('Tutarsız',        0.30, 0.50),
+        ('Çok tutarsız',    0.50, 1.01),
+    ]:
+        mask = (df_results['consistency_score'] >= lo) & (df_results['consistency_score'] < hi)
+        n = mask.sum()
+        ou25_hit = df_results[mask]['ou25_correct'].mean()
+        ms_hit = df_results[mask]['ms_correct'].mean()
+        print(f'  {label:<20} {n:>6,} maç ({n/len(df_results)*100:>4.0f}%) | '
+              f'MS={ms_hit:.0%} OU25={ou25_hit:.0%}')
+
+    # Raporu kaydet
+    report = {
+        'total_test': len(df_results),
+        'thresholds': {},
+    }
+    for t in thresholds:
+        mask = df_results['consistency_score'] <= t
+        n = mask.sum()
+        report['thresholds'][str(t)] = {
+            'n_matches': int(n),
+            'pct': round(n/len(df_results)*100, 1),
+            'markets': {},
+        }
+        for m in markets:
+            col_c = f'{m}_correct'
+            col_r = f'{m}_roi'
+            if col_c not in df_results.columns:
+                continue
+            sub_c = df_results[mask][col_c].dropna()
+            sub_r = df_results[mask][col_r].dropna()
+            if len(sub_c) > 0:
+                report['thresholds'][str(t)]['markets'][m] = {
+                    'hit_rate': round(float(sub_c.mean()), 4),
+                    'avg_roi': round(float(sub_r.mean()), 4),
+                    'total_roi': round(float(sub_r.sum()), 2),
+                }
+
+    out_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+                            'reports', 'backtest_consistency.json')
+    with open(out_path, 'w') as f:
+        json.dump(report, f, indent=2)
+    print(f'\nRapor: {out_path}')
+
+
+if __name__ == '__main__':
+    main()

ai-engine/scripts/backtest_day.py

@@ -0,0 +1,146 @@
+import os
+import sys
+import psycopg2
+from psycopg2.extras import RealDictCursor
+
+# Path ayarları
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from services.single_match_orchestrator import SingleMatchOrchestrator
+from services.feature_enrichment import FeatureEnrichmentService
+
+DSN = "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
+
+def run_backtest(target_date="2026-05-03"):
+    conn = psycopg2.connect(DSN)
+    cur = conn.cursor(cursor_factory=RealDictCursor)
+    
+    # 1. Hedef tarihteki bitmiş maçları ve takım isimlerini getir
+    cur.execute("""
+        SELECT m.id, m.score_home, m.score_away, m.mst_utc,
+               t1.name as home_name, t2.name as away_name
+        FROM matches m
+        LEFT JOIN teams t1 ON m.home_team_id = t1.id
+        LEFT JOIN teams t2 ON m.away_team_id = t2.id
+        WHERE m.status IN ('FT', 'AET', 'PEN') 
+          AND to_timestamp(m.mst_utc / 1000.0)::date = %s::date
+          AND m.score_home IS NOT NULL
+        ORDER BY m.mst_utc ASC
+    """, (target_date,))
+    matches = cur.fetchall()
+    
+    if not matches:
+        print(f"❌ {target_date} tarihinde bitmiş maç bulunamadı.")
+        return
+
+    print(f"🚀 {target_date} için Orkestratör Backtesti Başlatılıyor... ({len(matches)} maç bulundu)")
+    print("-" * 60)
+    
+    orchestrator = SingleMatchOrchestrator()
+    
+    bets_placed = 0
+    won = 0
+    lost = 0
+    total_odds_won = 0.0
+    
+    for match in matches:
+        # 3. Üst Akıl (Orkestratör) analizi yapar
+        try:
+            package = orchestrator.analyze_match(match['id'])
+        except Exception as e:
+            print(f"Hata ({match['id']}): {e}")
+            continue
+            
+        if not package:
+            continue
+            
+        package_data = package
+        
+        # 4. Üst akıl bu maça bahis yapmaya karar verdi mi?
+        bet_advice = package_data.get("bet_advice", {})
+        if bet_advice.get("playable") == True:
+            bets_placed += 1
+            main_pick = package_data.get("main_pick", {})
+            market = main_pick.get("market")
+            pick = main_pick.get("pick")
+            odds = float(main_pick.get("odds", 0.0) or 0.0)
+            
+            # Skora göre kazanıp kazanmadığını kontrol et
+            is_won = False
+            h = match['score_home']
+            a = match['score_away']
+            
+            if market == "MS":
+                if pick == "1" and h > a: is_won = True
+                elif pick in ("X", "0") and h == a: is_won = True
+                elif pick == "2" and a > h: is_won = True
+            elif market == "OU25":
+                if pick == "Üst" and (h+a) > 2.5: is_won = True
+                elif pick == "Alt" and (h+a) < 2.5: is_won = True
+            elif market == "OU15":
+                if pick == "Üst" and (h+a) > 1.5: is_won = True
+                elif pick == "Alt" and (h+a) < 1.5: is_won = True
+            elif market == "BTTS":
+                if pick == "KG Var" and h > 0 and a > 0: is_won = True
+                elif pick == "KG Yok" and (h == 0 or a == 0): is_won = True
+            elif market == "DC":
+                if pick == "1X" and h >= a: is_won = True
+                elif pick == "12" and h != a: is_won = True
+                elif pick == "X2" and h <= a: is_won = True
+            
+            if is_won:
+                won += 1
+                total_odds_won += odds
+                res = "✅ KAZANDI"
+            else:
+                lost += 1
+                res = "❌ KAYBETTİ"
+                
+            print(f"[{res}] {match['home_name']} {h}-{a} {match['away_name']} | Tahmin: {market} {pick} (Oran: {odds})")
+        else:
+            main_pick = package_data.get("main_pick", {})
+            reasons = main_pick.get("reasons", ["Bilinmeyen Neden"]) if main_pick else ["No main pick"]
+            reason = " | ".join(reasons) if isinstance(reasons, list) else str(reasons)
+            
+            market_board = package_data.get("market_board", {})
+            main_pick_market = main_pick.get('market', 'N/A') if main_pick else 'N/A'
+            main_pick_pick = main_pick.get('pick', 'N/A') if main_pick else 'N/A'
+            print(f"[PAS] {match['home_name']} {match['score_home']}-{match['score_away']} {match['away_name']} | Reddedilen: {main_pick_market} {main_pick_pick} -> Neden: {reason}")
+            if "market_passed_all_gates" in reason:
+                print(f"      DEBUG: bet_advice = {bet_advice}")
+            
+            v25_ms = market_board.get("MS", {}).get("probs", {})
+            v27_ms = {} # V27 is merged into V25 probabilities in market_board, or we don't have separate V27 access here
+            
+            # Skora göre ms kontrolü
+            h = match['score_home']
+            a = match['score_away']
+            actual_ms = "1" if h > a else ("X" if h == a else "2")
+            
+            v25_top = max(v25_ms, key=v25_ms.get) if v25_ms else "N/A"
+            v27_top = "N/A"
+            
+            rejected_market = main_pick.get("market", "N/A") if main_pick else "N/A"
+            rejected_pick = main_pick.get("pick", "N/A") if main_pick else "N/A"
+            
+            print(f"[PAS] {match['home_name']} {h}-{a} {match['away_name']} | Reddedilen: {rejected_market} {rejected_pick} -> Neden: {reason}")
+            print(f"      [V25 MS Raw: {v25_top}] [Gerçek MS: {actual_ms}]")
+            
+    # Sonuç Raporu
+    print("\n" + "=" * 60)
+    print(f"📊 BACKTEST SONUÇLARI ({target_date})")
+    print("=" * 60)
+    print(f"Toplam Maç Sayısı   : {len(matches)}")
+    print(f"Oynanan Bahis Sayısı: {bets_placed} (Oynama Oranı: %{bets_placed/len(matches)*100:.1f})")
+    print(f"Riskli Bulunup Pas Geçilen: {len(matches) - bets_placed}")
+    
+    if bets_placed > 0:
+        win_rate = won / bets_placed * 100
+        roi = ((total_odds_won - bets_placed) / bets_placed) * 100
+        print(f"Kazanılan           : {won}")
+        print(f"Kaybedilen          : {lost}")
+        print(f"İsabet Oranı        : %{win_rate:.1f}")
+        print(f"Net Kar (ROI)       : %{roi:.1f} {'📈' if roi > 0 else '📉'}")
+
+if __name__ == "__main__":
+    run_backtest("2026-05-03")