gg

- 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 +45,13 @@ uploads/
 public/uploads/
 
 # Large Datasets and ML Models
-ai-engine/models/
-models/
+ai-engine/models/*
+!ai-engine/models/*.py
+!ai-engine/models/v25/
+!ai-engine/models/v27/
+!ai-engine/models/basketball_v25/
+!ai-engine/models/calibration/
+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"},
+"iterations":[
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+{"learn":[0.6896961461],"iteration":4,"passed_time":0.1969265393,"remaining_time":78.57368918,"test":[0.689812816]},
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ai-engine/catboost_info/time_left.tsv

@@ -0,0 +1,871 @@
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+823	26359	37619
+824	26385	37580
+825	26408	37534
+826	26444	37507
+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/basketball_v25/__init__.py

@@ -0,0 +1,356 @@
+"""
+Basketball V25 Predictor Package
+=================================
+Loads trained XGBoost + LightGBM models for basketball market predictions:
+  - ML  (Money Line  — home / away win)
+  - Total (Over/Under total points)
+  - Spread (ATS home cover / away cover)
+
+Model files live in this directory:
+  xgb_basketball_v25_{market}.json  — XGBoost (primary)
+  lgb_basketball_v25_{market}.txt   — LightGBM (ensemble)
+  feature_cols.json                 — ordered feature list
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+# ── Constants ─────────────────────────────────────────────────────────────────
+_DIR = os.path.dirname(os.path.abspath(__file__))
+_MARKETS = ("ml", "total", "spread")
+
+
+# ── Output dataclass ──────────────────────────────────────────────────────────
+
+@dataclass
+class BasketballMatchPrediction:
+    """Complete basketball match prediction output."""
+
+    match_id: str
+    home_team_name: str
+    away_team_name: str
+    league_name: str = ""
+
+    # Money Line
+    ml_home_prob: float = 0.50
+    ml_away_prob: float = 0.50
+    ml_pick: str = ""
+    ml_confidence: float = 0.0
+
+    # Total (Over/Under)
+    total_line: float = 0.0
+    total_over_prob: float = 0.50
+    total_under_prob: float = 0.50
+    total_pick: str = ""
+    total_confidence: float = 0.0
+
+    # Spread (ATS)
+    spread_home_line: float = 0.0
+    spread_home_prob: float = 0.50
+    spread_away_prob: float = 0.50
+    spread_pick: str = ""
+    spread_confidence: float = 0.0
+
+    # Meta
+    model_version: str = "basketball_v25"
+    risk_level: str = "MEDIUM"
+    analysis_details: Dict[str, Any] = field(default_factory=dict)
+    market_board: Dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "match_id": self.match_id,
+            "home_team": self.home_team_name,
+            "away_team": self.away_team_name,
+            "league": self.league_name,
+            "model": self.model_version,
+            "risk_level": self.risk_level,
+            "ml": {
+                "home_prob": round(self.ml_home_prob * 100, 1),
+                "away_prob": round(self.ml_away_prob * 100, 1),
+                "pick": self.ml_pick,
+                "confidence": round(self.ml_confidence, 1),
+            },
+            "total": {
+                "line": self.total_line,
+                "over_prob": round(self.total_over_prob * 100, 1),
+                "under_prob": round(self.total_under_prob * 100, 1),
+                "pick": self.total_pick,
+                "confidence": round(self.total_confidence, 1),
+            },
+            "spread": {
+                "home_line": self.spread_home_line,
+                "home_prob": round(self.spread_home_prob * 100, 1),
+                "away_prob": round(self.spread_away_prob * 100, 1),
+                "pick": self.spread_pick,
+                "confidence": round(self.spread_confidence, 1),
+            },
+            "market_board": self.market_board,
+            "analysis_details": self.analysis_details,
+        }
+
+
+# ── Predictor ─────────────────────────────────────────────────────────────────
+
+class BasketballV25Predictor:
+    """
+    Ensemble basketball predictor using XGBoost + LightGBM models.
+
+    Markets:
+      - ml     → home/away win probability
+      - total  → over/under total points
+      - spread → home/away ATS cover
+    """
+
+    def __init__(self) -> None:
+        self.feature_cols: List[str] = self._load_feature_cols()
+        self.models: Dict[str, Any] = {}
+        self._load_models()
+        print(f"✅ BasketballV25Predictor ready ({len(self.models)} models loaded)")
+
+    # ── Setup ──────────────────────────────────────────────────────────────
+
+    def _load_feature_cols(self) -> List[str]:
+        path = os.path.join(_DIR, "feature_cols.json")
+        try:
+            with open(path, "r") as f:
+                return json.load(f)
+        except Exception as e:
+            print(f"⚠️  [Basketball] Could not load feature_cols.json: {e}")
+            return []
+
+    def _load_models(self) -> None:
+        for market in _MARKETS:
+            xgb_path = os.path.join(_DIR, f"xgb_basketball_v25_{market}.json")
+            lgb_path = os.path.join(_DIR, f"lgb_basketball_v25_{market}.txt")
+
+            xgb_model = self._try_load_xgb(xgb_path, market)
+            lgb_model = self._try_load_lgb(lgb_path, market)
+
+            if xgb_model is not None or lgb_model is not None:
+                self.models[market] = {"xgb": xgb_model, "lgb": lgb_model}
+
+    def _try_load_xgb(self, path: str, market: str) -> Optional[Any]:
+        if not os.path.exists(path):
+            return None
+        try:
+            import xgboost as xgb  # type: ignore[import-not-found]
+            m = xgb.XGBClassifier()
+            m.load_model(path)
+            return m
+        except Exception as e:
+            print(f"⚠️  [Basketball] XGB {market} load failed: {e}")
+            return None
+
+    def _try_load_lgb(self, path: str, market: str) -> Optional[Any]:
+        if not os.path.exists(path):
+            return None
+        try:
+            import lightgbm as lgb  # type: ignore[import-not-found]
+            with open(path, "r", encoding="utf-8") as f:
+                model_str = f.read()
+            return lgb.Booster(model_str=model_str)
+        except Exception as e:
+            print(f"⚠️  [Basketball] LGB {market} load failed: {e}")
+            return None
+
+    # ── Inference ──────────────────────────────────────────────────────────
+
+    def _build_feature_row(self, odds_data: Dict[str, Any], **kwargs: Any) -> "Any":
+        """Build a single-row DataFrame aligned to training feature columns."""
+        import pandas as pd  # type: ignore[import-not-found]
+
+        row: Dict[str, float] = {}
+        for col in self.feature_cols:
+            row[col] = float(kwargs.get(col) or odds_data.get(col) or 0.0)
+
+        # Map common odds keys
+        row["ml_home_odds"] = float(odds_data.get("ml_h") or 0.0)
+        row["ml_away_odds"] = float(odds_data.get("ml_a") or 0.0)
+        row["total_line"] = float(odds_data.get("tot_line") or 0.0)
+        row["total_over_odds"] = float(odds_data.get("tot_o") or 0.0)
+        row["total_under_odds"] = float(odds_data.get("tot_u") or 0.0)
+        row["spread_home_line"] = float(odds_data.get("spread_home_line") or 0.0)
+        row["spread_home_odds"] = float(odds_data.get("spread_h") or 0.0)
+        row["spread_away_odds"] = float(odds_data.get("spread_a") or 0.0)
+
+        # Implied probabilities
+        def _imp(odd: float) -> float:
+            return (1.0 / odd) if odd > 1.01 else 0.5
+
+        ml_h = row["ml_home_odds"]
+        ml_a = row["ml_away_odds"]
+        if ml_h > 1.01 and ml_a > 1.01:
+            raw = _imp(ml_h) + _imp(ml_a)
+            row["implied_home"] = _imp(ml_h) / raw
+            row["implied_away"] = _imp(ml_a) / raw
+            row["odds_overround"] = raw - 1.0
+
+        tot_o = row["total_over_odds"]
+        tot_u = row["total_under_odds"]
+        if tot_o > 1.01 and tot_u > 1.01:
+            raw = _imp(tot_o) + _imp(tot_u)
+            row["implied_total_over"] = _imp(tot_o) / raw
+            row["implied_total_under"] = _imp(tot_u) / raw
+
+        sp_h = row["spread_home_odds"]
+        sp_a = row["spread_away_odds"]
+        if sp_h > 1.01 and sp_a > 1.01:
+            raw = _imp(sp_h) + _imp(sp_a)
+            row["implied_spread_home"] = _imp(sp_h) / raw
+            row["implied_spread_away"] = _imp(sp_a) / raw
+
+        return pd.DataFrame([row])
+
+    def _ensemble_predict(self, market: str, df: "Any") -> List[float]:
+        """Return [p_class0, p_class1] from XGB+LGB ensemble."""
+        models = self.models.get(market, {})
+        xgb_model = models.get("xgb")
+        lgb_model = models.get("lgb")
+
+        probs_list: List[List[float]] = []
+
+        if xgb_model is not None:
+            try:
+                p = xgb_model.predict_proba(df)
+                probs_list.append([float(p[0][0]), float(p[0][1])])
+            except Exception as e:
+                print(f"⚠️  [Basketball] XGB {market} inference failed: {e}")
+
+        if lgb_model is not None:
+            try:
+                p_raw = lgb_model.predict(df)
+                p1 = float(p_raw[0]) if len(p_raw.shape) == 1 else float(p_raw[0][1])
+                probs_list.append([1.0 - p1, p1])
+            except Exception as e:
+                print(f"⚠️  [Basketball] LGB {market} inference failed: {e}")
+
+        if not probs_list:
+            return [0.5, 0.5]
+
+        p0 = sum(p[0] for p in probs_list) / len(probs_list)
+        p1 = sum(p[1] for p in probs_list) / len(probs_list)
+        total = p0 + p1 or 1.0
+        return [p0 / total, p1 / total]
+
+    # ── Public API ─────────────────────────────────────────────────────────
+
+    def predict(
+        self,
+        match_id: str,
+        home_team_id: str,
+        away_team_id: str,
+        home_team_name: str = "",
+        away_team_name: str = "",
+        league_id: str = "",
+        league_name: str = "",
+        odds_data: Optional[Dict[str, Any]] = None,
+        sidelined_data: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> BasketballMatchPrediction:
+        odds = odds_data or {}
+        prediction = BasketballMatchPrediction(
+            match_id=match_id,
+            home_team_name=home_team_name,
+            away_team_name=away_team_name,
+            league_name=league_name,
+        )
+
+        # Sidelined impact
+        home_sl = int((sidelined_data or {}).get("homeTeam", {}).get("totalSidelined", 0) or 0)
+        away_sl = int((sidelined_data or {}).get("awayTeam", {}).get("totalSidelined", 0) or 0)
+        kwargs.setdefault("home_sidelined_count", float(home_sl))
+        kwargs.setdefault("away_sidelined_count", float(away_sl))
+        kwargs.setdefault("sidelined_diff", float(home_sl - away_sl))
+        kwargs.setdefault("missing_players_impact", float(home_sl + away_sl) / 10.0)
+
+        if not self.models:
+            print("⚠️  [Basketball] No models loaded — returning neutral defaults")
+            return prediction
+
+        try:
+            df = self._build_feature_row(odds, **kwargs)
+
+            # ── ML ──
+            ml_probs = self._ensemble_predict("ml", df)
+            prediction.ml_home_prob = ml_probs[0]
+            prediction.ml_away_prob = ml_probs[1]
+            prediction.ml_pick = "1" if ml_probs[0] >= ml_probs[1] else "2"
+            prediction.ml_confidence = max(ml_probs) * 100.0
+
+            # ── Total ──
+            prediction.total_line = float(odds.get("tot_line") or 0.0)
+            tot_probs = self._ensemble_predict("total", df)
+            prediction.total_over_prob = tot_probs[1]
+            prediction.total_under_prob = tot_probs[0]
+            total_line = prediction.total_line
+            prediction.total_pick = (
+                f"Over {total_line}" if tot_probs[1] >= tot_probs[0] else f"Under {total_line}"
+            )
+            prediction.total_confidence = max(tot_probs) * 100.0
+
+            # ── Spread ──
+            prediction.spread_home_line = float(odds.get("spread_home_line") or 0.0)
+            sp_probs = self._ensemble_predict("spread", df)
+            prediction.spread_home_prob = sp_probs[0]
+            prediction.spread_away_prob = sp_probs[1]
+            home_line = prediction.spread_home_line
+            away_line = -home_line
+            prediction.spread_pick = (
+                f"Home {home_line:+.1f}" if sp_probs[0] >= sp_probs[1] else f"Away {away_line:+.1f}"
+            )
+            prediction.spread_confidence = max(sp_probs) * 100.0
+
+            # Market board summary
+            prediction.market_board = {
+                "ML": {
+                    "1": f"{prediction.ml_home_prob * 100:.0f}%",
+                    "2": f"{prediction.ml_away_prob * 100:.0f}%",
+                },
+                "Totals": {
+                    f"Over {total_line}": f"{prediction.total_over_prob * 100:.0f}%",
+                    f"Under {total_line}": f"{prediction.total_under_prob * 100:.0f}%",
+                },
+                "Spread": {
+                    f"Home {home_line:+.1f}": f"{prediction.spread_home_prob * 100:.0f}%",
+                    f"Away {away_line:+.1f}": f"{prediction.spread_away_prob * 100:.0f}%",
+                },
+            }
+
+            # Risk
+            top_conf = max(prediction.ml_confidence, prediction.total_confidence, prediction.spread_confidence)
+            prediction.risk_level = "LOW" if top_conf >= 65 else "MEDIUM" if top_conf >= 55 else "HIGH"
+            prediction.analysis_details = {
+                "model_version": "basketball_v25",
+                "markets_predicted": list(self.models.keys()),
+                "ensemble_size": {m: sum(1 for k in v.values() if v[k] is not None) for m, v in self.models.items()},
+            }
+
+        except Exception as e:
+            print(f"⚠️  [Basketball] Prediction failed for {match_id}: {e}")
+
+        return prediction
+
+
+# ── Singleton factory ──────────────────────────────────────────────────────────
+
+_predictor: Optional[BasketballV25Predictor] = None
+
+
+def get_basketball_v25_predictor() -> BasketballV25Predictor:
+    """Return the singleton BasketballV25Predictor (lazy-loaded)."""
+    global _predictor
+    if _predictor is None:
+        _predictor = BasketballV25Predictor()
+    return _predictor
+
+
+__all__ = [
+    "BasketballMatchPrediction",
+    "BasketballV25Predictor",
+    "get_basketball_v25_predictor",
+]

ai-engine/models/basketball_v25/feature_cols.json

@@ -0,0 +1,101 @@
+[
+  "home_overall_elo",
+  "away_overall_elo",
+  "elo_diff",
+  "home_home_elo",
+  "away_away_elo",
+  "home_form_elo",
+  "away_form_elo",
+  "home_form_score",
+  "away_form_score",
+  "form_score_diff",
+  "home_points_avg",
+  "away_points_avg",
+  "points_avg_diff",
+  "home_conceded_avg",
+  "away_conceded_avg",
+  "conceded_avg_diff",
+  "home_net_rating",
+  "away_net_rating",
+  "net_rating_diff",
+  "home_win_rate",
+  "away_win_rate",
+  "win_rate_diff",
+  "home_winning_streak",
+  "away_winning_streak",
+  "streak_diff",
+  "home_rest_days",
+  "away_rest_days",
+  "rest_diff",
+  "home_rebounds_avg",
+  "away_rebounds_avg",
+  "rebounds_diff",
+  "home_assists_avg",
+  "away_assists_avg",
+  "assists_diff",
+  "home_steals_avg",
+  "away_steals_avg",
+  "steals_diff",
+  "home_blocks_avg",
+  "away_blocks_avg",
+  "blocks_diff",
+  "home_turnovers_avg",
+  "away_turnovers_avg",
+  "turnovers_diff",
+  "home_fg_pct",
+  "away_fg_pct",
+  "fg_pct_diff",
+  "home_three_pt_pct",
+  "away_three_pt_pct",
+  "three_pt_pct_diff",
+  "home_ft_pct",
+  "away_ft_pct",
+  "ft_pct_diff",
+  "home_q1_avg",
+  "away_q1_avg",
+  "home_q4_avg",
+  "away_q4_avg",
+  "home_conc_rebounds_avg",
+  "away_conc_rebounds_avg",
+  "home_conc_assists_avg",
+  "away_conc_assists_avg",
+  "home_conc_turnovers_avg",
+  "away_conc_turnovers_avg",
+  "home_conc_fg_pct",
+  "away_conc_fg_pct",
+  "home_conc_three_pt_pct",
+  "away_conc_three_pt_pct",
+  "h2h_total_matches",
+  "h2h_home_win_rate",
+  "h2h_avg_points",
+  "h2h_avg_margin",
+  "h2h_over_total_rate",
+  "h2h_home_cover_rate",
+  "league_avg_points",
+  "league_home_win_rate",
+  "league_over_total_rate",
+  "league_home_cover_rate",
+  "ml_home_odds",
+  "ml_away_odds",
+  "implied_home",
+  "implied_away",
+  "total_line",
+  "total_over_odds",
+  "total_under_odds",
+  "implied_total_over",
+  "implied_total_under",
+  "spread_home_line",
+  "spread_home_odds",
+  "spread_away_odds",
+  "implied_spread_home",
+  "implied_spread_away",
+  "odds_overround",
+  "home_sidelined_count",
+  "away_sidelined_count",
+  "sidelined_diff",
+  "missing_players_impact",
+  "total_points_form",
+  "total_points_allowed_form",
+  "projected_total_delta_vs_line",
+  "projected_margin_vs_spread"
+]

ai-engine/models/basketball_v25/lgb_basketball_v25_ml.txt

@@ -0,0 +1,655 @@
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+
+Tree=21
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+
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+
+end of trees
+
+feature_importances:
+Column_77=25
+Column_46=15
+Column_76=15
+Column_97=14
+Column_43=13
+Column_50=13
+Column_49=12
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+Column_79=1
+Column_89=1
+Column_96=1
+
+parameters:
+[boosting: gbdt]
+[objective: binary]
+[metric: binary_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1200]
+[learning_rate: 0.04]
+[num_leaves: 31]
+[num_threads: 4]
+[seed: 42]
+[deterministic: 0]
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+[max_depth: 6]
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+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.84]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 5]
+[bagging_seed: 400]
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+[feature_fraction: 0.82]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 30056]
+[extra_trees: 0]
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+[early_stopping_round: 0]
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+[lambda_l1: 0]
+[lambda_l2: 0]
+[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/basketball_v25/lgb_basketball_v25_spread.txt

@@ -0,0 +1,790 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=98
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+
+Tree=27
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+right_child=-2 2 4 13 5 6 -8 10 -10 -11 11 -13 -14 -15
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+internal_weight=248.305 238.251 218.028 20.223 48.8396 41.8502 35.4674 169.188 132.79 122.118 36.3988 23.7915 12.6073 12.1006
+internal_count=1026 983 899 84 200 171 145 699 549 505 150 98 52 50
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+
+
+Tree=28
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+split_gain=6.19142 5.48502 8.27407 6.44704 5.11208 5.11786 5.55155 5.07182 5.97421 8.76085 8.56983 3.46147
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2
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+internal_weight=247.982 237.997 40.4362 197.561 25.6077 17.9652 12.384 186.194 177.699 102.892 74.8072 14.8285
+internal_count=1026 983 169 814 108 76 52 767 732 423 309 61
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+
+
+Tree=29
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+split_feature=87 85 10 83 41 73 86 39 8 44 13 76 61 36 97 45
+split_gain=5.37924 8.77104 7.1461 6.5763 6.24618 5.74552 7.24104 5.90731 7.82727 5.90939 5.68942 4.92086 12.7968 7.49136 4.69159 2.29423
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=10 4 3 11 -2 6 -6 8 -7 -9 -1 13 -13 -3 -12 -5
+right_child=1 2 -4 15 5 7 -8 9 -10 -11 14 12 -14 -15 -16 -17
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+internal_weight=247.661 225.74 68.9738 60.2328 156.766 146.234 17.3512 128.883 40.2129 88.6698 21.9211 47.8918 13.927 33.9648 11.7239 12.3411
+internal_count=1026 935 284 248 651 607 72 535 167 368 91 197 57 140 48 51
+is_linear=0
+shrinkage=0.04
+
+
+end of trees
+
+feature_importances:
+Column_33=16
+Column_55=14
+Column_75=13
+Column_95=13
+Column_17=12
+Column_27=12
+Column_40=12
+Column_60=12
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+Column_97=10
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+Column_53=9
+Column_64=9
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+Column_54=8
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+Column_26=7
+Column_29=7
+Column_39=7
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+Column_63=7
+Column_9=6
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+Column_96=2
+Column_7=1
+Column_21=1
+Column_44=1
+Column_66=1
+Column_68=1
+Column_71=1
+
+parameters:
+[boosting: gbdt]
+[objective: binary]
+[metric: binary_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1200]
+[learning_rate: 0.04]
+[num_leaves: 31]
+[num_threads: 4]
+[seed: 42]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 6]
+[min_data_in_leaf: 24]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.84]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 5]
+[bagging_seed: 400]
+[bagging_by_query: 0]
+[feature_fraction: 0.82]
+[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: 0]
+[lambda_l2: 0]
+[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/basketball_v25/lgb_basketball_v25_total.txt

@@ -0,0 +1,202 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=98
+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
+feature_infos=none none none none none none none [0:100] [0:100] [-100:100] [54:128.875] [61:138] [-41.333333333333329:24] [61:130.125] [59:133] [-50:34.916666666666671] [-28.666666666666657:32.5] [-24.5:27] [-35.5:31.833333333333329] [0:1] [0:1] [-1:1] [0:12] [0:12] [-12:10] [0.91666666666666663:186.1875] [0.85416666666666663:185.08333333333337] [-179.02083333333334:184.16666666666663] [20:54] [22:56] [-21:15.833333333333332] [11:34.5] [9:36] [-15:13.333333333333332] [2:13] [3:13] [-8:5.5] [0.875:9] [0:12] [-9:4.875] [7:22] [8:23] [-10:10] [0.41025641025641019:0.67647058823529416] [0.39730639730639727:0.76744186046511631] [-0.3174418604651163:0.22647058823529409] [0.20419254658385089:0.4825000000000001] [0.1875:0.54166666666666663] [-0.22171945701357459:0.17923280423280419] [0.47826086956521741:0.87898457080200498] [0.42857142857142849:0.88636363636363635] [-0.29647435897435898:0.38962801311591622] [11:39.666666666666664] [15.5:40] [10:31.875] [11.5:34] [15:56] [17:57] [12:32.125] [10:38] [8:20] [7:22] [0.40000000000000002:0.69047619047619047] [0.3902439024390244:0.71739130434782605] [0.23753976670201479:0.51851851851851849] [0.1707317073170731:0.54545454545454541] [0:8] [0:1] [120:295] [-38:36] [0:1] [0:1] [140.39130434782609:233.09999999999999] [0:1] [0:1] [0:1] [1.05:4.1100000000000003] [1.05:4.1299999999999999] [0.2034883720930232:0.79729729729729726] [0.20270270270270269:0.79651162790697683] [145.5:257.5] [1.3:7.7599999999999998] [1.4099999999999999:7.5599999999999996] [0.1789976133651551:0.75977653631284914] [0.24022346368715081:0.82100238663484482] [-15.5:12.5] [1.3:2.7200000000000002] [1.1799999999999999:2.7799999999999998] [0.36298076923076922:0.64953271028037374] [0.35046728971962621:0.63701923076923073] [0.1918833727344364:0.21555133935749429] none none none none [132:250.6875] [126.25:256.625] [-63.5:88.6875] [-35.5:27.5]
+tree_sizes=1714 1917
+
+Tree=0
+num_leaves=15
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+split_feature=72 82 49 25 36 25 17 77 34 69 25 40 80 40
+split_gain=11.2305 10.9895 14.5557 11.7291 11.5678 6.991 6.42947 6.52716 10.0318 9.34909 5.86638 5.89396 5.34606 8.20903
+threshold=232.51500000000001 1.8250000000000004 0.74446554478882077 2.8645833333333335 -0.46874999999999994 1.0104166666666667 12.562500000000002 2.6750000000000003 5.6339285714285721 -5.4166666666666652 3.1145833333333335 12.803571428571431 162.00000000000003 13.937500000000002
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 5 10 4 -4 -1 7 8 -7 -9 -3 -12 -5 -14
+right_child=-2 2 3 12 -6 6 -8 9 -10 -11 11 -13 13 -15
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+internal_value=-0.0483827 -0.0469209 -0.0344467 -0.0240408 -0.0513359 -0.0525288 -0.054236 -0.052269 -0.0474518 -0.0661521 -0.063157 -0.0771246 -0.0120241 -0.0192454
+internal_weight=259.096 251.351 77.9538 57.2161 17.4896 173.397 165.902 156.157 115.931 40.2261 20.7377 14.4914 39.7264 31.981
+internal_count=1037 1006 312 229 70 694 664 625 464 161 83 58 159 128
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=17
+num_cat=0
+split_feature=52 63 82 49 63 11 9 22 47 27 26 50 60 68 37 64
+split_gain=9.46149 12.0286 10.1725 11.5657 11.422 11.4943 11.2254 9.35335 10.3758 7.46163 7.15253 10.8161 5.87382 5.6308 5.10807 3.94362
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=15 2 4 9 5 -2 -7 8 -6 13 11 -5 -12 -4 -9 -1
+right_child=1 -3 3 10 7 6 -8 14 -10 -11 12 -13 -14 -15 -16 -17
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+leaf_count=24 39 34 29 169 126 232 71 37 50 37 26 39 47 29 24 24
+internal_value=0.000314777 0.00199863 0.0036638 0.0139129 -0.00299273 -0.0123505 -0.0176148 0.01051 0.00115015 -0.010233 0.0220716 0.0145076 0.0436201 0.00767928 0.0375162 -0.0343904
+internal_weight=259.049 247.061 238.57 93.9359 144.634 85.4291 75.6859 59.2045 43.9665 23.724 70.2119 51.9696 18.2423 14.4872 15.238 11.9871
+internal_count=1037 989 955 376 579 342 303 237 176 95 281 208 73 58 61 48
+is_linear=0
+shrinkage=0.04
+
+
+end of trees
+
+feature_importances:
+Column_25=3
+Column_40=2
+Column_49=2
+Column_63=2
+Column_82=2
+Column_9=1
+Column_11=1
+Column_17=1
+Column_22=1
+Column_26=1
+Column_27=1
+Column_34=1
+Column_36=1
+Column_37=1
+Column_47=1
+Column_50=1
+Column_52=1
+Column_60=1
+Column_64=1
+Column_68=1
+Column_69=1
+Column_72=1
+Column_77=1
+Column_80=1
+
+parameters:
+[boosting: gbdt]
+[objective: binary]
+[metric: binary_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1200]
+[learning_rate: 0.04]
+[num_leaves: 31]
+[num_threads: 4]
+[seed: 42]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 6]
+[min_data_in_leaf: 24]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.84]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 5]
+[bagging_seed: 400]
+[bagging_by_query: 0]
+[feature_fraction: 0.82]
+[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: 0]
+[lambda_l2: 0]
+[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/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/calibration/dc_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.1264,
+  "calibration_error": 0.0,
+  "sample_count": 26,
+  "last_trained": "2026-05-11T23:38:11.473645",
+  "mean_predicted": 0.757,
+  "mean_actual": 0.6923
+}

ai-engine/models/calibration/ht_ft_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.1667,
+  "calibration_error": 0.0,
+  "sample_count": 12,
+  "last_trained": "2026-05-11T23:38:11.484990",
+  "mean_predicted": 0.3579,
+  "mean_actual": 0.25
+}

ai-engine/models/calibration/ou15_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.196,
+  "calibration_error": 0.0,
+  "sample_count": 13,
+  "last_trained": "2026-05-11T23:38:11.500966",
+  "mean_predicted": 0.7239,
+  "mean_actual": 0.6154
+}

ai-engine/models/calibration/ou25_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.2222,
+  "calibration_error": 0.0,
+  "sample_count": 18,
+  "last_trained": "2026-05-11T23:38:11.508353",
+  "mean_predicted": 0.6258,
+  "mean_actual": 0.5
+}

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=0
+num_leaves=8
+num_cat=0
+split_feature=45 54 46 96 4 3 16
+split_gain=174.584 35.5641 24.9346 16.8552 14.3832 11.0732 10.8094
+threshold=1.155 1.405 2.5850000000000004 4.5000000000000009 1704.4687685416313 1441.3975280143418 1.0000000180025095e-35
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+internal_count=13397 9195 4202 6805 2064 2138 2390
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=8
+num_cat=0
+split_feature=47 48 49 86 141 133 150
+split_gain=140.072 28.5634 26.9673 23.4898 19.0464 14.6224 10.7313
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+internal_weight=2780.74 1744.41 1036.34 1371.24 532.645 503.692 373.168
+internal_count=13397 8468 4929 6640 2516 2413 1828
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=2
+num_leaves=8
+num_cat=0
+split_feature=45 50 53 39 96 28 150
+split_gain=110.988 23.3948 18.1465 13.3271 12.0676 11.8858 11.7377
+threshold=1.155 1.4150000000000003 3.3250000000000006 0.36187197152743827 4.5000000000000009 3.9500000000000006 0.48538789856891795
+decision_type=2 2 2 2 2 2 2
+left_child=1 4 5 -4 -1 -2 -3
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+leaf_weight=991.85861267149448 395.00907251238823 20.026126876473427 106.85419258475304 22.895305529236794 522.50144763290882 372.75306884944439 348.38899676501751
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+[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