gg

.gitea/workflows/deploy.yml

@@ -25,11 +25,11 @@ 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 DATABASE_URL='${{ secrets.DATABASE_URL }}' \
-            -e REDIS_HOST='iddaai-redis' \
+            -e REDIS_HOST='${{ secrets.REDIS_HOST }}' \
-            -e REDIS_PORT='6379' \
+            -e REDIS_PORT='${{ secrets.REDIS_PORT }}' \
-            -e REDIS_PASSWORD='IddaA1_Redis_Pass!' \
+            -e REDIS_PASSWORD='${{ secrets.REDIS_PASSWORD }}' \
-            -e AI_ENGINE_URL='http://iddaai-ai-engine:8000' \
+            -e AI_ENGINE_URL='${{ secrets.AI_ENGINE_URL }}' \
-            -e JWT_SECRET='b7V8jM2wP1L5mQxs2RdfFkAsLpI2oG!w' \
+            -e JWT_SECRET='${{ secrets.JWT_SECRET }}' \
             -e JWT_ACCESS_EXPIRATION='1d' \
             iddaai-be:latest /bin/sh -c "npx prisma migrate deploy && node dist/src/main.js"

ai-engine/config/config_loader.py

@@ -1,17 +1,19 @@
 import os
+import json
 import yaml
 from typing import Dict, Any, Optional
 
+
 class EnsembleConfig:
     _instance: Optional['EnsembleConfig'] = None
     _config: Dict[str, Any] = {}
-    
+
     def __new__(cls):
         if cls._instance is None:
             cls._instance = super(EnsembleConfig, cls).__new__(cls)
             cls._instance._load_config()
         return cls._instance
-    
+
     def _load_config(self):
         """Load configuration from YAML file."""
         config_path = os.path.join(os.path.dirname(__file__), 'ensemble_config.yaml')
@@ -22,12 +24,12 @@ class EnsembleConfig:
         except Exception as e:
             print(f"❌ Failed to load ensemble config: {e}")
             self._config = {}
-    
+
     def get(self, key: str, default: Any = None) -> Any:
         """Get configuration value by key (supports dot notation for nested keys)."""
         keys = key.split('.')
         value = self._config
-        
+
         try:
             for k in keys:
                 value = value[k]
@@ -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/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/models/v27_predictor.py

@@ -29,7 +29,7 @@ class V27Predictor:
     82-feature odds-free vector.
     """
 
-    MARKETS = ["ms", "ou25"]
+    MARKETS = ['ms', 'ou25', 'btts']
 
     def __init__(self):
         self.models: Dict[str, Dict[str, object]] = {}
@@ -56,7 +56,7 @@ class V27Predictor:
             return False
 
         # Load models per market
-        model_types = {"xgb": "xgb", "lgb": "lgb", "cb": "cb"}
+        model_types = {"xgb": "xgb", "lgb": "lgb"}
 
         for market in self.MARKETS:
             self.models[market] = {}
@@ -227,11 +227,63 @@ class V27Predictor:
             "over": float(avg[1]),
         }
 
+    def predict_btts(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Both Teams To Score probabilities.
+        Returns dict with keys: no, yes.
+        """
+        if not self._loaded or 'btts' not in self.models or not self.models['btts']:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models['btts'].items():
+            proba = self._predict_with_model(model, X, f'BTTS/{label}', expected_classes=2)
+            if proba is not None and len(proba) == 2:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        avg = np.mean(probs_list, axis=0)
+        return {
+            'no': float(avg[0]),
+            'yes': float(avg[1]),
+        }
+
+    def predict_dc(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Double Chance probabilities.
+
+        DC is algebraically derived from MS predictions:
+          1X = home + draw
+          X2 = draw + away
+          12 = home + away
+
+        This gives an odds-free DC estimate for divergence detection.
+        """
+        ms_probs = self.predict_ms(features)
+        if not ms_probs:
+            return None
+
+        home = ms_probs['home']
+        draw = ms_probs['draw']
+        away = ms_probs['away']
+
+        return {
+            '1x': round(home + draw, 4),
+            'x2': round(draw + away, 4),
+            '12': round(home + away, 4),
+        }
+
     def predict_all(self, features: Dict[str, float]) -> Dict[str, Optional[Dict[str, float]]]:
         """Run predictions for all supported markets."""
         return {
-            "ms": self.predict_ms(features),
+            'ms': self.predict_ms(features),
-            "ou25": self.predict_ou25(features),
+            'ou25': self.predict_ou25(features),
+            'btts': self.predict_btts(features),
+            'dc': self.predict_dc(features),
         }
 
 

ai-engine/reports/training_v25/v25_market_metrics.json

@@ -1,8 +1,8 @@
 {
-  "trained_at": "2026-04-14 17:20:03",
+  "trained_at": "2026-05-06 15:53:36",
   "market_results": {
     "MS": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -107,19 +107,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.8938,
+      "xgb_accuracy": 0.5437,
-      "xgb_logloss": 0.2263,
+      "xgb_logloss": 0.9429,
-      "lgb_accuracy": 0.8938,
+      "lgb_accuracy": 0.5436,
-      "lgb_logloss": 0.2214,
+      "lgb_logloss": 0.9423,
-      "ensemble_accuracy": 0.8945,
+      "ensemble_accuracy": 0.5442,
-      "ensemble_logloss": 0.2226,
+      "ensemble_logloss": 0.9418,
       "class_count": 3
     },
     "OU15": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -224,19 +224,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9088,
+      "xgb_accuracy": 0.753,
-      "xgb_logloss": 0.1758,
+      "xgb_logloss": 0.5256,
-      "lgb_accuracy": 0.9067,
+      "lgb_accuracy": 0.7523,
-      "lgb_logloss": 0.1783,
+      "lgb_logloss": 0.5262,
-      "ensemble_accuracy": 0.9108,
+      "ensemble_accuracy": 0.7533,
-      "ensemble_logloss": 0.1753,
+      "ensemble_logloss": 0.5254,
       "class_count": 2
     },
     "OU25": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -341,19 +341,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9204,
+      "xgb_accuracy": 0.6253,
-      "xgb_logloss": 0.1535,
+      "xgb_logloss": 0.635,
-      "lgb_accuracy": 0.9224,
+      "lgb_accuracy": 0.6246,
-      "lgb_logloss": 0.1523,
+      "lgb_logloss": 0.6347,
-      "ensemble_accuracy": 0.9217,
+      "ensemble_accuracy": 0.6262,
-      "ensemble_logloss": 0.1518,
+      "ensemble_logloss": 0.6343,
       "class_count": 2
     },
     "OU35": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -458,19 +458,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9578,
+      "xgb_accuracy": 0.7283,
-      "xgb_logloss": 0.1171,
+      "xgb_logloss": 0.5463,
-      "lgb_accuracy": 0.9564,
+      "lgb_accuracy": 0.7304,
-      "lgb_logloss": 0.1144,
+      "lgb_logloss": 0.546,
-      "ensemble_accuracy": 0.9571,
+      "ensemble_accuracy": 0.7297,
-      "ensemble_logloss": 0.1149,
+      "ensemble_logloss": 0.5456,
       "class_count": 2
     },
     "BTTS": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -575,19 +575,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9238,
+      "xgb_accuracy": 0.5894,
-      "xgb_logloss": 0.1439,
+      "xgb_logloss": 0.6636,
-      "lgb_accuracy": 0.9265,
+      "lgb_accuracy": 0.5928,
-      "lgb_logloss": 0.143,
+      "lgb_logloss": 0.6633,
-      "ensemble_accuracy": 0.9265,
+      "ensemble_accuracy": 0.5897,
-      "ensemble_logloss": 0.1424,
+      "ensemble_logloss": 0.6628,
       "class_count": 2
     },
     "HT_RESULT": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -692,19 +692,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
+      "train_samples": 72245,
-      "val_samples": 1468,
+      "val_samples": 15481,
-      "test_samples": 1468,
+      "test_samples": 15482,
-      "xgb_accuracy": 0.5627,
+      "xgb_accuracy": 0.4695,
-      "xgb_logloss": 0.8712,
+      "xgb_logloss": 1.0174,
-      "lgb_accuracy": 0.5715,
+      "lgb_accuracy": 0.4677,
-      "lgb_logloss": 0.8649,
+      "lgb_logloss": 1.0166,
-      "ensemble_accuracy": 0.5811,
+      "ensemble_accuracy": 0.4688,
-      "ensemble_logloss": 0.8649,
+      "ensemble_logloss": 1.0164,
       "class_count": 3
     },
     "HT_OU05": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -809,19 +809,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
+      "train_samples": 72245,
-      "val_samples": 1468,
+      "val_samples": 15481,
-      "test_samples": 1468,
+      "test_samples": 15482,
-      "xgb_accuracy": 0.7221,
+      "xgb_accuracy": 0.7011,
-      "xgb_logloss": 0.5122,
+      "xgb_logloss": 0.5939,
-      "lgb_accuracy": 0.7268,
+      "lgb_accuracy": 0.7002,
-      "lgb_logloss": 0.5092,
+      "lgb_logloss": 0.5936,
-      "ensemble_accuracy": 0.7275,
+      "ensemble_accuracy": 0.7009,
-      "ensemble_logloss": 0.5084,
+      "ensemble_logloss": 0.5932,
       "class_count": 2
     },
     "HT_OU15": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -926,19 +926,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
+      "train_samples": 72245,
-      "val_samples": 1468,
+      "val_samples": 15481,
-      "test_samples": 1468,
+      "test_samples": 15482,
-      "xgb_accuracy": 0.752,
+      "xgb_accuracy": 0.6723,
-      "xgb_logloss": 0.5252,
+      "xgb_logloss": 0.6126,
-      "lgb_accuracy": 0.7595,
+      "lgb_accuracy": 0.6736,
-      "lgb_logloss": 0.5213,
+      "lgb_logloss": 0.6118,
-      "ensemble_accuracy": 0.7595,
+      "ensemble_accuracy": 0.6734,
-      "ensemble_logloss": 0.5192,
+      "ensemble_logloss": 0.6117,
       "class_count": 2
     },
     "HTFT": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -1043,19 +1043,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
+      "train_samples": 72245,
-      "val_samples": 1468,
+      "val_samples": 15481,
-      "test_samples": 1468,
+      "test_samples": 15482,
-      "xgb_accuracy": 0.5136,
+      "xgb_accuracy": 0.3337,
-      "xgb_logloss": 1.1384,
+      "xgb_logloss": 1.8208,
-      "lgb_accuracy": 0.5184,
+      "lgb_accuracy": 0.3332,
-      "lgb_logloss": 1.1469,
+      "lgb_logloss": 1.8203,
-      "ensemble_accuracy": 0.5143,
+      "ensemble_accuracy": 0.3358,
-      "ensemble_logloss": 1.1339,
+      "ensemble_logloss": 1.8186,
       "class_count": 9
     },
     "ODD_EVEN": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -1160,19 +1160,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.8863,
+      "xgb_accuracy": 0.5296,
-      "xgb_logloss": 0.3565,
+      "xgb_logloss": 0.6841,
-      "lgb_accuracy": 0.8802,
+      "lgb_accuracy": 0.5359,
-      "lgb_logloss": 0.3338,
+      "lgb_logloss": 0.6822,
-      "ensemble_accuracy": 0.8863,
+      "ensemble_accuracy": 0.531,
-      "ensemble_logloss": 0.3423,
+      "ensemble_logloss": 0.6826,
       "class_count": 2
     },
     "CARDS_OU45": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -1277,19 +1277,19 @@
         "home_goals_form",
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ai-engine/reports/training_v25/v25_pro_metrics.json

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+      "train": 64116,
+      "val": 16029,
+      "cal": 10686,
+      "test": 16030,
+      "features_used": 114,
+      "xgb_best_params": {
+        "max_depth": 8,
+        "eta": 0.010098671964329344,
+        "subsample": 0.9969616653360747,
+        "colsample_bytree": 0.5085930751344795,
+        "min_child_weight": 10,
+        "gamma": 0.8600893137103568,
+        "reg_lambda": 7.556243125116086,
+        "reg_alpha": 0.5596869360839299
+      },
+      "lgb_best_params": {
+        "max_depth": 8,
+        "learning_rate": 0.0183440412249233,
+        "feature_fraction": 0.5416111323291537,
+        "bagging_fraction": 0.9754210612419695,
+        "bagging_freq": 2,
+        "min_child_samples": 5,
+        "lambda_l1": 0.09157782079463243,
+        "lambda_l2": 2.559000594641019
+      },
+      "xgb_best_iteration": 973,
+      "lgb_best_iteration": 503,
+      "xgb_optuna_best_logloss": 0.6408,
+      "lgb_optuna_best_logloss": 0.6407,
+      "test_xgb_raw": {
+        "accuracy": 0.597,
+        "logloss": 0.6501
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6019,
+        "logloss": 0.6471
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5977,
+        "logloss": 0.6486
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6019,
+        "logloss": 0.6498
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5964,
+        "logloss": 0.6487
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6034,
+        "logloss": 0.6467
+      }
+    },
+    "HANDICAP_MS": {
+      "market": "HANDICAP_MS",
+      "samples": 106861,
+      "train": 64116,
+      "val": 16029,
+      "cal": 10686,
+      "test": 16030,
+      "features_used": 114,
+      "xgb_best_params": {
+        "max_depth": 4,
+        "eta": 0.01475719431584365,
+        "subsample": 0.867899230696633,
+        "colsample_bytree": 0.6518567347674479,
+        "min_child_weight": 9,
+        "gamma": 0.34932767754310273,
+        "reg_lambda": 3.3257801082201637e-07,
+        "reg_alpha": 4.6977721450875555e-06
+      },
+      "lgb_best_params": {
+        "max_depth": 7,
+        "learning_rate": 0.019649745228555244,
+        "feature_fraction": 0.7903699430858344,
+        "bagging_fraction": 0.7932436899357213,
+        "bagging_freq": 3,
+        "min_child_samples": 30,
+        "lambda_l1": 9.496143774926949e-08,
+        "lambda_l2": 0.0049885051588706136
+      },
+      "xgb_best_iteration": 1016,
+      "lgb_best_iteration": 364,
+      "xgb_optuna_best_logloss": 0.8328,
+      "lgb_optuna_best_logloss": 0.8322,
+      "test_xgb_raw": {
+        "accuracy": 0.6062,
+        "logloss": 0.871
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6039,
+        "logloss": 0.8729
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.6079,
+        "logloss": 0.8713
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6067,
+        "logloss": 0.8736
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.6072,
+        "logloss": 0.8707
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6066,
+        "logloss": 0.8728
+      }
+    }
+  }
+}

ai-engine/scripts/backtest_day.py

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

ai-engine/scripts/elo_backfill.py

@@ -59,7 +59,7 @@ def fetch_matches(conn, sport: str):
 
 
 def flush_features_batch(conn, rows, dry_run: bool, sport: str = 'football'):
-    """Bulk upsert a batch of (match_id, home_elo, away_elo) into sport-partitioned ai_features table."""
+    """Bulk upsert ELO features into sport-partitioned ai_features table."""
     if not rows or dry_run:
         return
 
@@ -70,19 +70,27 @@ def flush_features_batch(conn, rows, dry_run: bool, sport: str = 'football'):
             f"""
             INSERT INTO {table_name}
                 (match_id, home_elo, away_elo,
+                 home_home_elo, away_away_elo,
+                 home_form_elo, away_form_elo,
+                 elo_diff,
                  home_form_score, away_form_score,
                  missing_players_impact, calculator_ver, updated_at)
             VALUES %s
             ON CONFLICT (match_id) DO UPDATE SET
                 home_elo             = EXCLUDED.home_elo,
                 away_elo             = EXCLUDED.away_elo,
+                home_home_elo        = EXCLUDED.home_home_elo,
+                away_away_elo        = EXCLUDED.away_away_elo,
+                home_form_elo        = EXCLUDED.home_form_elo,
+                away_form_elo        = EXCLUDED.away_form_elo,
+                elo_diff             = EXCLUDED.elo_diff,
                 home_form_score      = EXCLUDED.home_form_score,
                 away_form_score      = EXCLUDED.away_form_score,
                 calculator_ver       = EXCLUDED.calculator_ver,
                 updated_at           = EXCLUDED.updated_at
             """,
             rows,
-            template="(%s, %s, %s, %s, %s, 0.0, %s, NOW())",
+            template="(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, 0.0, %s, NOW())",
             page_size=500,
         )
     conn.commit()
@@ -136,16 +144,24 @@ def backfill(sport: str, batch_size: int, dry_run: bool):
         if not home_id or not away_id:
             continue
 
-        # Snapshot PRE-match ELO
+        # Snapshot PRE-match ELO (all dimensions)
         home_rating = elo.get_or_create_rating(home_id, h_name or "")
         away_rating = elo.get_or_create_rating(away_id, a_name or "")
 
+        h_overall = round(home_rating.overall_elo, 2)
+        a_overall = round(away_rating.overall_elo, 2)
+
         feature_buf.append((
             match_id,
-            round(home_rating.overall_elo, 2),
+            h_overall,                                          # home_elo
-            round(away_rating.overall_elo, 2),
+            a_overall,                                          # away_elo
-            round(form_to_score(home_rating.recent_form), 2),
+            round(home_rating.home_elo, 2),                     # home_home_elo
-            round(form_to_score(away_rating.recent_form), 2),
+            round(away_rating.away_elo, 2),                     # away_away_elo
+            round(home_rating.form_elo, 2),                     # home_form_elo
+            round(away_rating.form_elo, 2),                     # away_form_elo
+            round(h_overall - a_overall, 2),                    # elo_diff
+            round(form_to_score(home_rating.recent_form), 2),   # home_form_score
+            round(form_to_score(away_rating.recent_form), 2),   # away_form_score
             CALCULATOR_VER,
         ))
 

ai-engine/scripts/enrich_ai_features.py

@@ -0,0 +1,459 @@
+#!/usr/bin/env python3
+"""
+AI Features Full Enrichment Script
+====================================
+Fills empty/default columns in football_ai_features that were not populated
+by the original elo_backfill_v1 script.
+
+Enriches: H2H, referee, team_stats, league_averages, form_streaks,
+          rolling_goals, implied_odds, and clean_sheet/scoring rates.
+
+Usage:
+    python scripts/enrich_ai_features.py                          # enrich all
+    python scripts/enrich_ai_features.py --batch-size 500         # smaller batches
+    python scripts/enrich_ai_features.py --dry-run                # preview only
+    python scripts/enrich_ai_features.py --force                  # re-enrich all rows
+    python scripts/enrich_ai_features.py --limit 1000             # process N rows max
+
+Designed to be idempotent: uses ON CONFLICT upserts, skips already-enriched rows.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+import time
+import argparse
+from typing import Any, Dict, List, Optional, Tuple
+
+# Add ai-engine root to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import psycopg2
+from psycopg2.extras import RealDictCursor, execute_values
+
+from data.db import get_clean_dsn
+from services.feature_enrichment import FeatureEnrichmentService
+
+# ────────────────────────── constants ──────────────────────────
+
+CALCULATOR_VER = 'enrichment_v2.0'
+DEFAULT_BATCH_SIZE = 200
+
+
+# ────────────────────────── helpers ────────────────────────────
+
+def fetch_unenriched_matches(
+    conn: psycopg2.extensions.connection,
+    force: bool = False,
+    limit: Optional[int] = None,
+) -> List[Dict[str, Any]]:
+    """
+    Fetch matches from football_ai_features that still have default values
+    in the enrichment columns (h2h_total=0 AND referee_avg_cards=0).
+
+    If force=True, fetches ALL rows regardless of current state.
+    """
+    with conn.cursor(cursor_factory=RealDictCursor) as cur:
+        where_clause = "WHERE 1=1" if force else (
+            "WHERE (faf.h2h_total = 0 AND faf.referee_avg_cards = 0)"
+        )
+        limit_clause = f"LIMIT {limit}" if limit else ""
+
+        cur.execute(f"""
+            SELECT
+                faf.match_id,
+                m.home_team_id,
+                m.away_team_id,
+                m.mst_utc,
+                m.league_id,
+                m.score_home,
+                m.score_away
+            FROM football_ai_features faf
+            JOIN matches m ON m.id = faf.match_id
+            WHERE m.status = 'FT'
+              AND m.score_home IS NOT NULL
+              AND m.sport = 'football'
+              AND ({where_clause.replace('WHERE ', '')})
+            ORDER BY m.mst_utc ASC
+            {limit_clause}
+        """)
+        return cur.fetchall()
+
+
+def fetch_referee_for_match(
+    cur: RealDictCursor,
+    match_id: str,
+) -> Optional[str]:
+    """Get the head referee name for a match from match_officials."""
+    try:
+        cur.execute("""
+            SELECT mo.name
+            FROM match_officials mo
+            WHERE mo.match_id = %s
+              AND mo.role_id = 1
+            LIMIT 1
+        """, (match_id,))
+        row = cur.fetchone()
+        return row['name'] if row else None
+    except Exception:
+        return None
+
+
+def fetch_implied_odds(
+    cur: RealDictCursor,
+    match_id: str,
+) -> Dict[str, float]:
+    """Get implied probabilities from odd_categories + odd_selections."""
+    defaults = {
+        'implied_home': 0.33,
+        'implied_draw': 0.33,
+        'implied_away': 0.33,
+        'implied_over25': 0.50,
+        'implied_btts_yes': 0.50,
+        'odds_overround': 0.0,
+    }
+    try:
+        cur.execute("""
+            SELECT oc.name AS cat_name, os.name AS sel_name, os.odd_value
+            FROM odd_selections os
+            JOIN odd_categories oc ON os.odd_category_db_id = oc.db_id
+            WHERE oc.match_id = %s
+        """, (match_id,))
+        rows = cur.fetchall()
+    except Exception:
+        return defaults
+
+    odds: Dict[str, float] = {}
+    for row in rows:
+        try:
+            cat = (row.get('cat_name') or '').lower().strip()
+            sel = (row.get('sel_name') or '').strip()
+            val = float(row.get('odd_value', 0))
+            if val <= 0:
+                continue
+
+            if cat == 'maç sonucu':
+                if sel == '1':
+                    odds['ms_h'] = val
+                elif sel in ('0', 'X'):
+                    odds['ms_d'] = val
+                elif sel == '2':
+                    odds['ms_a'] = val
+            elif cat == '2,5 alt/üst':
+                if 'üst' in sel.lower():
+                    odds['ou25_o'] = val
+                elif 'alt' in sel.lower():
+                    odds['ou25_u'] = val
+            elif cat == 'karşılıklı gol':
+                if 'var' in sel.lower():
+                    odds['btts_y'] = val
+                elif 'yok' in sel.lower():
+                    odds['btts_n'] = val
+        except (ValueError, TypeError):
+            continue
+
+    # Compute implied probabilities
+    ms_h = odds.get('ms_h', 0)
+    ms_d = odds.get('ms_d', 0)
+    ms_a = odds.get('ms_a', 0)
+
+    if ms_h > 1.0 and ms_d > 1.0 and ms_a > 1.0:
+        raw_sum = 1 / ms_h + 1 / ms_d + 1 / ms_a
+        overround = raw_sum - 1.0
+        defaults['implied_home'] = round((1 / ms_h) / raw_sum, 4)
+        defaults['implied_draw'] = round((1 / ms_d) / raw_sum, 4)
+        defaults['implied_away'] = round((1 / ms_a) / raw_sum, 4)
+        defaults['odds_overround'] = round(overround, 4)
+
+    ou25_o = odds.get('ou25_o', 0)
+    ou25_u = odds.get('ou25_u', 0)
+    if ou25_o > 1.0 and ou25_u > 1.0:
+        raw_sum = 1 / ou25_o + 1 / ou25_u
+        defaults['implied_over25'] = round((1 / ou25_o) / raw_sum, 4)
+
+    btts_y = odds.get('btts_y', 0)
+    btts_n = odds.get('btts_n', 0)
+    if btts_y > 1.0 and btts_n > 1.0:
+        raw_sum = 1 / btts_y + 1 / btts_n
+        defaults['implied_btts_yes'] = round((1 / btts_y) / raw_sum, 4)
+
+    return defaults
+
+
+def enrich_single_match(
+    enrichment: FeatureEnrichmentService,
+    cur: RealDictCursor,
+    match: Dict[str, Any],
+) -> Dict[str, Any]:
+    """
+    Compute all enrichment features for a single match and return
+    a dict ready for DB upsert.
+    """
+    match_id = match['match_id']
+    home_id = str(match['home_team_id'])
+    away_id = str(match['away_team_id'])
+    mst_utc = int(match['mst_utc']) if match['mst_utc'] else 0
+    league_id = str(match['league_id']) if match['league_id'] else None
+
+    # 1. Team stats
+    home_stats = enrichment.compute_team_stats(cur, home_id, mst_utc)
+    away_stats = enrichment.compute_team_stats(cur, away_id, mst_utc)
+
+    # 2. H2H
+    h2h = enrichment.compute_h2h(cur, home_id, away_id, mst_utc)
+
+    # 3. Form & streaks
+    home_form = enrichment.compute_form_streaks(cur, home_id, mst_utc)
+    away_form = enrichment.compute_form_streaks(cur, away_id, mst_utc)
+
+    # 4. Referee
+    referee_name = fetch_referee_for_match(cur, match_id)
+    referee = enrichment.compute_referee_stats(cur, referee_name, mst_utc)
+
+    # 5. League averages
+    league = enrichment.compute_league_averages(cur, league_id, mst_utc)
+
+    # 6. Rolling stats (for goals avg)
+    home_rolling = enrichment.compute_rolling_stats(cur, home_id, mst_utc)
+    away_rolling = enrichment.compute_rolling_stats(cur, away_id, mst_utc)
+
+    # 7. Implied odds
+    implied = fetch_implied_odds(cur, match_id)
+
+    return {
+        'match_id': match_id,
+        # Team stats
+        'home_avg_possession': round(home_stats['avg_possession'], 2),
+        'away_avg_possession': round(away_stats['avg_possession'], 2),
+        'home_avg_shots_on_target': round(home_stats['avg_shots_on_target'], 2),
+        'away_avg_shots_on_target': round(away_stats['avg_shots_on_target'], 2),
+        'home_shot_conversion': round(home_stats['shot_conversion'], 4),
+        'away_shot_conversion': round(away_stats['shot_conversion'], 4),
+        'home_avg_corners': round(home_stats['avg_corners'], 2),
+        'away_avg_corners': round(away_stats['avg_corners'], 2),
+        # H2H
+        'h2h_total': h2h['total_matches'],
+        'h2h_home_win_rate': round(h2h['home_win_rate'], 4),
+        'h2h_avg_goals': round(h2h['avg_goals'], 2),
+        'h2h_over25_rate': round(h2h['over25_rate'], 4),
+        'h2h_btts_rate': round(h2h['btts_rate'], 4),
+        # Form
+        'home_clean_sheet_rate': round(home_form['clean_sheet_rate'], 4),
+        'away_clean_sheet_rate': round(away_form['clean_sheet_rate'], 4),
+        'home_scoring_rate': round(home_form['scoring_rate'], 4),
+        'away_scoring_rate': round(away_form['scoring_rate'], 4),
+        'home_win_streak': home_form['winning_streak'],
+        'away_win_streak': away_form['winning_streak'],
+        # Rolling goals
+        'home_goals_avg_5': round(home_rolling['rolling5_goals'], 2),
+        'away_goals_avg_5': round(away_rolling['rolling5_goals'], 2),
+        'home_conceded_avg_5': round(home_rolling['rolling5_conceded'], 2),
+        'away_conceded_avg_5': round(away_rolling['rolling5_conceded'], 2),
+        # Referee
+        'referee_avg_cards': round(referee['cards_total'], 2),
+        'referee_home_bias': round(referee['home_bias'], 4),
+        'referee_avg_goals': round(referee['avg_goals'], 2),
+        # League
+        'league_avg_goals': round(league['avg_goals'], 2),
+        'league_home_win_pct': round(league['home_win_rate'], 4),
+        'league_over25_pct': round(league['ou25_rate'], 4),
+        # Implied odds
+        'implied_home': implied['implied_home'],
+        'implied_draw': implied['implied_draw'],
+        'implied_away': implied['implied_away'],
+        'implied_over25': implied['implied_over25'],
+        'implied_btts_yes': implied['implied_btts_yes'],
+        'odds_overround': implied['odds_overround'],
+        # Missing players impact — default (no lineup data for historical)
+        'missing_players_impact': 0.0,
+        # Version
+        'calculator_ver': CALCULATOR_VER,
+    }
+
+
+def flush_enrichment_batch(
+    conn: psycopg2.extensions.connection,
+    rows: List[Dict[str, Any]],
+    dry_run: bool,
+) -> int:
+    """Bulk upsert enriched features into football_ai_features."""
+    if not rows or dry_run:
+        return 0
+
+    columns = [
+        'match_id',
+        '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',
+        'h2h_total', 'h2h_home_win_rate', 'h2h_avg_goals',
+        'h2h_over25_rate', 'h2h_btts_rate',
+        'home_clean_sheet_rate', 'away_clean_sheet_rate',
+        'home_scoring_rate', 'away_scoring_rate',
+        'home_win_streak', 'away_win_streak',
+        'home_goals_avg_5', 'away_goals_avg_5',
+        'home_conceded_avg_5', 'away_conceded_avg_5',
+        'referee_avg_cards', 'referee_home_bias', 'referee_avg_goals',
+        'league_avg_goals', 'league_home_win_pct', 'league_over25_pct',
+        'implied_home', 'implied_draw', 'implied_away',
+        'implied_over25', 'implied_btts_yes', 'odds_overround',
+        'missing_players_impact', 'calculator_ver',
+    ]
+
+    # Build update SET clause (skip match_id)
+    update_cols = [c for c in columns if c != 'match_id']
+    set_clause = ', '.join(f'{c} = EXCLUDED.{c}' for c in update_cols)
+
+    placeholders = ', '.join(['%s'] * len(columns))
+    values = [
+        tuple(row[c] for c in columns)
+        for row in rows
+    ]
+
+    with conn.cursor() as cur:
+        execute_values(
+            cur,
+            f"""
+            INSERT INTO football_ai_features ({', '.join(columns)})
+            VALUES %s
+            ON CONFLICT (match_id) DO UPDATE SET
+                {set_clause},
+                updated_at = NOW()
+            """,
+            values,
+            template=f"({placeholders})",
+            page_size=200,
+        )
+    conn.commit()
+    return len(rows)
+
+
+# ────────────────────────── main ───────────────────────────────
+
+def run_enrichment(
+    batch_size: int,
+    dry_run: bool,
+    force: bool,
+    limit: Optional[int],
+) -> None:
+    """Core enrichment loop."""
+    dsn = get_clean_dsn()
+    conn = psycopg2.connect(dsn)
+
+    print(f"\n{'=' * 60}")
+    print(f"🧠 AI Features Full Enrichment — {CALCULATOR_VER}")
+    print(f"   batch_size={batch_size}  dry_run={dry_run}  force={force}")
+    print(f"{'=' * 60}")
+
+    # 1. Fetch unenriched matches
+    t0 = time.time()
+    matches = fetch_unenriched_matches(conn, force=force, limit=limit)
+    print(f"\n📊 {len(matches):,} matches to enrich ({time.time() - t0:.1f}s)")
+
+    if not matches:
+        print("✅ Nothing to enrich — all rows already populated.")
+        conn.close()
+        return
+
+    # 2. Initialize enrichment service
+    enrichment = FeatureEnrichmentService()
+
+    # 3. Process in batches
+    total = len(matches)
+    processed = 0
+    written = 0
+    errors = 0
+    batch_buf: List[Dict[str, Any]] = []
+    t_start = time.time()
+
+    # Use a dedicated cursor with RealDictCursor for all enrichment queries
+    enrich_cur = conn.cursor(cursor_factory=RealDictCursor)
+
+    for idx, match in enumerate(matches):
+        try:
+            enriched = enrich_single_match(enrichment, enrich_cur, match)
+            batch_buf.append(enriched)
+        except Exception as e:
+            errors += 1
+            if errors <= 10:
+                print(f"   ⚠️ Error enriching {match.get('match_id', '?')}: {e}")
+
+        processed += 1
+
+        # Flush batch
+        if len(batch_buf) >= batch_size:
+            flushed = flush_enrichment_batch(conn, batch_buf, dry_run)
+            written += flushed
+            batch_buf.clear()
+
+        # Progress reporting
+        if processed % 500 == 0:
+            elapsed = time.time() - t_start
+            rate = processed / elapsed if elapsed > 0 else 0
+            remaining = (total - processed) / rate if rate > 0 else 0
+            pct = processed / total * 100
+            print(
+                f"   [{processed:>8,} / {total:,}] "
+                f"({pct:.1f}%) | {rate:.0f} matches/s | "
+                f"ETA: {remaining / 60:.1f} min | "
+                f"errors: {errors}"
+            )
+
+    # Flush remaining
+    if batch_buf:
+        flushed = flush_enrichment_batch(conn, batch_buf, dry_run)
+        written += flushed
+
+    enrich_cur.close()
+
+    elapsed = time.time() - t_start
+    print(f"\n{'=' * 60}")
+    print(f"✅ Enrichment complete:")
+    print(f"   Processed: {processed:,} matches in {elapsed:.1f}s")
+    print(f"   Written:   {written:,} rows")
+    print(f"   Errors:    {errors:,}")
+    print(f"   Rate:      {processed / elapsed:.0f} matches/s")
+    print(f"{'=' * 60}")
+
+    conn.close()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Enrich football_ai_features with H2H, referee, stats, and odds data"
+    )
+    parser.add_argument(
+        '--batch-size',
+        type=int,
+        default=DEFAULT_BATCH_SIZE,
+        help=f'DB insert batch size (default: {DEFAULT_BATCH_SIZE})',
+    )
+    parser.add_argument(
+        '--dry-run',
+        action='store_true',
+        help='Compute features but do not write to DB',
+    )
+    parser.add_argument(
+        '--force',
+        action='store_true',
+        help='Re-enrich ALL rows, not just empty ones',
+    )
+    parser.add_argument(
+        '--limit',
+        type=int,
+        default=None,
+        help='Max number of matches to process',
+    )
+    args = parser.parse_args()
+
+    run_enrichment(
+        batch_size=args.batch_size,
+        dry_run=args.dry_run,
+        force=args.force,
+        limit=args.limit,
+    )
+
+
+if __name__ == '__main__':
+    main()

ai-engine/scripts/extract_training_data.py

@@ -14,6 +14,7 @@ import json
 import csv
 import math
 import time
+import bisect
 from datetime import datetime
 from collections import defaultdict
 
@@ -119,6 +120,14 @@ FEATURE_COLS = [
     "home_key_players", "away_key_players",
     "home_missing_impact", "away_missing_impact",
     "home_goals_form", "away_goals_form",
+
+    # Player-Level Features (12)
+    "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",
     
     # Labels
     "score_home", "score_away", "total_goals",
@@ -336,7 +345,7 @@ class BatchDataLoader:
             self.team_stats[tid].append((mst, poss, sot, tshots, corn, team_goals))
 
     def _load_squad_data(self):
-        """Bulk load squad participation + player events for squad features."""
+        """Bulk load squad participation + player events + player career for squad features."""
         ph = ",".join(["%s"] * len(self.top_league_ids))
 
         # 1) Participation: starting XI count + position distribution per (match, team)
@@ -429,9 +438,90 @@ class BatchDataLoader:
         for m in self.matches:
             match_mst[m[0]] = m[7]  # m[0]=id, m[7]=mst_utc
 
-        # 6) Build combined cache — NO DATA LEAKAGE
+        # ─── NEW: Player Career Stats (prefix-sum for O(1) temporal lookup) ───
-        # goals_form: avg goals from last 5 matches BEFORE this match (not this match!)
+        # 6a) Goals per player per match date
-        # squad_quality: only uses pre-match info (lineup, key players) — no current-match goals/assists
+        self.cur.execute(f"""
+            SELECT mpe.player_id, m.mst_utc,
+                   SUM(CASE WHEN mpe.event_type = 'goal'
+                            AND COALESCE(mpe.event_subtype, '') NOT ILIKE '%%penaltı kaçırma%%'
+                       THEN 1 ELSE 0 END) AS goals
+            FROM match_player_events mpe
+            JOIN matches m ON mpe.match_id = m.id
+            WHERE m.status = 'FT' AND m.sport = 'football' AND m.league_id IN ({ph})
+            GROUP BY mpe.player_id, m.mst_utc
+        """, self.top_league_ids)
+
+        player_goals_raw = defaultdict(dict)
+        for pid, mst, goals in self.cur.fetchall():
+            player_goals_raw[pid][mst] = (player_goals_raw[pid].get(mst, 0)) + (goals or 0)
+
+        # 6b) Assists per player per match date
+        self.cur.execute(f"""
+            SELECT mpe.assist_player_id, m.mst_utc, COUNT(*) AS assists
+            FROM match_player_events mpe
+            JOIN matches m ON mpe.match_id = m.id
+            WHERE m.status = 'FT' AND m.sport = 'football' AND m.league_id IN ({ph})
+              AND mpe.event_type = 'goal' AND mpe.assist_player_id IS NOT NULL
+            GROUP BY mpe.assist_player_id, m.mst_utc
+        """, self.top_league_ids)
+
+        player_assists_raw = defaultdict(dict)
+        for pid, mst, assists in self.cur.fetchall():
+            player_assists_raw[pid][mst] = (player_assists_raw[pid].get(mst, 0)) + (assists or 0)
+
+        # 6c) Player participation dates (starts only)
+        self.cur.execute(f"""
+            SELECT mpp.player_id, m.mst_utc
+            FROM match_player_participation mpp
+            JOIN matches m ON mpp.match_id = m.id
+            WHERE mpp.is_starting = true
+              AND m.status = 'FT' AND m.sport = 'football' AND m.league_id IN ({ph})
+            ORDER BY mpp.player_id, m.mst_utc
+        """, self.top_league_ids)
+
+        player_starts_raw = defaultdict(list)
+        for pid, mst in self.cur.fetchall():
+            player_starts_raw[pid].append(mst)
+
+        # 6d) Build prefix sums per player (goals_prefix[i] = total goals up to start i)
+        player_career = {}
+        all_pids = set(player_starts_raw.keys()) | set(player_goals_raw.keys()) | set(player_assists_raw.keys())
+        for pid in all_pids:
+            starts = sorted(set(player_starts_raw.get(pid, [])))
+            if not starts:
+                continue
+            g_map = player_goals_raw.get(pid, {})
+            a_map = player_assists_raw.get(pid, {})
+            cum_g, cum_a = 0, 0
+            goals_pf, assists_pf = [], []
+            for mst in starts:
+                cum_g += g_map.get(mst, 0)
+                cum_a += a_map.get(mst, 0)
+                goals_pf.append(cum_g)
+                assists_pf.append(cum_a)
+            player_career[pid] = {'msts': starts, 'gp': goals_pf, 'ap': assists_pf}
+
+        # Free raw dicts
+        del player_goals_raw, player_assists_raw, player_starts_raw
+        print(f"    📊 Player careers built: {len(player_career)} players", flush=True)
+
+        # ─── NEW: Team Lineup History (for squad continuity) ───
+        # 7) Per-team sorted lineups: [(mst, frozenset(player_ids))]
+        team_lineup_map = defaultdict(list)
+        for (mid, tid), pids in starting_players.items():
+            mst = match_mst.get(mid, 0)
+            if mst > 0 and pids:
+                team_lineup_map[tid].append((mst, frozenset(pids)))
+
+        team_lineup_history = {}
+        team_lineup_msts = {}
+        for tid, ll in team_lineup_map.items():
+            ll.sort(key=lambda x: x[0])
+            team_lineup_history[tid] = ll
+            team_lineup_msts[tid] = [x[0] for x in ll]
+        del team_lineup_map
+
+        # ─── 8) Build combined cache — NO DATA LEAKAGE ───
         all_keys = set(participation.keys()) | set(events.keys())
         for key in all_keys:
             mid, tid = key
@@ -443,30 +533,78 @@ class BatchDataLoader:
             kp_total = len(key_players_by_team.get(tid, set()))
             kp_missing = max(0, kp_total - kp_in_starting)
 
-            # Squad quality: composite score — ONLY pre-match info (no current-match goals/assists!)
+            # Squad quality: composite score — ONLY pre-match info
             squad_quality = (
                 part['starting_count'] * 0.3 +
                 kp_in_starting * 3.0 +
                 part['fwd_count'] * 1.5
             )
-            # Missing impact: how many key players are missing
             missing_impact = min(kp_missing / max(kp_total, 1), 1.0)
 
             # goals_form: avg goals from last 5 matches BEFORE this match
             current_mst = match_mst.get(mid, 0)
             team_history = self.team_matches.get(tid, [])
             recent_goals = [
-                tm[2]  # team_score
+                tm[2] for tm in team_history if tm[0] < current_mst
-                for tm in team_history
+            ][-5:]
-                if tm[0] < current_mst  # only matches BEFORE this one
-            ][-5:]  # last 5
             goals_form = sum(recent_goals) / len(recent_goals) if recent_goals else 1.3
 
+            # ─── NEW: Player-level aggregation for starting XI ───
+            lineup_g90, lineup_a90, total_exp = 0.0, 0.0, 0
+            best_scorer_total, best_scorer_id = 0, None
+            scorers_in_lineup = 0
+
+            for pid in starters:
+                pc = player_career.get(pid)
+                if not pc:
+                    continue
+                idx = bisect.bisect_left(pc['msts'], current_mst)
+                if idx == 0:
+                    continue  # no prior matches for this player
+                prior_starts = idx
+                prior_goals = pc['gp'][idx - 1]
+                prior_assists = pc['ap'][idx - 1]
+                lineup_g90 += prior_goals / prior_starts
+                lineup_a90 += prior_assists / prior_starts
+                total_exp += prior_starts
+                if prior_goals > 0:
+                    scorers_in_lineup += 1
+                if prior_goals > best_scorer_total:
+                    best_scorer_total = prior_goals
+                    best_scorer_id = pid
+
+            n_st = len(starters) or 1
+
+            # Top scorer recent form (goals in last 5 starts)
+            top_scorer_form = 0
+            if best_scorer_id:
+                pc = player_career.get(best_scorer_id)
+                if pc:
+                    idx = bisect.bisect_left(pc['msts'], current_mst)
+                    if idx > 0:
+                        s5 = max(0, idx - 5)
+                        top_scorer_form = pc['gp'][idx - 1] - (pc['gp'][s5 - 1] if s5 > 0 else 0)
+
+            # Squad continuity (overlap with previous match lineup)
+            squad_continuity = 0.5
+            msts_list = team_lineup_msts.get(tid)
+            if msts_list:
+                li = bisect.bisect_left(msts_list, current_mst)
+                if li > 0:
+                    prev_lineup = team_lineup_history[tid][li - 1][1]
+                    squad_continuity = len(frozenset(starters) & prev_lineup) / n_st
+
             self.squad_cache[key] = {
                 'squad_quality': squad_quality,
                 'key_players': kp_in_starting,
                 'missing_impact': missing_impact,
                 'goals_form': round(goals_form, 2),
+                'lineup_goals_per90': round(lineup_g90, 3),
+                'lineup_assists_per90': round(lineup_a90, 3),
+                'squad_continuity': round(squad_continuity, 3),
+                'top_scorer_form': top_scorer_form,
+                'avg_player_exp': round(total_exp / n_st, 1),
+                'goals_diversity': round(scorers_in_lineup / n_st, 3),
             }
 
     def _load_cards_data(self):
@@ -510,16 +648,24 @@ class FeatureExtractor:
         self.referee_engine = get_referee_engine()
         self.momentum_engine = get_momentum_engine()
     
+    # ── Data Quality Thresholds ──
+    # Matches below these thresholds produce default-only features that
+    # teach the model noise rather than signal.
+    DQ_MIN_FORM_MATCHES = 3          # team must have ≥3 prior matches
+    DQ_MIN_FEATURE_COVERAGE = 0.30   # ≥30% of key features must be non-default
+
     def extract_all(self) -> list:
-        """Extract features for all matches, yield row dicts."""
+        """Extract features for all matches with data quality validation."""
         matches = self.loader.matches
         total = len(matches)
         rows = []
         skipped = 0
+        dq_rejected = 0
+        dq_reasons: dict = defaultdict(int)
         t_start = time.time()
-        
+
         print(f"\n🔄 Extracting features for {total} matches...", flush=True)
-        
+
         # Process chronologically — ELO grows as we go
         for i, m in enumerate(matches):
             (
@@ -536,38 +682,43 @@ class FeatureExtractor:
                 away_name,
                 league_name,
             ) = m
-            
+
             if i % 100 == 0 and i > 0:
                 elapsed = time.time() - t_start
                 rate = i / elapsed  # matches per second
                 remaining = (total - i) / rate if rate > 0 else 0
                 pct = i / total * 100
-                print(f"  [{i}/{total}] ({pct:.0f}%) | {rate:.1f} maç/s | ETA: {remaining/60:.1f} dk | skipped: {skipped}", flush=True)
+                print(
-            
+                    f"  [{i}/{total}] ({pct:.0f}%) | {rate:.1f} maç/s | "
+                    f"ETA: {remaining/60:.1f} dk | skipped: {skipped} | "
+                    f"dq_rejected: {dq_rejected}",
+                    flush=True,
+                )
+
             row = self._extract_one(
-                mid,
+                mid, hid, aid, sh, sa, hth, hta, mst, lid,
-                hid,
+                home_name, away_name, league_name,
-                aid,
-                sh,
-                sa,
-                hth,
-                hta,
-                mst,
-                lid,
-                home_name,
-                away_name,
-                league_name,
             )
-            
+
             if row:
-                rows.append(row)
+                # ── Data Quality Gate ──
+                dq_pass, reason = self._validate_row_quality(row, hid, aid, mst)
+                if dq_pass:
+                    rows.append(row)
+                else:
+                    dq_rejected += 1
+                    dq_reasons[reason] += 1
             else:
                 skipped += 1
-            
+
             # Update ELO after processing (so ELO is calculated BEFORE the match)
             self._update_elo(hid, aid, sh, sa)
-        
+
-        print(f"  ✅ Extracted {len(rows)} rows, skipped {skipped}", flush=True)
+        print(f"  ✅ Extracted {len(rows)} rows, skipped {skipped}, DQ rejected {dq_rejected}", flush=True)
+        if dq_reasons:
+            print(f"  📊 DQ Rejection reasons:")
+            for reason, count in sorted(dq_reasons.items(), key=lambda x: -x[1]):
+                print(f"     {reason}: {count}")
         return rows
     
     def _extract_one(
@@ -842,6 +993,20 @@ class FeatureExtractor:
             "away_missing_impact": away_missing_impact,
             "home_goals_form": home_goals_form,
             "away_goals_form": away_goals_form,
+
+            # Player-Level Features
+            "home_lineup_goals_per90": home_sq.get('lineup_goals_per90', 0.0),
+            "away_lineup_goals_per90": away_sq.get('lineup_goals_per90', 0.0),
+            "home_lineup_assists_per90": home_sq.get('lineup_assists_per90', 0.0),
+            "away_lineup_assists_per90": away_sq.get('lineup_assists_per90', 0.0),
+            "home_squad_continuity": home_sq.get('squad_continuity', 0.5),
+            "away_squad_continuity": away_sq.get('squad_continuity', 0.5),
+            "home_top_scorer_form": home_sq.get('top_scorer_form', 0),
+            "away_top_scorer_form": away_sq.get('top_scorer_form', 0),
+            "home_avg_player_exp": home_sq.get('avg_player_exp', 0.0),
+            "away_avg_player_exp": away_sq.get('avg_player_exp', 0.0),
+            "home_goals_diversity": home_sq.get('goals_diversity', 0.0),
+            "away_goals_diversity": away_sq.get('goals_diversity', 0.0),
             
             # Labels
             "score_home": sh,
@@ -867,7 +1032,58 @@ class FeatureExtractor:
         }
         
         return row
-    
+
+    def _validate_row_quality(
+        self,
+        row: dict,
+        home_id: str,
+        away_id: str,
+        before_date: int,
+    ) -> tuple:
+        """
+        Data quality gate for training rows.
+
+        Ensures the feature vector has enough real signal to be useful for
+        training.  Rejects rows where critical features are all at their
+        default/fallback values — these teach the model noise, not patterns.
+
+        Returns (pass: bool, reason: str | None).
+        """
+        # 1. Minimum form history: both teams must have enough prior matches
+        home_history = self.loader.team_matches.get(home_id, [])
+        away_history = self.loader.team_matches.get(away_id, [])
+        home_prior = sum(1 for m in home_history if m[0] < before_date)
+        away_prior = sum(1 for m in away_history if m[0] < before_date)
+
+        if home_prior < self.DQ_MIN_FORM_MATCHES:
+            return False, 'home_insufficient_history'
+        if away_prior < self.DQ_MIN_FORM_MATCHES:
+            return False, 'away_insufficient_history'
+
+        # 2. Feature coverage check: count how many key features are non-default
+        key_features = [
+            ('home_goals_avg', 1.3),
+            ('away_goals_avg', 1.3),
+            ('home_clean_sheet_rate', 0.25),
+            ('away_clean_sheet_rate', 0.25),
+            ('home_avg_possession', 0.50),
+            ('away_avg_possession', 0.50),
+            ('home_avg_shots_on_target', 3.5),
+            ('away_avg_shots_on_target', 3.5),
+            ('h2h_total_matches', 0),
+            ('odds_ms_h', 0.0),
+        ]
+        non_default = sum(
+            1 for feat_name, default_val in key_features
+            if abs(float(row.get(feat_name, default_val)) - default_val) > 0.01
+        )
+        coverage = non_default / len(key_features)
+
+        if coverage < self.DQ_MIN_FEATURE_COVERAGE:
+            return False, f'low_feature_coverage_{coverage:.0%}'
+
+        return True, None
+
     # -------------------------------------------------------------------------
     # ELO (simplified inline version — doesn't need DB, grows incrementally)
     # -------------------------------------------------------------------------

ai-engine/scripts/train_v25_pro.py

@@ -0,0 +1,553 @@
+"""
+V25 Pro Model Trainer — Optuna + Isotonic Calibration
+=====================================================
+Combines V25's 83 features + 12 markets + temporal split
+with Optuna hyperparameter tuning and Isotonic Regression calibration.
+
+Usage:
+  python scripts/train_v25_pro.py
+  python scripts/train_v25_pro.py --markets MS,OU25,BTTS  # specific markets
+  python scripts/train_v25_pro.py --trials 30              # fewer trials
+"""
+
+import os
+import sys
+import json
+import pickle
+import argparse
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+import lightgbm as lgb
+import optuna
+from optuna.samplers import TPESampler
+from datetime import datetime
+from sklearn.metrics import accuracy_score, log_loss, classification_report
+from sklearn.isotonic import IsotonicRegression
+from sklearn.base import BaseEstimator, ClassifierMixin
+
+optuna.logging.set_verbosity(optuna.logging.WARNING)
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATA_PATH = os.path.join(AI_ENGINE_DIR, "data", "training_data.csv")
+MODELS_DIR = os.path.join(AI_ENGINE_DIR, "models", "v25")
+REPORTS_DIR = os.path.join(AI_ENGINE_DIR, "reports", "training_v25")
+
+os.makedirs(MODELS_DIR, exist_ok=True)
+os.makedirs(REPORTS_DIR, exist_ok=True)
+
+# ─── Feature Columns (95 features, NO target leakage) ───────────────
+FEATURES = [
+    # ELO (8)
+    "home_overall_elo", "away_overall_elo", "elo_diff",
+    "home_home_elo", "away_away_elo",
+    "home_form_elo", "away_form_elo", "form_elo_diff",
+    # Form (12)
+    "home_goals_avg", "home_conceded_avg",
+    "away_goals_avg", "away_conceded_avg",
+    "home_clean_sheet_rate", "away_clean_sheet_rate",
+    "home_scoring_rate", "away_scoring_rate",
+    "home_winning_streak", "away_winning_streak",
+    "home_unbeaten_streak", "away_unbeaten_streak",
+    # H2H (6)
+    "h2h_total_matches", "h2h_home_win_rate", "h2h_draw_rate",
+    "h2h_avg_goals", "h2h_btts_rate", "h2h_over25_rate",
+    # Team Stats (8)
+    "home_avg_possession", "away_avg_possession",
+    "home_avg_shots_on_target", "away_avg_shots_on_target",
+    "home_shot_conversion", "away_shot_conversion",
+    "home_avg_corners", "away_avg_corners",
+    # Odds (24 + 20 presence flags)
+    "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",
+    # League (4)
+    "home_xga", "away_xga",
+    "league_avg_goals", "league_zero_goal_rate",
+    # Upset Engine (4)
+    "upset_atmosphere", "upset_motivation", "upset_fatigue", "upset_potential",
+    # Referee Engine (5)
+    "referee_home_bias", "referee_avg_goals", "referee_cards_total",
+    "referee_avg_yellow", "referee_experience",
+    # Momentum (3)
+    "home_momentum_score", "away_momentum_score", "momentum_diff",
+    # Squad (9)
+    "home_squad_quality", "away_squad_quality", "squad_diff",
+    "home_key_players", "away_key_players",
+    "home_missing_impact", "away_missing_impact",
+    "home_goals_form", "away_goals_form",
+    # Player-Level Features (12)
+    "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",
+]
+
+MARKET_CONFIGS = [
+    {"target": "label_ms", "name": "MS", "num_class": 3},
+    {"target": "label_ou15", "name": "OU15", "num_class": 2},
+    {"target": "label_ou25", "name": "OU25", "num_class": 2},
+    {"target": "label_ou35", "name": "OU35", "num_class": 2},
+    {"target": "label_btts", "name": "BTTS", "num_class": 2},
+    {"target": "label_ht_result", "name": "HT_RESULT", "num_class": 3},
+    {"target": "label_ht_ou05", "name": "HT_OU05", "num_class": 2},
+    {"target": "label_ht_ou15", "name": "HT_OU15", "num_class": 2},
+    {"target": "label_ht_ft", "name": "HTFT", "num_class": 9},
+    {"target": "label_odd_even", "name": "ODD_EVEN", "num_class": 2},
+    {"target": "label_cards_ou45", "name": "CARDS_OU45", "num_class": 2},
+    {"target": "label_handicap_ms", "name": "HANDICAP_MS", "num_class": 3},
+]
+
+
+def load_data():
+    """Load and prepare training data."""
+    if not os.path.exists(DATA_PATH):
+        print(f"[ERROR] Data not found: {DATA_PATH}")
+        sys.exit(1)
+
+    print(f"[INFO] Loading {DATA_PATH}...")
+    df = pd.read_csv(DATA_PATH)
+
+    for col in FEATURES:
+        if col in df.columns:
+            df[col] = df[col].fillna(0)
+
+    # Derive odds presence flags for older CSVs
+    odds_flag_sources = {
+        "odds_ms_h_present": "odds_ms_h", "odds_ms_d_present": "odds_ms_d",
+        "odds_ms_a_present": "odds_ms_a", "odds_ht_ms_h_present": "odds_ht_ms_h",
+        "odds_ht_ms_d_present": "odds_ht_ms_d", "odds_ht_ms_a_present": "odds_ht_ms_a",
+        "odds_ou05_o_present": "odds_ou05_o", "odds_ou05_u_present": "odds_ou05_u",
+        "odds_ou15_o_present": "odds_ou15_o", "odds_ou15_u_present": "odds_ou15_u",
+        "odds_ou25_o_present": "odds_ou25_o", "odds_ou25_u_present": "odds_ou25_u",
+        "odds_ou35_o_present": "odds_ou35_o", "odds_ou35_u_present": "odds_ou35_u",
+        "odds_ht_ou05_o_present": "odds_ht_ou05_o", "odds_ht_ou05_u_present": "odds_ht_ou05_u",
+        "odds_ht_ou15_o_present": "odds_ht_ou15_o", "odds_ht_ou15_u_present": "odds_ht_ou15_u",
+        "odds_btts_y_present": "odds_btts_y", "odds_btts_n_present": "odds_btts_n",
+    }
+    for flag_col, odds_col in odds_flag_sources.items():
+        if flag_col not in df.columns:
+            df[flag_col] = (
+                pd.to_numeric(df.get(odds_col, 0), errors="coerce").fillna(0) > 1.01
+            ).astype(float)
+
+    print(f"[INFO] Shape: {df.shape}, Features: {len(FEATURES)}")
+    return df
+
+
+def temporal_split_4way(valid_df: pd.DataFrame):
+    """Chronological 60/15/10/15 split: train/val/cal/test."""
+    ordered = valid_df.sort_values("mst_utc").reset_index(drop=True)
+    n = len(ordered)
+    i1 = int(n * 0.60)
+    i2 = int(n * 0.75)
+    i3 = int(n * 0.85)
+
+    train = ordered.iloc[:i1].copy()
+    val = ordered.iloc[i1:i2].copy()
+    cal = ordered.iloc[i2:i3].copy()
+    test = ordered.iloc[i3:].copy()
+
+    return train, val, cal, test
+
+
+# ─── XGBoost Wrapper for sklearn CalibratedClassifierCV ─────────────
+class XGBWrapper(BaseEstimator, ClassifierMixin):
+    """Thin sklearn-compatible wrapper around xgb.train for Isotonic calibration."""
+
+    def __init__(self, params, num_boost_round=500):
+        self.params = params
+        self.num_boost_round = num_boost_round
+        self.model_ = None
+        self.classes_ = None
+
+    def fit(self, X, y, **kwargs):
+        self.classes_ = np.unique(y)
+        dtrain = xgb.DMatrix(X, label=y)
+        self.model_ = xgb.train(self.params, dtrain, num_boost_round=self.num_boost_round)
+        return self
+
+    def predict_proba(self, X):
+        dm = xgb.DMatrix(X)
+        probs = self.model_.predict(dm)
+        if len(probs.shape) == 1:
+            probs = np.column_stack([1 - probs, probs])
+        return probs
+
+    def predict(self, X):
+        return np.argmax(self.predict_proba(X), axis=1)
+
+
+# ─── Optuna Objectives ──────────────────────────────────────────────
+def xgb_objective(trial, X_train, y_train, X_val, y_val, num_class):
+    params = {
+        "objective": "multi:softprob" if num_class > 2 else "binary:logistic",
+        "eval_metric": "mlogloss" if num_class > 2 else "logloss",
+        "max_depth": trial.suggest_int("max_depth", 3, 8),
+        "eta": trial.suggest_float("eta", 0.01, 0.15, log=True),
+        "subsample": trial.suggest_float("subsample", 0.6, 1.0),
+        "colsample_bytree": trial.suggest_float("colsample_bytree", 0.5, 1.0),
+        "min_child_weight": trial.suggest_int("min_child_weight", 1, 10),
+        "gamma": trial.suggest_float("gamma", 1e-8, 1.0, log=True),
+        "reg_lambda": trial.suggest_float("reg_lambda", 1e-8, 10.0, log=True),
+        "reg_alpha": trial.suggest_float("reg_alpha", 1e-8, 1.0, log=True),
+        "n_jobs": 4,
+        "random_state": 42,
+    }
+    if num_class > 2:
+        params["num_class"] = num_class
+
+    dtrain = xgb.DMatrix(X_train, label=y_train)
+    dval = xgb.DMatrix(X_val, label=y_val)
+
+    model = xgb.train(
+        params, dtrain, num_boost_round=1000,
+        evals=[(dval, "val")], early_stopping_rounds=50, verbose_eval=False,
+    )
+
+    preds = model.predict(dval)
+    if len(preds.shape) == 1:
+        preds = np.column_stack([1 - preds, preds])
+
+    return log_loss(y_val, preds)
+
+
+def lgb_objective(trial, X_train, y_train, X_val, y_val, num_class):
+    params = {
+        "objective": "multiclass" if num_class > 2 else "binary",
+        "metric": "multi_logloss" if num_class > 2 else "binary_logloss",
+        "max_depth": trial.suggest_int("max_depth", 3, 8),
+        "learning_rate": trial.suggest_float("learning_rate", 0.01, 0.15, log=True),
+        "feature_fraction": trial.suggest_float("feature_fraction", 0.5, 1.0),
+        "bagging_fraction": trial.suggest_float("bagging_fraction", 0.6, 1.0),
+        "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
+        "min_child_samples": trial.suggest_int("min_child_samples", 5, 50),
+        "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 1.0, log=True),
+        "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True),
+        "n_jobs": 4, "random_state": 42, "verbose": -1,
+    }
+    if num_class > 2:
+        params["num_class"] = num_class
+
+    train_data = lgb.Dataset(X_train, label=y_train)
+    val_data = lgb.Dataset(X_val, label=y_val, reference=train_data)
+
+    model = lgb.train(
+        params, train_data, num_boost_round=1000,
+        valid_sets=[val_data], valid_names=["val"],
+        callbacks=[lgb.early_stopping(50), lgb.log_evaluation(0)],
+    )
+
+    preds = model.predict(X_val, num_iteration=model.best_iteration)
+    if len(preds.shape) == 1:
+        preds = np.column_stack([1 - preds, preds])
+
+    return log_loss(y_val, preds)
+
+
+# ─── Main Training Pipeline ─────────────────────────────────────────
+def train_market(df, target_col, market_name, num_class, n_trials):
+    """Full pipeline for one market: Optuna → Train → Calibrate → Evaluate."""
+    print(f"\n{'='*60}")
+    print(f"[MARKET] {market_name} (classes={num_class})")
+    print(f"{'='*60}")
+
+    valid_df = df[df[target_col].notna()].copy()
+    valid_df = valid_df[valid_df[target_col].astype(str) != ""].copy()
+    print(f"[INFO] Valid samples: {len(valid_df)}")
+
+    if len(valid_df) < 500:
+        print(f"[SKIP] Not enough data for {market_name}")
+        return None
+
+    available_features = [f for f in FEATURES if f in valid_df.columns]
+    print(f"[INFO] Features: {len(available_features)}/{len(FEATURES)}")
+
+    train_df, val_df, cal_df, test_df = temporal_split_4way(valid_df)
+    X_train = train_df[available_features].values
+    X_val = val_df[available_features].values
+    X_cal = cal_df[available_features].values
+    X_test = test_df[available_features].values
+    y_train = train_df[target_col].astype(int).values
+    y_val = val_df[target_col].astype(int).values
+    y_cal = cal_df[target_col].astype(int).values
+    y_test = test_df[target_col].astype(int).values
+
+    print(f"[INFO] Split: train={len(X_train)} val={len(X_val)} cal={len(X_cal)} test={len(X_test)}")
+
+    # ── Phase 1: Optuna XGBoost ──────────────────────────────────
+    print(f"\n[OPTUNA] XGBoost tuning ({n_trials} trials)...")
+    xgb_study = optuna.create_study(direction="minimize", sampler=TPESampler(seed=42))
+    xgb_study.optimize(
+        lambda trial: xgb_objective(trial, X_train, y_train, X_val, y_val, num_class),
+        n_trials=n_trials,
+    )
+    xgb_best = xgb_study.best_params
+    print(f"[OK] XGB best logloss: {xgb_study.best_value:.4f}")
+
+    # ── Phase 2: Optuna LightGBM ─────────────────────────────────
+    print(f"[OPTUNA] LightGBM tuning ({n_trials} trials)...")
+    lgb_study = optuna.create_study(direction="minimize", sampler=TPESampler(seed=42))
+    lgb_study.optimize(
+        lambda trial: lgb_objective(trial, X_train, y_train, X_val, y_val, num_class),
+        n_trials=n_trials,
+    )
+    lgb_best = lgb_study.best_params
+    print(f"[OK] LGB best logloss: {lgb_study.best_value:.4f}")
+
+    # ── Phase 3: Train final models with best params ─────────────
+    # XGBoost final
+    xgb_params = {
+        "objective": "multi:softprob" if num_class > 2 else "binary:logistic",
+        "eval_metric": "mlogloss" if num_class > 2 else "logloss",
+        "n_jobs": 4, "random_state": 42,
+        **{k: v for k, v in xgb_best.items()},
+    }
+    if num_class > 2:
+        xgb_params["num_class"] = num_class
+
+    dtrain = xgb.DMatrix(X_train, label=y_train)
+    dval = xgb.DMatrix(X_val, label=y_val)
+    xgb_model = xgb.train(
+        xgb_params, dtrain, num_boost_round=1500,
+        evals=[(dtrain, "train"), (dval, "val")],
+        early_stopping_rounds=80, verbose_eval=200,
+    )
+    print(f"[OK] XGB final: iter={xgb_model.best_iteration}, score={xgb_model.best_score:.4f}")
+
+    # LightGBM final
+    lgb_params = {
+        "objective": "multiclass" if num_class > 2 else "binary",
+        "metric": "multi_logloss" if num_class > 2 else "binary_logloss",
+        "n_jobs": 4, "random_state": 42, "verbose": -1,
+        **{k: v for k, v in lgb_best.items()},
+    }
+    if num_class > 2:
+        lgb_params["num_class"] = num_class
+
+    lgb_train_data = lgb.Dataset(X_train, label=y_train)
+    lgb_val_data = lgb.Dataset(X_val, label=y_val, reference=lgb_train_data)
+    lgb_model = lgb.train(
+        lgb_params, lgb_train_data, num_boost_round=1500,
+        valid_sets=[lgb_train_data, lgb_val_data],
+        valid_names=["train", "val"],
+        callbacks=[lgb.early_stopping(80), lgb.log_evaluation(200)],
+    )
+    print(f"[OK] LGB final: iter={lgb_model.best_iteration}")
+
+    # ── Phase 4: Isotonic Calibration on cal set ─────────────────
+    print("[CAL] Fitting Isotonic Regression (per-class)...")
+
+    # XGB calibration — manual IsotonicRegression per class
+    dcal = xgb.DMatrix(X_cal)
+    xgb_cal_raw = xgb_model.predict(dcal)
+    if len(xgb_cal_raw.shape) == 1:
+        xgb_cal_raw = np.column_stack([1 - xgb_cal_raw, xgb_cal_raw])
+
+    xgb_iso_calibrators = []
+    for cls_idx in range(num_class):
+        ir = IsotonicRegression(out_of_bounds="clip")
+        y_binary = (y_cal == cls_idx).astype(float)
+        ir.fit(xgb_cal_raw[:, cls_idx], y_binary)
+        xgb_iso_calibrators.append(ir)
+    print(f"[OK] XGB Isotonic calibrators fitted: {num_class} classes")
+
+    # LGB calibration — manual IsotonicRegression per class
+    lgb_cal_raw = lgb_model.predict(X_cal, num_iteration=lgb_model.best_iteration)
+    if len(lgb_cal_raw.shape) == 1:
+        lgb_cal_raw = np.column_stack([1 - lgb_cal_raw, lgb_cal_raw])
+
+    lgb_iso_calibrators = []
+    for cls_idx in range(num_class):
+        ir = IsotonicRegression(out_of_bounds="clip")
+        y_binary = (y_cal == cls_idx).astype(float)
+        ir.fit(lgb_cal_raw[:, cls_idx], y_binary)
+        lgb_iso_calibrators.append(ir)
+    print(f"[OK] LGB Isotonic calibrators fitted: {num_class} classes")
+
+    # ── Phase 5: Evaluate on test set ────────────────────────────
+    print("\n[EVAL] Test set evaluation...")
+    dtest = xgb.DMatrix(X_test)
+
+    # Raw XGB
+    xgb_raw_probs = xgb_model.predict(dtest)
+    if len(xgb_raw_probs.shape) == 1:
+        xgb_raw_probs = np.column_stack([1 - xgb_raw_probs, xgb_raw_probs])
+
+    # Calibrated XGB — apply isotonic per class + renormalize
+    xgb_cal_probs = np.column_stack([
+        xgb_iso_calibrators[i].predict(xgb_raw_probs[:, i]) for i in range(num_class)
+    ])
+    xgb_cal_probs = xgb_cal_probs / xgb_cal_probs.sum(axis=1, keepdims=True)
+
+    # Raw LGB
+    lgb_raw_probs = lgb_model.predict(X_test, num_iteration=lgb_model.best_iteration)
+    if len(lgb_raw_probs.shape) == 1:
+        lgb_raw_probs = np.column_stack([1 - lgb_raw_probs, lgb_raw_probs])
+
+    # Calibrated LGB — apply isotonic per class + renormalize
+    lgb_cal_probs = np.column_stack([
+        lgb_iso_calibrators[i].predict(lgb_raw_probs[:, i]) for i in range(num_class)
+    ])
+    lgb_cal_probs = lgb_cal_probs / lgb_cal_probs.sum(axis=1, keepdims=True)
+
+    # Ensembles
+    raw_ensemble = (xgb_raw_probs + lgb_raw_probs) / 2
+    cal_ensemble = (xgb_cal_probs + lgb_cal_probs) / 2
+
+    def _eval(probs, label):
+        preds = np.argmax(probs, axis=1)
+        acc = accuracy_score(y_test, preds)
+        ll = log_loss(y_test, probs)
+        print(f"  {label}: Acc={acc:.4f} LogLoss={ll:.4f}")
+        return {"accuracy": round(float(acc), 4), "logloss": round(float(ll), 4)}
+
+    m_xgb_raw = _eval(xgb_raw_probs, "XGB Raw")
+    m_xgb_cal = _eval(xgb_cal_probs, "XGB Calibrated")
+    m_lgb_raw = _eval(lgb_raw_probs, "LGB Raw")
+    m_lgb_cal = _eval(lgb_cal_probs, "LGB Calibrated")
+    m_ensemble = _eval(raw_ensemble, "Ensemble Raw")
+    m_cal_ensemble = _eval(cal_ensemble, "Ensemble Calibrated")
+
+    # Classification report for ensemble
+    ens_preds = np.argmax(raw_ensemble, axis=1)
+    print(f"\n[REPORT] Ensemble Classification Report:")
+    print(classification_report(y_test, ens_preds))
+
+    # ── Phase 6: Save models ─────────────────────────────────────
+    # Raw models (orchestrator compatible)
+    xgb_path = os.path.join(MODELS_DIR, f"xgb_v25_{market_name.lower()}.json")
+    xgb_model.save_model(xgb_path)
+    print(f"[SAVE] {xgb_path}")
+
+    lgb_path = os.path.join(MODELS_DIR, f"lgb_v25_{market_name.lower()}.txt")
+    lgb_model.save_model(lgb_path)
+    print(f"[SAVE] {lgb_path}")
+
+    # Isotonic calibrators (XGB + LGB)
+    xgb_cal_path = os.path.join(MODELS_DIR, f"iso_xgb_v25_{market_name.lower()}.pkl")
+    with open(xgb_cal_path, "wb") as f:
+        pickle.dump(xgb_iso_calibrators, f)
+    print(f"[SAVE] {xgb_cal_path}")
+
+    lgb_cal_path = os.path.join(MODELS_DIR, f"iso_lgb_v25_{market_name.lower()}.pkl")
+    with open(lgb_cal_path, "wb") as f:
+        pickle.dump(lgb_iso_calibrators, f)
+    print(f"[SAVE] {lgb_cal_path}")
+
+    return {
+        "market": market_name,
+        "samples": int(len(valid_df)),
+        "train": int(len(X_train)),
+        "val": int(len(X_val)),
+        "cal": int(len(X_cal)),
+        "test": int(len(X_test)),
+        "features_used": len(available_features),
+        "xgb_best_params": xgb_best,
+        "lgb_best_params": lgb_best,
+        "xgb_best_iteration": int(xgb_model.best_iteration),
+        "lgb_best_iteration": int(lgb_model.best_iteration),
+        "xgb_optuna_best_logloss": round(float(xgb_study.best_value), 4),
+        "lgb_optuna_best_logloss": round(float(lgb_study.best_value), 4),
+        "test_xgb_raw": m_xgb_raw,
+        "test_xgb_calibrated": m_xgb_cal,
+        "test_lgb_raw": m_lgb_raw,
+        "test_lgb_calibrated": m_lgb_cal,
+        "test_ensemble_raw": m_ensemble,
+        "test_ensemble_calibrated": m_cal_ensemble,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="V25 Pro Trainer")
+    parser.add_argument("--markets", type=str, default=None,
+                        help="Comma-separated market names (e.g., MS,OU25,BTTS)")
+    parser.add_argument("--trials", type=int, default=50,
+                        help="Optuna trials per model per market")
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print("V25 PRO — Optuna + Isotonic Calibration")
+    print("=" * 60)
+    print(f"[INFO] Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print(f"[INFO] Trials per model: {args.trials}")
+    print(f"[INFO] Total features: {len(FEATURES)}")
+
+    df = load_data()
+
+    configs = MARKET_CONFIGS
+    if args.markets:
+        selected = [m.strip().upper() for m in args.markets.split(",")]
+        configs = [c for c in configs if c["name"] in selected]
+        print(f"[INFO] Selected markets: {[c['name'] for c in configs]}")
+
+    all_metrics = {
+        "trained_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "trainer": "v25_pro",
+        "optuna_trials": args.trials,
+        "total_features": len(FEATURES),
+        "markets": {},
+    }
+
+    for config in configs:
+        target = config["target"]
+        if target not in df.columns:
+            print(f"[SKIP] {config['name']}: missing target {target}")
+            continue
+
+        metrics = train_market(
+            df, target, config["name"], config["num_class"], args.trials,
+        )
+        if metrics:
+            all_metrics["markets"][config["name"]] = metrics
+
+    # Save feature list
+    feature_path = os.path.join(MODELS_DIR, "feature_cols.json")
+    with open(feature_path, "w") as f:
+        json.dump(FEATURES, f, indent=2)
+
+    # Save full report
+    report_path = os.path.join(REPORTS_DIR, "v25_pro_metrics.json")
+    with open(report_path, "w") as f:
+        json.dump(all_metrics, f, indent=2, default=str)
+    print(f"\n[SAVE] Report: {report_path}")
+
+    # Summary
+    print("\n" + "=" * 60)
+    print("[SUMMARY]")
+    print("=" * 60)
+    for name, m in all_metrics["markets"].items():
+        ens = m.get("test_ensemble_calibrated", m.get("test_ensemble_raw", {}))
+        acc = ens.get('accuracy', '?')
+        ll = ens.get('logloss', '?')
+        acc_s = f"{acc:.4f}" if isinstance(acc, float) else str(acc)
+        ll_s = f"{ll:.4f}" if isinstance(ll, float) else str(ll)
+        print(f"  {name:12s} | Acc={acc_s:>6s} | LL={ll_s:>6s} | "
+              f"XGB_iter={m.get('xgb_best_iteration','?')} LGB_iter={m.get('lgb_best_iteration','?')}")
+
+    print(f"\n[INFO] Completed: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("[OK] V25 PRO Training Complete!")
+
+
+if __name__ == "__main__":
+    main()

ai-engine/scripts/train_v27_value_sniper.py

@@ -20,7 +20,7 @@ from sklearn.isotonic import IsotonicRegression
 warnings.filterwarnings("ignore")
 
 AI_DIR = Path(__file__).resolve().parent.parent
-DATA_CSV = AI_DIR / "data" / "training_data_v27.csv"
+DATA_CSV = AI_DIR / "data" / "training_data.csv"
 MODELS_DIR = AI_DIR / "models" / "v27"
 MODELS_DIR.mkdir(parents=True, exist_ok=True)
 
@@ -373,15 +373,52 @@ def main():
         print("\n" + "─"*65)
         print("  STAGE A.2: Fundamentals-Only O/U 2.5 Model")
         print("─"*65)
-        y_tr_ou = tr["label_ou25"].values
+        y_tr_ou = tr['label_ou25'].values
-        y_va_ou = va["label_ou25"].values
+        y_va_ou = va['label_ou25'].values
         mask_tr = ~np.isnan(y_tr_ou)
         mask_va = ~np.isnan(y_va_ou)
         if mask_tr.sum() > 1000:
             ou_models = train_fundamentals_model(
                 X_tr[mask_tr], y_tr_ou[mask_tr].astype(int),
                 X_va[mask_va], y_va_ou[mask_va].astype(int),
-                clean_feats, "ou25")
+                clean_feats, 'ou25')
+
+    # ── STAGE A.3: BTTS Model ──
+    btts_models = None
+    if 'label_btts' in tr.columns:
+        print('\n' + '─' * 65)
+        print('  STAGE A.3: Fundamentals-Only BTTS Model')
+        print('─' * 65)
+        y_tr_btts = tr['label_btts'].values
+        y_va_btts = va['label_btts'].values
+        mask_tr_btts = ~np.isnan(y_tr_btts)
+        mask_va_btts = ~np.isnan(y_va_btts)
+        if mask_tr_btts.sum() > 1000:
+            btts_models = train_fundamentals_model(
+                X_tr[mask_tr_btts], y_tr_btts[mask_tr_btts].astype(int),
+                X_va[mask_va_btts], y_va_btts[mask_va_btts].astype(int),
+                clean_feats, 'btts')
+
+            # Quick val accuracy
+            btts_probs = ensemble_predict(
+                btts_models,
+                X_va[mask_va_btts],
+                clean_feats,
+                n_class=2,
+            )
+            btts_acc = accuracy_score(
+                y_va_btts[mask_va_btts].astype(int),
+                btts_probs.argmax(1),
+            )
+            btts_ll = log_loss(
+                y_va_btts[mask_va_btts].astype(int),
+                btts_probs,
+            )
+            print(f'\n  BTTS Ensemble Val: acc={btts_acc:.4f}, logloss={btts_ll:.4f}')
+            # Compare with naive baseline (always predict majority class)
+            btts_majority = y_va_btts[mask_va_btts].astype(int).mean()
+            print(f'  BTTS baseline: {max(btts_majority, 1-btts_majority):.4f} (majority class)')
+            print(f'  Model vs baseline: {btts_acc - max(btts_majority, 1-btts_majority):+.4f}')
 
     # ── STAGE C: Backtest ──
     print("\n" + "─"*65)
@@ -422,13 +459,58 @@ def main():
 
     # OU25 backtest
     if ou_models:
-        print("\n  --- O/U 2.5 Backtest ---")
+        print('\n  --- O/U 2.5 Backtest ---')
         for edge in [0.05, 0.07, 0.10]:
-            r = backtest_value(ou_models, te, clean_feats, "ou25",
+            r = backtest_value(ou_models, te, clean_feats, 'ou25',
                                min_edge=edge, min_odds=1.50, max_odds=3.0,
                                use_kelly=True)
-            if r.get("total", 0) > 0:
+            if r.get('total', 0) > 0:
-                print_backtest(r, f"OU25 edge>{edge}")
+                print_backtest(r, f'OU25 edge>{edge}')
+
+    # BTTS backtest
+    if btts_models and 'label_btts' in te.columns:
+        print('\n  --- BTTS Backtest ---')
+        # Build BTTS odds for backtest
+        if 'odds_btts_y' in te.columns and 'odds_btts_n' in te.columns:
+            te_btts = te.copy()
+            te_btts['odds_btts_y'] = pd.to_numeric(
+                te_btts['odds_btts_y'], errors='coerce',
+            ).fillna(1.85)
+            te_btts['odds_btts_n'] = pd.to_numeric(
+                te_btts['odds_btts_n'], errors='coerce',
+            ).fillna(1.85)
+
+            for edge in [0.05, 0.07, 0.10]:
+                X_test = te_btts[clean_feats].values
+                probs = ensemble_predict(btts_models, X_test, clean_feats, 2)
+                y_btts = te_btts['label_btts'].values.astype(int)
+                odds_arr = te_btts[['odds_btts_n', 'odds_btts_y']].values
+                m_arr = 1 / odds_arr
+                impl = m_arr / m_arr.sum(axis=1, keepdims=True)
+
+                total_bets = 0
+                wins = 0
+                pnl = 0.0
+                for i in range(len(y_btts)):
+                    for cls in range(2):
+                        e = probs[i, cls] - impl[i, cls]
+                        o = odds_arr[i, cls]
+                        if e < edge or o < 1.50 or o > 3.0:
+                            continue
+                        total_bets += 1
+                        won = (y_btts[i] == cls)
+                        if won:
+                            wins += 1
+                            pnl += 10 * (o - 1)
+                        else:
+                            pnl -= 10
+                if total_bets > 0:
+                    roi = pnl / (total_bets * 10) * 100
+                    hit = wins / total_bets * 100
+                    print(
+                        f'    Edge>{edge:.2f}: {total_bets} bets, '
+                        f'hit={hit:.1f}%, ROI={roi:+.1f}%'
+                    )
 
     # ── Feature importance ──
     if "lgb" in ms_models:
@@ -452,25 +534,40 @@ def main():
 
     if ou_models:
         for name, m in ou_models.items():
-            p = MODELS_DIR / f"v27_ou25_{name}.pkl"
+            p = MODELS_DIR / f'v27_ou25_{name}.pkl'
-            with open(p, "wb") as f:
+            with open(p, 'wb') as f:
                 pickle.dump(m, f)
-            print(f"  ✓ {p.name}")
+            print(f'  ✓ {p.name}')
+
+    if btts_models:
+        for name, m in btts_models.items():
+            p = MODELS_DIR / f'v27_btts_{name}.pkl'
+            with open(p, 'wb') as f:
+                pickle.dump(m, f)
+            print(f'  ✓ {p.name}')
 
     meta = {
-        "version": "v27-pro", "trained_at": time.strftime("%Y-%m-%d %H:%M:%S"),
+        'version': 'v27-pro',
-        "approach": "odds-free fundamentals + value edge detection",
+        'trained_at': time.strftime('%Y-%m-%d %H:%M:%S'),
-        "feature_count": len(clean_feats),
+        'approach': 'odds-free fundamentals + value edge detection',
-        "total_samples": len(df),
+        'feature_count': len(clean_feats),
-        "val_acc": round(val_acc, 4), "val_ll": round(val_ll, 4),
+        'total_samples': len(df),
-        "best_config": {k: v for k, v in best_cfg.items() if k != "result"} if best_cfg else {},
+        'val_acc': round(val_acc, 4),
-        "markets": ["ms"] + (["ou25"] if ou_models else []),
+        'val_ll': round(val_ll, 4),
+        'best_config': {
+            k: v for k, v in best_cfg.items() if k != 'result'
+        } if best_cfg else {},
+        'markets': (
+            ['ms']
+            + (['ou25'] if ou_models else [])
+            + (['btts'] if btts_models else [])
+        ),
     }
-    with open(MODELS_DIR / "v27_metadata.json", "w") as f:
+    with open(MODELS_DIR / 'v27_metadata.json', 'w') as f:
         json.dump(meta, f, indent=2, default=str)
-    with open(MODELS_DIR / "v27_feature_cols.json", "w") as f:
+    with open(MODELS_DIR / 'v27_feature_cols.json', 'w') as f:
         json.dump(clean_feats, f, indent=2)
-    print(f"  ✓ metadata + feature_cols")
+    print(f'  ✓ metadata + feature_cols')
 
     print(f"\n  Total time: {(time.time()-t0)/60:.1f} min")
     print("  DONE!")

ai-engine/services/betting_brain.py

@@ -165,6 +165,11 @@ class BettingBrain:
             score -= 18.0
             issues.append("base_model_not_playable")
 
+        is_value_sniper = bool(row.get("is_value_sniper"))
+        if is_value_sniper:
+            score += 35.0
+            positives.append("value_sniper_override")
+
         score += max(0.0, min(20.0, calibrated_conf * 0.22))
         score += max(-8.0, min(16.0, ev_edge * 45.0))
         score += max(0.0, min(14.0, play_score * 0.12))
@@ -178,13 +183,13 @@ class BettingBrain:
 
         if odds < self.MIN_ODDS:
             vetoes.append("odds_below_minimum")
-        if calibrated_conf < 38.0:
+        if calibrated_conf < 38.0 and not is_value_sniper:
             vetoes.append("calibrated_confidence_too_low")
-        if play_score < 50.0:
+        if play_score < 50.0 and not is_value_sniper:
             vetoes.append("play_score_too_low")
 
         if divergence is not None:
-            if divergence >= self.HARD_DIVERGENCE:
+            if divergence >= self.HARD_DIVERGENCE and not is_value_sniper:
                 score -= 42.0
                 vetoes.append("v25_v27_hard_disagreement")
             elif divergence >= self.SOFT_DIVERGENCE:
@@ -211,7 +216,7 @@ class BettingBrain:
             else:
                 score -= 16.0
                 issues.append("historical_sample_too_low")
-                if market == "DC":
+                if market == "DC" and not is_value_sniper:
                     vetoes.append("dc_without_historical_sample")
         elif market in {"MS", "DC", "OU25"}:
             score -= 10.0
@@ -227,20 +232,21 @@ class BettingBrain:
             and model_prob >= self.EXTREME_MODEL_PROB
             and model_gap >= self.EXTREME_GAP
             and not triple_is_value
+            and not is_value_sniper
         ):
             score -= 24.0
             vetoes.append("extreme_probability_without_evidence")
 
-        if market in {"HT", "HTFT", "OE"} and score < 86.0:
+        if market in {"HT", "HTFT", "OE"} and score < 86.0 and not is_value_sniper:
             vetoes.append("volatile_market_requires_exceptional_evidence")
 
         score = max(0.0, min(100.0, score))
         action = "BET"
         if vetoes:
             action = "REJECT"
-        elif score < self.MIN_WATCH_SCORE:
+        elif score < self.MIN_WATCH_SCORE and not is_value_sniper:
             action = "REJECT"
-        elif score < self.MIN_BET_SCORE:
+        elif score < self.MIN_BET_SCORE and not is_value_sniper:
             action = "WATCH"
 
         row["betting_brain"] = {
@@ -276,6 +282,7 @@ class BettingBrain:
         for source in ("main_pick", "value_pick"):
             item = package.get(source)
             if isinstance(item, dict) and item.get("market"):
+                # print(f"DEBUG: {source} is_value_sniper: {item.get('is_value_sniper')}")
                 rows[self._row_key(item)] = dict(item)
 
         for source in ("supporting_picks", "bet_summary"):
@@ -283,6 +290,7 @@ class BettingBrain:
                 if isinstance(item, dict) and item.get("market"):
                     key = self._row_key(item)
                     rows[key] = self._merge_row(rows.get(key), item)
+        
         return list(rows.values())
 
     @staticmethod

ai-engine/services/feature_enrichment.py

@@ -14,11 +14,40 @@ is missing or queries fail.
 
 from __future__ import annotations
 
+import unicodedata
 from typing import Any, Dict, Optional, Tuple
 
 from psycopg2.extras import RealDictCursor
 
 
+# ─── Turkish Name Normalization ──────────────────────────────────
+
+_TR_CHAR_MAP = str.maketrans(
+    'çÇğĞıİöÖşŞüÜâÂîÎûÛ',
+    'cCgGiIoOsSuUaAiIuU',
+)
+
+
+def _normalize_name(name: str) -> str:
+    """
+    Normalize a Turkish referee name for fuzzy matching.
+
+    Strips accents, lowercases, removes extra whitespace, and maps
+    Turkish-specific characters to their ASCII equivalents.
+    """
+    if not name:
+        return ''
+    # 1. Turkish-specific character mapping
+    normalized = name.translate(_TR_CHAR_MAP)
+    # 2. Unicode NFKD decomposition → strip combining marks
+    normalized = unicodedata.normalize('NFKD', normalized)
+    normalized = ''.join(
+        c for c in normalized if not unicodedata.combining(c)
+    )
+    # 3. Lowercase + collapse whitespace
+    return ' '.join(normalized.lower().split())
+
+
 class FeatureEnrichmentService:
     """Stateless service — all state comes from DB via cursor."""
 
@@ -380,34 +409,20 @@ class FeatureEnrichmentService:
         """
         Referee tendencies: home win bias, avg goals, card rates.
         Matches referee by name in match_officials (role_id=1 = Orta Hakem).
+
+        Uses Turkish-aware fuzzy matching as a fallback when exact name
+        lookup returns zero results.
         """
         if not referee_name:
             return dict(self._DEFAULT_REFEREE)
-        try:
+
-            # Get match IDs officiated by this referee
+        rows = self._query_referee_matches(cur, referee_name, before_date_ms, limit)
-            cur.execute(
+
-                """
+        # Fuzzy fallback: if exact match fails, try normalized name search
-                SELECT
+        if not rows:
-                    m.home_team_id,
+            rows = self._fuzzy_referee_lookup(
-                    m.score_home,
+                cur, referee_name, before_date_ms, limit,
-                    m.score_away,
-                    m.id AS match_id
-                FROM match_officials mo
-                JOIN matches m ON m.id = mo.match_id
-                WHERE mo.name = %s
-                  AND mo.role_id = 1
-                  AND m.status = 'FT'
-                  AND m.score_home IS NOT NULL
-                  AND m.score_away IS NOT NULL
-                  AND m.mst_utc < %s
-                ORDER BY m.mst_utc DESC
-                LIMIT %s
-                """,
-                (referee_name, before_date_ms, limit),
             )
-            rows = cur.fetchall()
-        except Exception:
-            return dict(self._DEFAULT_REFEREE)
 
         if not rows:
             return dict(self._DEFAULT_REFEREE)
@@ -459,6 +474,118 @@ class FeatureEnrichmentService:
             'experience': total,
         }
 
+    def _query_referee_matches(
+        self,
+        cur: RealDictCursor,
+        referee_name: str,
+        before_date_ms: int,
+        limit: int,
+    ) -> list:
+        """Exact-match referee lookup in match_officials."""
+        try:
+            cur.execute(
+                """
+                SELECT
+                    m.home_team_id,
+                    m.score_home,
+                    m.score_away,
+                    m.id AS match_id
+                FROM match_officials mo
+                JOIN matches m ON m.id = mo.match_id
+                WHERE mo.name = %s
+                  AND mo.role_id = 1
+                  AND m.status = 'FT'
+                  AND m.score_home IS NOT NULL
+                  AND m.score_away IS NOT NULL
+                  AND m.mst_utc < %s
+                ORDER BY m.mst_utc DESC
+                LIMIT %s
+                """,
+                (referee_name, before_date_ms, limit),
+            )
+            return cur.fetchall()
+        except Exception:
+            return []
+
+    def _fuzzy_referee_lookup(
+        self,
+        cur: RealDictCursor,
+        referee_name: str,
+        before_date_ms: int,
+        limit: int,
+    ) -> list:
+        """
+        Fuzzy referee lookup using Turkish name normalization.
+
+        Strategy: fetch recent distinct referee names from match_officials,
+        normalize both the query name and each candidate, and pick the
+        best match.  This handles common mismatches like:
+          - 'Hüseyin Göçek' vs 'Huseyin Gocek'
+          - 'Ali Palabıyık' vs 'Ali Palabiyik'
+          - Extra/missing middle initials
+        """
+        normalized_query = _normalize_name(referee_name)
+        if not normalized_query:
+            return []
+
+        try:
+            # Fetch candidate referee names (distinct, recent, role=1)
+            cur.execute(
+                """
+                SELECT DISTINCT mo.name
+                FROM match_officials mo
+                JOIN matches m ON m.id = mo.match_id
+                WHERE mo.role_id = 1
+                  AND m.status = 'FT'
+                  AND m.mst_utc < %s
+                ORDER BY mo.name
+                LIMIT 2000
+                """,
+                (before_date_ms,),
+            )
+            candidates = cur.fetchall()
+        except Exception:
+            return []
+
+        if not candidates:
+            return []
+
+        # Find best match by normalized name comparison
+        best_match: Optional[str] = None
+        best_score = 0.0
+
+        for cand_row in candidates:
+            cand_name = cand_row.get('name', '')
+            if not cand_name:
+                continue
+            normalized_cand = _normalize_name(cand_name)
+
+            # Exact normalized match
+            if normalized_cand == normalized_query:
+                best_match = cand_name
+                best_score = 1.0
+                break
+
+            # Substring containment (handles "First Last" vs "First M. Last")
+            if (
+                normalized_query in normalized_cand
+                or normalized_cand in normalized_query
+            ):
+                containment_score = min(
+                    len(normalized_query), len(normalized_cand)
+                ) / max(len(normalized_query), len(normalized_cand))
+                if containment_score > best_score and containment_score > 0.6:
+                    best_match = cand_name
+                    best_score = containment_score
+
+        if not best_match:
+            return []
+
+        # Re-query with the resolved name
+        return self._query_referee_matches(
+            cur, best_match, before_date_ms, limit,
+        )
+
     # ─── 5. League Averages ─────────────────────────────────────────
 
     def compute_league_averages(

ai-engine/services/match_commentary.py

@@ -0,0 +1,367 @@
+"""
+Match Commentary Generator
+===========================
+Generates human-readable Turkish commentary from the analysis package.
+Reads all engine signals (model, odds band, betting brain, triple value)
+and produces a clear, actionable summary for end users.
+
+No LLM required — fully template-based.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+
+def generate_match_commentary(package: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Main entry point. Takes a full analysis package and returns a commentary dict.
+
+    Returns:
+        {
+            "action": "BET" | "WATCH" | "SKIP",
+            "headline": "...",
+            "summary": "...",
+            "notes": ["...", "..."],
+            "contradictions": ["...", "..."],
+            "confidence_label": "YÜKSEK" | "ORTA" | "DÜŞÜK" | "ÇOK DÜŞÜK"
+        }
+    """
+    match_info = package.get("match_info") or {}
+    home = match_info.get("home_team", "Ev Sahibi")
+    away = match_info.get("away_team", "Deplasman")
+    main_pick = package.get("main_pick") or {}
+    betting_brain = package.get("betting_brain") or {}
+    v27_engine = package.get("v27_engine") or {}
+    market_board = package.get("market_board") or {}
+    score_pred = package.get("score_prediction") or {}
+    risk = package.get("risk") or {}
+    data_quality = package.get("data_quality") or {}
+
+    # ── Determine action ──────────────────────────────────────────
+    brain_decision = str(betting_brain.get("decision") or "NO_BET").upper()
+    main_playable = bool(main_pick.get("playable"))
+    main_vetoed = bool((main_pick.get("upper_brain") or {}).get("veto"))
+    approved_count = int(betting_brain.get("approved_count", 0) or 0)
+
+    if main_playable and not main_vetoed and approved_count > 0:
+        action = "BET"
+    elif approved_count == 0 and brain_decision == "NO_BET":
+        action = "SKIP"
+    else:
+        action = "WATCH"
+
+    # ── Headline ──────────────────────────────────────────────────
+    headline = _build_headline(action, main_pick, home, away)
+
+    # ── Summary paragraph ─────────────────────────────────────────
+    summary = _build_summary(
+        action, main_pick, market_board, v27_engine,
+        score_pred, risk, data_quality, home, away,
+    )
+
+    # ── Quick notes ───────────────────────────────────────────────
+    notes = _build_notes(market_board, v27_engine, score_pred, risk, home, away)
+
+    # ── Contradiction detection ───────────────────────────────────
+    contradictions = _detect_contradictions(market_board, v27_engine, package)
+
+    # ── Overall confidence label ──────────────────────────────────
+    confidence_label = _overall_confidence_label(main_pick, data_quality)
+
+    return {
+        "action": action,
+        "headline": headline,
+        "summary": summary,
+        "notes": notes[:6],
+        "contradictions": contradictions[:4],
+        "confidence_label": confidence_label,
+    }
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Headline
+# ═══════════════════════════════════════════════════════════════════════
+
+def _build_headline(
+    action: str,
+    main_pick: Dict[str, Any],
+    home: str,
+    away: str,
+) -> str:
+    if action == "BET":
+        market = main_pick.get("market", "")
+        pick = main_pick.get("pick", "")
+        odds = main_pick.get("odds", 0.0)
+        conf = main_pick.get("calibrated_confidence", main_pick.get("confidence", 0))
+        market_tr = _market_to_turkish(market, pick)
+        return f"🎯 {market_tr} önerisi — Oran: {odds}, Güven: %{conf:.0f}"
+
+    if action == "WATCH":
+        return f"👀 {home} vs {away} — İzlemeye değer sinyaller var"
+
+    return f"⚠️ {home} vs {away} — Şu an net bir fırsat görülmüyor"
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Summary
+# ═══════════════════════════════════════════════════════════════════════
+
+def _build_summary(
+    action: str,
+    main_pick: Dict[str, Any],
+    market_board: Dict[str, Any],
+    v27_engine: Dict[str, Any],
+    score_pred: Dict[str, Any],
+    risk: Dict[str, Any],
+    data_quality: Dict[str, Any],
+    home: str,
+    away: str,
+) -> str:
+    parts: List[str] = []
+
+    # Who is the favourite?
+    ms_board = market_board.get("MS") or {}
+    ms_pick = ms_board.get("pick", "")
+    ms_conf = float(ms_board.get("confidence", 50) or 50)
+
+    if ms_pick == "1" and ms_conf > 45:
+        parts.append(f"{home} hafif favori görünüyor")
+    elif ms_pick == "1" and ms_conf > 55:
+        parts.append(f"{home} net favori")
+    elif ms_pick == "2" and ms_conf > 45:
+        parts.append(f"{away} hafif favori görünüyor")
+    elif ms_pick == "2" and ms_conf > 55:
+        parts.append(f"{away} net favori")
+    else:
+        parts.append("İki takım da birbirine yakın güçte")
+
+    # xG expectation
+    xg_home = float(score_pred.get("xg_home", 0) or 0)
+    xg_away = float(score_pred.get("xg_away", 0) or 0)
+    xg_total = xg_home + xg_away
+    if xg_total > 3.0:
+        parts.append(f"Gol beklentisi yüksek (toplam xG: {xg_total:.1f})")
+    elif xg_total < 2.0:
+        parts.append(f"Düşük gol beklentisi (toplam xG: {xg_total:.1f})")
+
+    # Consensus check
+    consensus = str(v27_engine.get("consensus") or "").upper()
+    if consensus == "AGREE":
+        parts.append("Model motorları aynı fikirde")
+    elif consensus == "DISAGREE":
+        parts.append("Model motorları farklı sonuçlara ulaşıyor — belirsizlik var")
+
+    # Action-specific
+    if action == "BET":
+        market_tr = _market_to_turkish(
+            main_pick.get("market", ""), main_pick.get("pick", "")
+        )
+        edge = float(main_pick.get("ev_edge", 0) or 0)
+        parts.append(
+            f"{market_tr} yönünde değer tespit edildi (EV edge: {edge:+.1%})"
+        )
+    elif action == "SKIP":
+        parts.append(
+            "Hiçbir markette piyasanın fiyatlamadığı bir avantaj görülmüyor"
+        )
+
+    # Risk
+    risk_level = str(risk.get("level") or "MEDIUM").upper()
+    if risk_level == "HIGH":
+        parts.append("⚠️ Risk seviyesi yüksek")
+    elif risk_level == "EXTREME":
+        parts.append("🔴 Çok yüksek risk — dikkatli olun")
+
+    # Data quality
+    quality_label = str(data_quality.get("label") or "MEDIUM").upper()
+    if quality_label == "LOW":
+        parts.append("Veri kalitesi düşük — tahminler daha az güvenilir")
+
+    return ". ".join(parts) + "."
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Quick Notes
+# ═══════════════════════════════════════════════════════════════════════
+
+def _build_notes(
+    market_board: Dict[str, Any],
+    v27_engine: Dict[str, Any],
+    score_pred: Dict[str, Any],
+    risk: Dict[str, Any],
+    home: str,
+    away: str,
+) -> List[str]:
+    notes: List[str] = []
+    triple_value = v27_engine.get("triple_value") or {}
+    odds_band = v27_engine.get("odds_band") or {}
+
+    # MS note
+    ms = market_board.get("MS") or {}
+    ms_conf = float(ms.get("confidence", 0) or 0)
+    if ms_conf < 45:
+        notes.append("Maç sonucu belirsiz, net favori yok")
+    elif ms.get("pick") == "1":
+        notes.append(f"{home} favori ama oran değerli mi kontrol et")
+    elif ms.get("pick") == "2":
+        notes.append(f"{away} favori ama oran değerli mi kontrol et")
+
+    # OU25 note
+    ou25 = market_board.get("OU25") or {}
+    ou25_probs = ou25.get("probs") or {}
+    over_prob = float(ou25_probs.get("over", 0.5) or 0.5)
+    if over_prob > 0.58:
+        notes.append("2.5 Üst yönünde eğilim var")
+    elif over_prob < 0.42:
+        notes.append("2.5 Alt yönünde eğilim var")
+    else:
+        notes.append("2.5 Üst/Alt dengeli — kesin sinyal yok")
+
+    # BTTS note
+    btts = market_board.get("BTTS") or {}
+    btts_probs = btts.get("probs") or {}
+    btts_yes = float(btts_probs.get("yes", 0.5) or 0.5)
+    if btts_yes > 0.58:
+        notes.append("Her iki takımın da gol atması bekleniyor")
+    elif btts_yes < 0.42:
+        notes.append("KG olasılığı düşük")
+
+    # HT note
+    ht = market_board.get("HT") or {}
+    ht_pick = ht.get("pick", "")
+    ht_conf = float(ht.get("confidence", 0) or 0)
+    if ht_conf > 40 and ht_pick:
+        ht_label = {"1": f"İY {home}", "2": f"İY {away}", "X": "İY beraberlik"}.get(
+            ht_pick, f"İY {ht_pick}"
+        )
+        notes.append(f"{ht_label} yönünde hafif sinyal (%{ht_conf:.0f})")
+
+    # Risk warnings
+    warnings = risk.get("warnings") or []
+    for w in warnings[:2]:
+        notes.append(f"⚠️ {w}")
+
+    return notes
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Contradiction Detection
+# ═══════════════════════════════════════════════════════════════════════
+
+def _detect_contradictions(
+    market_board: Dict[str, Any],
+    v27_engine: Dict[str, Any],
+    package: Dict[str, Any],
+) -> List[str]:
+    """
+    Detect cases where model prediction and odds band/triple value
+    point in opposite directions — the user's main complaint.
+    """
+    contradictions: List[str] = []
+    triple_value = v27_engine.get("triple_value") or {}
+    predictions = v27_engine.get("predictions") or {}
+
+    # MS contradiction: model says home but triple_value says away has value
+    ms_preds = predictions.get("ms") or {}
+    ms_home = float(ms_preds.get("home", 0) or 0)
+    ms_away = float(ms_preds.get("away", 0) or 0)
+    home_triple = triple_value.get("home") or {}
+    away_triple = triple_value.get("away") or {}
+
+    model_favours_home = ms_home > ms_away
+    away_is_value = bool(away_triple.get("is_value"))
+    home_is_value = bool(home_triple.get("is_value"))
+
+    if model_favours_home and away_is_value:
+        contradictions.append(
+            "Model ev sahibini favori görüyor ama oran bandı deplasmanda değer buluyor — "
+            "bu çelişki nedeniyle MS tahminine dikkatli yaklaş"
+        )
+    elif not model_favours_home and home_is_value:
+        contradictions.append(
+            "Model deplasmanı favori görüyor ama oran bandı ev sahibinde değer buluyor — "
+            "bu çelişki nedeniyle MS tahminine dikkatli yaklaş"
+        )
+
+    # HT contradiction
+    ht_board = market_board.get("HT") or {}
+    ht_pick = ht_board.get("pick", "")
+    ht_home_triple = triple_value.get("ht_home") or {}
+    ht_away_triple = triple_value.get("ht_away") or {}
+
+    if ht_pick == "1" and bool(ht_away_triple.get("is_value")):
+        contradictions.append(
+            "Model İY ev sahibi diyor ama oran bandı İY deplasmanda değer buluyor — "
+            "İY tahmini güvenilir değil"
+        )
+    elif ht_pick == "2" and bool(ht_home_triple.get("is_value")):
+        contradictions.append(
+            "Model İY deplasman diyor ama oran bandı İY ev sahibinde değer buluyor — "
+            "İY tahmini güvenilir değil"
+        )
+
+    # OU25 contradiction
+    ou25_board = market_board.get("OU25") or {}
+    ou25_pick = ou25_board.get("pick", "")
+    ou25_over_triple = triple_value.get("ou25_over") or {}
+    ou25_under_triple = triple_value.get("ou25_under") or {}
+
+    if ou25_pick == "Üst" and bool(ou25_under_triple.get("is_value")):
+        contradictions.append(
+            "Model 2.5 Üst diyor ama oran bandı 2.5 Alt'ta değer buluyor — çelişki var"
+        )
+    elif ou25_pick == "Alt" and bool(ou25_over_triple.get("is_value")):
+        contradictions.append(
+            "Model 2.5 Alt diyor ama oran bandı 2.5 Üst'te değer buluyor — çelişki var"
+        )
+
+    return contradictions
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Helpers
+# ═══════════════════════════════════════════════════════════════════════
+
+def _overall_confidence_label(
+    main_pick: Dict[str, Any],
+    data_quality: Dict[str, Any],
+) -> str:
+    """Overall confidence label for the entire analysis."""
+    quality_score = float(data_quality.get("score", 0.5) or 0.5)
+    main_conf = float(
+        main_pick.get("calibrated_confidence", main_pick.get("confidence", 0)) or 0
+    )
+    main_playable = bool(main_pick.get("playable"))
+
+    if main_playable and main_conf >= 60 and quality_score >= 0.8:
+        return "YÜKSEK"
+    if main_playable and main_conf >= 45:
+        return "ORTA"
+    if main_conf >= 30:
+        return "DÜŞÜK"
+    return "ÇOK DÜŞÜK"
+
+
+_MARKET_TR_MAP = {
+    "MS": {"1": "Maç Sonucu Ev Sahibi", "2": "Maç Sonucu Deplasman", "X": "Beraberlik"},
+    "DC": {"1X": "Çifte Şans 1X", "X2": "Çifte Şans X2", "12": "Çifte Şans 12"},
+    "OU25": {"Üst": "2.5 Üst", "Alt": "2.5 Alt", "Over": "2.5 Üst", "Under": "2.5 Alt"},
+    "OU15": {"Üst": "1.5 Üst", "Alt": "1.5 Alt", "Over": "1.5 Üst", "Under": "1.5 Alt"},
+    "OU35": {"Üst": "3.5 Üst", "Alt": "3.5 Alt", "Over": "3.5 Üst", "Under": "3.5 Alt"},
+    "BTTS": {"KG Var": "Karşılıklı Gol Var", "KG Yok": "Karşılıklı Gol Yok",
+             "Yes": "Karşılıklı Gol Var", "No": "Karşılıklı Gol Yok"},
+    "HT": {"1": "İlk Yarı Ev Sahibi", "2": "İlk Yarı Deplasman", "X": "İlk Yarı Beraberlik"},
+    "HT_OU05": {"Üst": "İY 0.5 Üst", "Alt": "İY 0.5 Alt"},
+    "HT_OU15": {"Üst": "İY 1.5 Üst", "Alt": "İY 1.5 Alt"},
+    "OE": {"Tek": "Tek", "Çift": "Çift", "Odd": "Tek", "Even": "Çift"},
+    "CARDS": {"Üst": "Kart Üst", "Alt": "Kart Alt"},
+}
+
+
+def _market_to_turkish(market: str, pick: str) -> str:
+    market_map = _MARKET_TR_MAP.get(market, {})
+    result = market_map.get(pick)
+    if result:
+        return result
+    return f"{market} {pick}"

ai-engine/services/single_match_orchestrator.py

@@ -51,8 +51,10 @@ from core.engines.player_predictor import PlayerPrediction, get_player_predictor
 from services.feature_enrichment import FeatureEnrichmentService
 from services.betting_brain import BettingBrain
 from services.v26_shadow_engine import V26ShadowEngine, get_v26_shadow_engine
+from services.match_commentary import generate_match_commentary
 from utils.top_leagues import load_top_league_ids
 from utils.league_reliability import load_league_reliability
+from config.config_loader import build_threshold_dict, get_threshold_default
 
 
 @dataclass
@@ -84,6 +86,7 @@ class MatchData:
     current_score_home: Optional[int] = None
     current_score_away: Optional[int] = None
     lineup_confidence: float = 0.0
+    source_table: str = "matches"
 
 
 class SingleMatchOrchestrator:
@@ -164,77 +167,15 @@ class SingleMatchOrchestrator:
         self.league_reliability = load_league_reliability()
         self.enrichment = FeatureEnrichmentService()
         self.odds_band_analyzer = OddsBandAnalyzer()
-        # ── V32 Calibration Rebalance ──────────────────────────────────
+        # ── Market Thresholds (loaded from config/market_thresholds.json) ──
-        # RULE: max_reachable = 100 × calibration MUST be > min_conf + 8
+        # All values are centralized in a single JSON file for easy tuning
-        # Previous values had 5 markets where this was IMPOSSIBLE:
+        # without code changes. See config/market_thresholds.json for details.
-        #   HT(0.42×100=42 < 45), HCAP(0.40×100=40 < 46), HTFT(0.28×100=28 < 32)
+        self.market_calibration: Dict[str, float] = build_threshold_dict("calibration")
-        #   HT_OU15(0.46×100=46 < 48), CARDS(0.45×100=45 < 48)
+        self.market_min_conf: Dict[str, float] = build_threshold_dict("min_conf")
-        # These markets could NEVER become playable → all predictions were PASS.
+        self.market_min_play_score: Dict[str, float] = build_threshold_dict("min_play_score")
-        #
+        self.market_min_edge: Dict[str, float] = build_threshold_dict("min_edge")
-        # New calibration: conservative but mathematically achievable.
+        self.odds_band_min_sample: Dict[str, float] = build_threshold_dict("odds_band_min_sample")
-        # Each market's calibration ensures high-confidence model outputs CAN pass.
+        self.odds_band_min_edge: Dict[str, float] = build_threshold_dict("odds_band_min_edge")
-        self.market_calibration: Dict[str, float] = {
-            "MS": 0.62,       # max=62 vs min=42 ✓ (was 0.48→max=48 vs 44 ⚠️)
-            "DC": 0.82,       # max=82 vs min=52 ✓ (unchanged, already good)
-            "OU15": 0.84,     # max=84 vs min=55 ✓ (unchanged, already good)
-            "OU25": 0.68,     # max=68 vs min=48 ✓ (was 0.54→max=54 vs 52 ⚠️)
-            "OU35": 0.60,     # max=60 vs min=48 ✓ (was 0.44→max=44 vs 54 ❌)
-            "BTTS": 0.65,     # max=65 vs min=46 ✓ (was 0.50→max=50 vs 50 ⚠️)
-            "HT": 0.58,       # max=58 vs min=40 ✓ (was 0.42→max=42 vs 45 ❌)
-            "HT_OU05": 0.68,  # max=68 vs min=50 ✓ (unchanged)
-            "HT_OU15": 0.60,  # max=60 vs min=42 ✓ (was 0.46→max=46 vs 48 ❌)
-            "OE": 0.62,       # max=62 vs min=46 ✓ (was 0.58→max=58 vs 50 ok)
-            "CARDS": 0.58,    # max=58 vs min=42 ✓ (was 0.45→max=45 vs 48 ❌)
-            "HCAP": 0.56,     # max=56 vs min=40 ✓ (was 0.40→max=40 vs 46 ❌)
-            "HTFT": 0.45,     # max=45 vs min=28 ✓ (was 0.28→max=28 vs 32 ❌)
-        }
-        # Min confidence: lowered to be achievable (max_reachable - 16 to -20)
-        self.market_min_conf: Dict[str, float] = {
-            "MS": 42.0,       # was 44 — 3-way market, hard to get high conf
-            "DC": 52.0,       # was 55 — double chance is easier
-            "OU15": 55.0,     # was 58 — binary + usually high conf
-            "OU25": 48.0,     # was 52 — core market, allow more through
-            "OU35": 48.0,     # was 54 — lowered to let signals pass
-            "BTTS": 46.0,     # was 50 — binary market
-            "HT": 40.0,       # was 45 — was ❌ impossible, now achievable
-            "HT_OU05": 50.0,  # was 54 — binary HT market
-            "HT_OU15": 42.0,  # was 48 — was ❌ impossible, now achievable
-            "OE": 46.0,       # was 50 — coin-flip market, lower bar
-            "CARDS": 42.0,    # was 48 — was ❌ impossible, now achievable
-            "HCAP": 40.0,     # was 46 — was ❌ impossible, now achievable
-            "HTFT": 28.0,     # was 32 — was ❌ impossible, 9-way market
-        }
-        # Min play score: moderate reduction to allow more C-grade bets
-        self.market_min_play_score: Dict[str, float] = {
-            "MS": 65.0,       # was 72 — let more MS through for tracking
-            "DC": 58.0,       # was 62 — DC is high accuracy
-            "OU15": 60.0,     # was 64 — strong market per backtest
-            "OU25": 64.0,     # was 70 — core market
-            "OU35": 68.0,     # was 76 — riskier market
-            "BTTS": 64.0,     # was 70 — allow more signals
-            "HT": 66.0,       # was 74 — was never reachable anyway
-            "HT_OU05": 60.0,  # was 64 — strong backtest market
-            "HT_OU15": 64.0,  # was 72 — moderate
-            "OE": 60.0,       # was 66 — low priority market
-            "CARDS": 66.0,    # was 74 — niche market
-            "HCAP": 68.0,     # was 76 — risky
-            "HTFT": 72.0,     # was 82 — 9-way, very risky
-        }
-        self.market_min_edge: Dict[str, float] = {
-            "MS": 0.02,       # was 0.03 — slight relaxation
-            "DC": 0.01,       # unchanged
-            "OU15": 0.01,     # unchanged
-            "OU25": 0.02,     # unchanged
-            "OU35": 0.03,     # was 0.04
-            "BTTS": 0.02,     # was 0.03
-            "HT": 0.03,       # was 0.04
-            "HT_OU05": 0.01,  # unchanged
-            "HT_OU15": 0.02,  # was 0.03
-            "OE": 0.02,       # unchanged
-            "CARDS": 0.02,    # was 0.03
-            "HCAP": 0.03,     # was 0.04
-            "HTFT": 0.05,     # was 0.06
-        }
 
     def _get_v25_predictor(self) -> V25Predictor:
         if self.v25_predictor is None:
@@ -362,6 +303,32 @@ class SingleMatchOrchestrator:
                         away_venue_elo = float(elo_row.get('away_away_elo') or away_elo)
                         home_form_elo_val = float(elo_row.get('home_form_elo') or home_elo)
                         away_form_elo_val = float(elo_row.get('away_form_elo') or away_elo)
+                    else:
+                        cur.execute(
+                            """
+                            SELECT
+                                team_id,
+                                overall_elo,
+                                home_elo,
+                                away_elo,
+                                form_elo
+                            FROM team_elo_ratings
+                            WHERE team_id IN (%s, %s)
+                            """,
+                            (data.home_team_id, data.away_team_id),
+                        )
+                        elo_rows = cur.fetchall()
+                        by_team = {str(r.get("team_id")): r for r in elo_rows}
+                        home_row = by_team.get(str(data.home_team_id))
+                        away_row = by_team.get(str(data.away_team_id))
+                        if home_row:
+                            home_elo = float(home_row.get("overall_elo") or 1500.0)
+                            home_venue_elo = float(home_row.get("home_elo") or home_elo)
+                            home_form_elo_val = float(home_row.get("form_elo") or home_elo)
+                        if away_row:
+                            away_elo = float(away_row.get("overall_elo") or 1500.0)
+                            away_venue_elo = float(away_row.get("away_elo") or away_elo)
+                            away_form_elo_val = float(away_row.get("form_elo") or away_elo)
 
                     # Enrichment queries
                     home_stats = enr.compute_team_stats(cur, data.home_team_id, data.match_date_ms)
@@ -390,6 +357,8 @@ class SingleMatchOrchestrator:
                         before_ts=data.match_date_ms,
                         referee_name=data.referee_name,
                     )
+                    setattr(data, "odds_band_features", odds_band_features)
+                    setattr(data, "feature_source", "football_ai_features" if elo_row else "live_prematch_enrichment")
         except Exception:
             # Full fallback — use all defaults
             home_stats = dict(enr._DEFAULT_TEAM_STATS)
@@ -409,6 +378,8 @@ class SingleMatchOrchestrator:
             home_rest = 7.0
             away_rest = 7.0
             odds_band_features = {}  # V28 fallback
+            setattr(data, "odds_band_features", odds_band_features)
+            setattr(data, "feature_source", "fallback_defaults")
 
         odds_presence = {
             'odds_ms_h_present': 1.0 if ms_h > 1.01 else 0.0,
@@ -667,7 +638,7 @@ class SingleMatchOrchestrator:
 
         signal: Dict[str, Any] = {}
 
-        def _temperature_scale(probs_dict: Dict[str, float], temperature: float = 2.5) -> Dict[str, float]:
+        def _temperature_scale(probs_dict: Dict[str, float], temperature: float = 1.5) -> Dict[str, float]:
             """
             Apply temperature scaling to soften overconfident model outputs.
 
@@ -676,19 +647,22 @@ class SingleMatchOrchestrator:
             T=1.0 → no change, T>1 → softer probabilities.
 
             Standard approach for post-hoc model calibration (Guo et al., 2017).
+
+            V34: Reduced from 2.5 to 1.5 — V25 model is already calibrated via
+            odds-aware training. Excessive flattening was destroying signal.
             """
             import math
             eps = 1e-7  # numerical stability
             n = len(probs_dict)
 
-            # Determine appropriate temperature based on market type
+            # V34: Reduced temperature — odds-aware model is already calibrated
             # Binary markets (2-class) tend to be more overconfident in LGB
             if n <= 2:
-                T = max(temperature, 2.0)
+                T = max(temperature, 1.5)   # was 2.0
             elif n == 3:
-                T = max(temperature * 0.8, 1.5)  # 3-way slightly less aggressive
+                T = max(temperature * 0.8, 1.2)  # was 1.5 — 3-way slightly less aggressive
             else:
-                T = max(temperature * 0.6, 1.3)  # 9-way (HTFT) already spread
+                T = max(temperature * 0.6, 1.0)  # was 1.3 — 9-way (HTFT) already spread
 
             # Convert to log-odds and apply temperature
             labels = list(probs_dict.keys())
@@ -714,8 +688,8 @@ class SingleMatchOrchestrator:
             Applies temperature scaling to convert overconfident LightGBM outputs
             into realistic, calibrated probabilities.
             """
-            # Apply temperature scaling to soften extreme probabilities
+            # V34: Apply temperature scaling — reduced from 2.5 to 1.5
-            scaled_probs = _temperature_scale(probs_dict, temperature=2.5)
+            scaled_probs = _temperature_scale(probs_dict, temperature=1.5)
 
             best_label = max(scaled_probs, key=scaled_probs.get)
             best_prob = float(scaled_probs[best_label])
@@ -1290,25 +1264,72 @@ class SingleMatchOrchestrator:
                     ),
                 }
 
-                # BTTS triple value
+                # BTTS triple value — now with V27 BTTS model
-                btts_yes_odds = float((data.odds_data or {}).get("btts_y", 0))
+                btts_yes_odds = float((data.odds_data or {}).get('btts_y', 0))
                 btts_implied = (1.0 / btts_yes_odds) if btts_yes_odds > 1.0 else 0.50
-                btts_band_rate = odds_band_btts["yes_rate"]
+                btts_band_rate = odds_band_btts['yes_rate']
-                btts_combined = btts_band_rate
+
+                # V27 BTTS model prediction (if available)
+                v27_btts = v27_preds.get('btts')
+                v27_btts_yes = (v27_btts or {}).get('yes', 0) if v27_btts else 0
+
+                if v27_btts_yes > 0:
+                    btts_combined = (v27_btts_yes + btts_band_rate) / 2.0
+                else:
+                    btts_combined = btts_band_rate
                 btts_edge = btts_combined - btts_implied
                 btts_band_confirms = btts_band_rate > btts_implied
+                btts_v27_confirms = v27_btts_yes > btts_implied if v27_btts_yes > 0 else False
+                btts_conf_count = sum([btts_v27_confirms, btts_band_confirms])
 
-                triple_value["btts_yes"] = {
+                # BTTS divergence (V25 vs V27)
-                    "band_rate": round(btts_band_rate, 4),
+                v25_btts_probs = {
-                    "implied_prob": round(btts_implied, 4),
+                    'no': 1.0 - prediction.btts_yes_prob,
-                    "combined_prob": round(btts_combined, 4),
+                    'yes': prediction.btts_yes_prob,
-                    "edge": round(btts_edge, 4),
+                }
-                    "band_sample": odds_band_btts["sample"],
+                btts_divergence = compute_divergence(v25_btts_probs, v27_btts) if v27_btts else {}
-                    "confirmations": 1 if btts_band_confirms else 0,
+                btts_odds = {
-                    "is_value": (
+                    'yes': float((data.odds_data or {}).get('btts_y', 0)),
+                    'no': float((data.odds_data or {}).get('btts_n', 0)),
+                }
+                btts_value_edge = compute_value_edge(
+                    v25_btts_probs, v27_btts, btts_odds,
+                ) if v27_btts else {}
+
+                # DC divergence (derived from V27 MS probs)
+                v27_dc = v27_preds.get('dc')
+                dc_divergence = {}
+                dc_value_edge = {}
+                if v27_dc:
+                    v25_dc_probs = {
+                        '1x': prediction.ms_home_prob + prediction.ms_draw_prob,
+                        'x2': prediction.ms_draw_prob + prediction.ms_away_prob,
+                        '12': prediction.ms_home_prob + prediction.ms_away_prob,
+                    }
+                    dc_divergence = compute_divergence(v25_dc_probs, v27_dc)
+                    dc_odds = {
+                        '1x': float((data.odds_data or {}).get('dc_1x', 0)),
+                        'x2': float((data.odds_data or {}).get('dc_x2', 0)),
+                        '12': float((data.odds_data or {}).get('dc_12', 0)),
+                    }
+                    dc_value_edge = compute_value_edge(v25_dc_probs, v27_dc, dc_odds)
+
+                triple_value['btts_yes'] = {
+                    'v27_prob': round(v27_btts_yes, 4),
+                    'band_rate': round(btts_band_rate, 4),
+                    'implied_prob': round(btts_implied, 4),
+                    'combined_prob': round(btts_combined, 4),
+                    'edge': round(btts_edge, 4),
+                    'band_sample': odds_band_btts['sample'],
+                    'confirmations': btts_conf_count,
+                    'is_value': (
+                        btts_conf_count >= 2
+                        and btts_edge > 0.05
+                        and odds_band_btts['sample'] >= 8
+                    ) if v27_btts_yes > 0 else (
                         btts_band_confirms
                         and btts_edge > 0.05
-                        and odds_band_btts["sample"] >= 8
+                        and odds_band_btts['sample'] >= 8
                     ),
                 }
 
@@ -1366,14 +1387,20 @@ class SingleMatchOrchestrator:
                     "predictions": {
                         "ms": v27_ms or {},
                         "ou25": v27_ou25 or {},
+                        "btts": v27_btts or {},
+                        "dc": v27_dc or {},
                     },
                     "divergence": {
                         "ms": ms_divergence,
                         "ou25": ou25_divergence,
+                        "btts": btts_divergence,
+                        "dc": dc_divergence,
                     },
                     "value_edge": {
                         "ms": ms_value,
                         "ou25": ou25_value,
+                        "btts": btts_value_edge,
+                        "dc": dc_value_edge,
                     },
                     "odds_band": {
                         "ms_home": odds_band_ms_home,
@@ -1426,6 +1453,13 @@ class SingleMatchOrchestrator:
 
         base_package = self._apply_upper_brain_guards(base_package)
 
+        # ── Match Commentary: human-readable summary ──────────────
+        try:
+            base_package["match_commentary"] = generate_match_commentary(base_package)
+        except Exception as e:
+            print(f"[Commentary] ⚠ Generation failed (non-fatal): {e}")
+            base_package["match_commentary"] = None
+
         mode = str(getattr(self, "engine_mode", "v28-pro-max") or "v28-pro-max").lower()
         if mode not in {"v25", "v26", "dual", "v28", "v28-pro-max"}:
             mode = "v25"
@@ -1439,6 +1473,7 @@ class SingleMatchOrchestrator:
         )
 
         if mode == "v26":
+            shadow_package["match_commentary"] = base_package.get("match_commentary")
             return shadow_package
         if mode == "dual":
             merged = dict(base_package)
@@ -2670,6 +2705,13 @@ class SingleMatchOrchestrator:
                     # Hard gate: predictions with unknown teams are noisy and misleading.
                     return None
 
+                status, state, substate = self._normalize_match_status(
+                    row.get("status"),
+                    row.get("state"),
+                    row.get("substate"),
+                    row.get("score_home"),
+                    row.get("score_away"),
+                )
                 odds_data = self._extract_odds(cur, row)
                 home_lineup, away_lineup, lineup_source, lineup_confidence = self._extract_lineups(cur, row)
                 sidelined = self._parse_json_dict(row.get("sidelined"))
@@ -2723,10 +2765,11 @@ class SingleMatchOrchestrator:
                     home_position=home_position,
                     away_position=away_position,
                     lineup_source=lineup_source,
-                    status=str(row.get("status") or ""),
+                    status=status,
-                    state=row.get("state"),
+                    state=state,
-                    substate=row.get("substate"),
+                    substate=substate,
                     lineup_confidence=lineup_confidence,
+                    source_table=str(row.get("source_table") or "matches"),
                     current_score_home=(
                         int(row.get("score_home"))
                         if row.get("score_home") is not None
@@ -2760,7 +2803,8 @@ class SingleMatchOrchestrator:
                 lm.referee_name,
                 ht.name as home_team_name,
                 at.name as away_team_name,
-                l.name as league_name
+                l.name as league_name,
+                'live_matches'::text as source_table
             FROM live_matches lm
             LEFT JOIN teams ht ON ht.id = lm.home_team_id
             LEFT JOIN teams at ON at.id = lm.away_team_id
@@ -2772,6 +2816,37 @@ class SingleMatchOrchestrator:
         )
         return cur.fetchone()
 
+    @staticmethod
+    def _normalize_match_status(
+        status: Any,
+        state: Any,
+        substate: Any,
+        score_home: Any,
+        score_away: Any,
+    ) -> Tuple[str, Optional[str], Optional[str]]:
+        state_text = str(state or "").strip()
+        status_text = str(status or "").strip()
+        substate_text = str(substate or "").strip()
+
+        state_key = state_text.lower().replace("_", "").replace(" ", "")
+        status_key = status_text.lower().replace("_", "").replace(" ", "")
+        substate_key = substate_text.lower().replace("_", "").replace(" ", "")
+
+        live_tokens = {"live", "livegame", "firsthalf", "secondhalf", "halftime", "1h", "2h", "ht", "1q", "2q", "3q", "4q"}
+        finished_tokens = {"post", "postgame", "finished", "played", "ft", "ended", "aet", "pen", "penalties", "afterpenalties"}
+        pre_tokens = {"pre", "pregame", "scheduled", "ns", "notstarted", "timestamp"}
+
+        if state_key in live_tokens or status_key in live_tokens or substate_key in live_tokens:
+            return "LIVE", state_text or "live", substate_text or None
+        if state_key in finished_tokens or status_key in finished_tokens or substate_key in finished_tokens:
+            return "FT", state_text or "post", substate_text or None
+        if score_home is not None and score_away is not None and status_key not in pre_tokens:
+            return "FT", state_text or "post", substate_text or None
+        if state_key in pre_tokens or status_key in pre_tokens or substate_key in pre_tokens:
+            return "NS", state_text or "pre", substate_text or None
+
+        return status_text or "NS", state_text or None, substate_text or None
+
     def _fetch_hist_match(self, cur: RealDictCursor, match_id: str) -> Optional[Dict[str, Any]]:
         cur.execute(
             """
@@ -2793,7 +2868,8 @@ class SingleMatchOrchestrator:
                 ref.name as referee_name,
                 ht.name as home_team_name,
                 at.name as away_team_name,
-                l.name as league_name
+                l.name as league_name,
+                'matches'::text as source_table
             FROM matches m
             LEFT JOIN teams ht ON ht.id = m.home_team_id
             LEFT JOIN teams at ON at.id = m.away_team_id
@@ -3668,66 +3744,33 @@ class SingleMatchOrchestrator:
 
         playable_rows = [row for row in market_rows if row.get("playable")]
         
-        # GUARANTEED PICK LOGIC (V32 - Calibration-aware):
-        # Runtime replay insights:
-        #   - Trust only markets that remain robust after pre-match replay.
-        #   - Current strongest football markets: DC, OU15, HT_OU05.
-        #
-        # Priority 1: High-accuracy market (DC/OU15/HT_OU05/OU25) + Odds >= 1.30 + Conf >= 44%
-        # Priority 2: Any playable + Odds >= 1.30 + Conf >= 44%
-        # Priority 3: Playable + Odds >= 1.30
-        # Priority 4: Best non-playable (fallback)
         MIN_ODDS = 1.30
-        MIN_CONFIDENCE = 44.0  # V32: lowered from 52 to match new calibration
+        playable_with_odds = [
-        
-        # High-accuracy markets from backtest (prioritize these)
-        HIGH_ACCURACY_MARKETS = {"DC", "OU15", "HT_OU05"}
-        
-        # Priority 1: High-accuracy markets with good odds and confidence
-        high_accuracy_picks = [
             row for row in playable_rows
-            if row.get("market") in HIGH_ACCURACY_MARKETS
+            if float(row.get("odds", 0.0)) >= MIN_ODDS
-            and float(row.get("odds", 0.0)) >= MIN_ODDS
-            and float(row.get("calibrated_confidence", 0.0)) >= MIN_CONFIDENCE
         ]
-        
+
-        if high_accuracy_picks:
+        if playable_with_odds:
-            # Sort by play_score, pick the best
+            playable_with_odds.sort(
-            high_accuracy_picks.sort(key=lambda r: float(r.get("play_score", 0.0)), reverse=True)
+                key=lambda r: (
-            main_pick = high_accuracy_picks[0]
+                    float(r.get("ev_edge", 0.0)),
-            main_pick["is_guaranteed"] = True
+                    float(r.get("play_score", 0.0)),
-            main_pick["pick_reason"] = "high_accuracy_market"
+                ),
+                reverse=True,
+            )
+            main_pick = playable_with_odds[0]
+            main_pick["is_guaranteed"] = False
+            main_pick["pick_reason"] = "positive_ev_after_odds_band_gate"
         else:
-            # Priority 2: Any playable with odds >= 1.30 and confidence >= 40%
+            fallback_with_odds = [r for r in market_rows if float(r.get("odds", 0.0)) > 1.0]
-            guaranteed_picks = [
+            fallback_with_odds.sort(key=lambda r: float(r.get("play_score", 0.0)), reverse=True)
-                row for row in playable_rows
+            main_pick = fallback_with_odds[0] if fallback_with_odds else (market_rows[0] if market_rows else None)
-                if float(row.get("odds", 0.0)) >= MIN_ODDS
+            if main_pick:
-                and float(row.get("calibrated_confidence", 0.0)) >= MIN_CONFIDENCE
+                main_pick["is_guaranteed"] = False
-            ]
+                main_pick["playable"] = False
-            
+                main_pick["stake_units"] = 0.0
-            if guaranteed_picks:
+                main_pick["bet_grade"] = "PASS"
-                guaranteed_picks.sort(key=lambda r: float(r.get("play_score", 0.0)), reverse=True)
+                main_pick["pick_reason"] = "no_playable_value_after_odds_band_gate"
-                main_pick = guaranteed_picks[0]
-                main_pick["is_guaranteed"] = True
-                main_pick["pick_reason"] = "confidence_threshold_met"
-            else:
-                # Priority 3: Fallback - playable with odds >= 1.30
-                playable_with_odds = [
-                    row for row in playable_rows
-                    if float(row.get("odds", 0.0)) >= MIN_ODDS
-                ]
-                if playable_with_odds:
-                    playable_with_odds.sort(key=lambda r: float(r.get("play_score", 0.0)), reverse=True)
-                    main_pick = playable_with_odds[0]
-                    main_pick["is_guaranteed"] = False
-                    main_pick["pick_reason"] = "odds_only_fallback"
-                else:
-                    # Priority 4: Last resort - any playable or first market WITH ODDS > 0
-                    fallback_with_odds = [r for r in market_rows if float(r.get("odds", 0.0)) > 1.0]
-                    main_pick = playable_rows[0] if playable_rows else (fallback_with_odds[0] if fallback_with_odds else (market_rows[0] if market_rows else None))
-                    if main_pick:
-                        main_pick["is_guaranteed"] = False
-                        main_pick["pick_reason"] = "last_resort"
 
         aggressive_pick = None
         htft_probs = prediction.ht_ft_probs or {}
@@ -3756,11 +3799,13 @@ class SingleMatchOrchestrator:
         value_candidates = [
             row for row in playable_rows
             if float(row.get("odds", 0.0)) >= 1.60
-            and float(row.get("calibrated_confidence", 0.0)) >= 40.0
+            # V34: Lowered min calibrated_confidence for value candidates from 40.0 to 25.0 
+            # to allow high-odds value bets (which naturally have lower probabilities).
+            and float(row.get("calibrated_confidence", 0.0)) >= 25.0
         ]
         if value_candidates:
-            # Score them by (play_score * odds) to reward higher odds
+            # Score them by (ev_edge) to reward actual mathematical value
-            value_candidates.sort(key=lambda r: float(r.get("play_score", 0.0)) * float(r.get("odds", 1.0)), reverse=True)
+            value_candidates.sort(key=lambda r: float(r.get("ev_edge", 0.0)), reverse=True)
             for v_cand in value_candidates:
                 if not main_pick or (v_cand["market"] != main_pick["market"] or v_cand["pick"] != main_pick["pick"]):
                     value_pick = v_cand
@@ -3982,51 +4027,33 @@ class SingleMatchOrchestrator:
 
         playable_rows = [row for row in market_rows if row.get("playable")]
         
-        # GUARANTEED PICK LOGIC (Optimized - same as football)
         MIN_ODDS = 1.30
-        MIN_CONFIDENCE = 40.0
+        playable_with_odds = [
-        HIGH_ACCURACY_MARKETS = {"ML", "TOT", "SPREAD"}
-        
-        high_accuracy_picks = [
             row for row in playable_rows
-            if row.get("market_type") in HIGH_ACCURACY_MARKETS
+            if float(row.get("odds", 0.0)) >= MIN_ODDS
-            and float(row.get("odds", 0.0)) >= MIN_ODDS
-            and float(row.get("calibrated_confidence", 0.0)) >= MIN_CONFIDENCE
         ]
-        
+
-        if high_accuracy_picks:
+        if playable_with_odds:
-            high_accuracy_picks.sort(key=lambda r: float(r.get("play_score", 0.0)), reverse=True)
+            playable_with_odds.sort(
-            main_pick = high_accuracy_picks[0]
+                key=lambda r: (
-            main_pick["is_guaranteed"] = True
+                    float(r.get("ev_edge", 0.0)),
-            main_pick["pick_reason"] = "high_accuracy_market"
+                    float(r.get("play_score", 0.0)),
+                ),
+                reverse=True,
+            )
+            main_pick = playable_with_odds[0]
+            main_pick["is_guaranteed"] = False
+            main_pick["pick_reason"] = "positive_ev_pick"
         else:
-            guaranteed_picks = [
+            fallback_with_odds = [r for r in market_rows if float(r.get("odds", 0.0)) > 1.0]
-                row for row in playable_rows
+            fallback_with_odds.sort(key=lambda r: float(r.get("play_score", 0.0)), reverse=True)
-                if float(row.get("odds", 0.0)) >= MIN_ODDS
+            main_pick = fallback_with_odds[0] if fallback_with_odds else (market_rows[0] if market_rows else None)
-                and float(row.get("calibrated_confidence", 0.0)) >= MIN_CONFIDENCE
+            if main_pick:
-            ]
+                main_pick["is_guaranteed"] = False
-            
+                main_pick["playable"] = False
-            if guaranteed_picks:
+                main_pick["stake_units"] = 0.0
-                guaranteed_picks.sort(key=lambda r: float(r.get("play_score", 0.0)), reverse=True)
+                main_pick["bet_grade"] = "PASS"
-                main_pick = guaranteed_picks[0]
+                main_pick["pick_reason"] = "no_playable_value_found"
-                main_pick["is_guaranteed"] = True
-                main_pick["pick_reason"] = "confidence_threshold_met"
-            else:
-                playable_with_odds = [
-                    row for row in playable_rows
-                    if float(row.get("odds", 0.0)) >= MIN_ODDS
-                ]
-                if playable_with_odds:
-                    playable_with_odds.sort(key=lambda r: float(r.get("play_score", 0.0)), reverse=True)
-                    main_pick = playable_with_odds[0]
-                    main_pick["is_guaranteed"] = False
-                    main_pick["pick_reason"] = "odds_only_fallback"
-                else:
-                    fallback_with_odds = [r for r in market_rows if float(r.get("odds", 0.0)) > 1.0]
-                    main_pick = playable_rows[0] if playable_rows else (fallback_with_odds[0] if fallback_with_odds else (market_rows[0] if market_rows else None))
-                    if main_pick:
-                        main_pick["is_guaranteed"] = False
-                        main_pick["pick_reason"] = "last_resort"
 
         supporting: List[Dict[str, Any]] = []
         for row in market_rows:
@@ -4518,6 +4545,121 @@ class SingleMatchOrchestrator:
             return True
         return self._v25_market_odds(odds, market, pick) > 1.01
 
+    def _odds_band_verdict(
+        self,
+        data: MatchData,
+        market: str,
+        pick: str,
+        implied_prob: float,
+    ) -> Dict[str, Any]:
+        features = getattr(data, "odds_band_features", {}) or {}
+        market_key = str(market or "").upper()
+        if not isinstance(features, dict) or implied_prob <= 0.0:
+            return {
+                "required": market_key in self.odds_band_min_sample,
+                "available": False,
+                "band_prob": 0.0,
+                "band_sample": 0.0,
+                "band_edge": 0.0,
+                "aligned": False,
+                "reason": "odds_band_unavailable",
+            }
+
+        pick_key = self._normalize_pick_token(pick)
+        band_prob = 0.0
+        sample = 0.0
+
+        if market_key == "MS":
+            if pick_key == "1":
+                band_prob = float(features.get("home_band_ms_win_rate", 0.0) or 0.0)
+                sample = float(features.get("home_band_ms_sample", 0.0) or 0.0)
+            elif pick_key == "2":
+                band_prob = float(features.get("away_band_ms_win_rate", 0.0) or 0.0)
+                sample = float(features.get("away_band_ms_sample", 0.0) or 0.0)
+            elif pick_key in {"X", "0"}:
+                home_draw = float(features.get("home_band_ms_draw_rate", 0.0) or 0.0)
+                away_draw = float(features.get("away_band_ms_draw_rate", 0.0) or 0.0)
+                band_prob = (home_draw + away_draw) / 2.0 if home_draw and away_draw else max(home_draw, away_draw)
+                sample = max(
+                    float(features.get("home_band_ms_sample", 0.0) or 0.0),
+                    float(features.get("away_band_ms_sample", 0.0) or 0.0),
+                )
+        elif market_key == "DC":
+            dc_key = pick_key.replace("-", "").lower()
+            band_prob = float(features.get(f"band_dc_{dc_key}_rate", 0.0) or 0.0)
+            sample = float(features.get(f"band_dc_{dc_key}_sample", 0.0) or 0.0)
+        elif market_key in {"OU15", "OU25", "OU35"}:
+            suffix = {"OU15": "ou15", "OU25": "ou25", "OU35": "ou35"}[market_key]
+            rate_key = "over_rate" if self._pick_is_over(pick_key) else "under_rate"
+            band_prob = float(features.get(f"band_{suffix}_{rate_key}", 0.0) or 0.0)
+            sample = float(features.get(f"band_{suffix}_sample", 0.0) or 0.0)
+        elif market_key == "BTTS":
+            is_yes = "VAR" in pick_key or "YES" in pick_key or pick_key == "Y"
+            band_prob = float(features.get(f"band_btts_{'yes' if is_yes else 'no'}_rate", 0.0) or 0.0)
+            sample = float(features.get("band_btts_sample", 0.0) or 0.0)
+        elif market_key == "HT":
+            if pick_key == "1":
+                band_prob = float(features.get("home_band_ht_win_rate", 0.0) or 0.0)
+                sample = float(features.get("home_band_ht_sample", 0.0) or 0.0)
+            elif pick_key == "2":
+                band_prob = float(features.get("away_band_ht_win_rate", 0.0) or 0.0)
+                sample = float(features.get("away_band_ht_sample", 0.0) or 0.0)
+            elif pick_key in {"X", "0"}:
+                home_draw = float(features.get("home_band_ht_draw_rate", 0.0) or 0.0)
+                away_draw = float(features.get("away_band_ht_draw_rate", 0.0) or 0.0)
+                band_prob = (home_draw + away_draw) / 2.0 if home_draw and away_draw else max(home_draw, away_draw)
+                sample = max(
+                    float(features.get("home_band_ht_sample", 0.0) or 0.0),
+                    float(features.get("away_band_ht_sample", 0.0) or 0.0),
+                )
+        elif market_key in {"HT_OU05", "HT_OU15"}:
+            suffix = "ht_ou05" if market_key == "HT_OU05" else "ht_ou15"
+            rate_key = "over_rate" if self._pick_is_over(pick_key) else "under_rate"
+            band_prob = float(features.get(f"band_{suffix}_{rate_key}", 0.0) or 0.0)
+            sample = float(features.get(f"band_{suffix}_sample", 0.0) or 0.0)
+
+        band_edge = band_prob - implied_prob if band_prob > 0.0 else 0.0
+        required_sample = float(self.odds_band_min_sample.get(market_key, 0.0))
+        required_edge = float(self.odds_band_min_edge.get(market_key, 0.0))
+        available = band_prob > 0.0 and sample >= required_sample
+        aligned = available and band_edge >= required_edge
+
+        reason = "odds_band_confirms_value"
+        if required_sample > 0.0 and sample < required_sample:
+            reason = "odds_band_sample_too_low"
+        elif band_prob <= 0.0:
+            reason = "odds_band_missing_probability"
+        elif band_edge < required_edge:
+            reason = f"odds_band_no_value_{band_edge:+.3f}"
+
+        return {
+            "required": market_key in self.odds_band_min_sample,
+            "available": available,
+            "band_prob": band_prob,
+            "band_sample": sample,
+            "band_edge": band_edge,
+            "aligned": aligned,
+            "reason": reason,
+        }
+
+    @staticmethod
+    def _normalize_pick_token(pick: str) -> str:
+        return (
+            str(pick or "")
+            .strip()
+            .upper()
+            .replace("İ", "I")
+            .replace("Ü", "U")
+            .replace("Ş", "S")
+            .replace("Ğ", "G")
+            .replace("Ö", "O")
+            .replace("Ç", "C")
+        )
+
+    @staticmethod
+    def _pick_is_over(pick_key: str) -> bool:
+        return "UST" in pick_key or "OVER" in pick_key
+
     @staticmethod
     def _goal_line_for_market(market: str) -> Optional[float]:
         return {
@@ -4968,12 +5110,8 @@ class SingleMatchOrchestrator:
         calibrated_conf = max(1.0, min(99.0, raw_conf * calibration))
         min_conf = self.market_min_conf.get(market, 55.0)
 
-        # ── V2 Quant: EV Edge formula ──────────────────────────────────
-        # Old: edge = prob - (1/odd)   ← simple probability difference
-        # New: edge = (prob × odd) - 1 ← Expected Value (what a quant uses)
         implied_prob = (1.0 / odd) if odd > 1.0 else 0.0
-        ev_edge = (prob * odd) - 1.0 if odd > 1.0 else 0.0
+        band_verdict = self._odds_band_verdict(data, market, str(row.get("pick") or ""), implied_prob)
-        simple_edge = prob - implied_prob if implied_prob > 0 else 0.0
 
         # ── V31: League-specific odds reliability ──────────────────────
         # Higher reliability → trust odds-based edge more in play_score
@@ -4995,6 +5133,25 @@ class SingleMatchOrchestrator:
             quality_label,
             5.0,
         )
+        # V33: Removed probability deflation. Deflating probability breaks normalization 
+        # (probs no longer sum to 1) and mathematically guarantees negative EV edge.
+        # Data quality and confidence penalties are already applied to play_score.
+        model_calibrated_prob = prob
+        band_prob = float(band_verdict.get("band_prob", 0.0) or 0.0)
+        if bool(band_verdict.get("available")):
+            calibrated_probability = (
+                (model_calibrated_prob * 0.45)
+                + (band_prob * 0.35)
+                + (implied_prob * 0.20)
+            )
+        elif implied_prob > 0.0:
+            calibrated_probability = (model_calibrated_prob * 0.65) + (implied_prob * 0.35)
+        else:
+            calibrated_probability = model_calibrated_prob
+        calibrated_probability = max(0.0, min(0.99, calibrated_probability))
+        model_edge = model_calibrated_prob - implied_prob if implied_prob > 0 else 0.0
+        ev_edge = (calibrated_probability * odd) - 1.0 if odd > 1.0 else 0.0
+        simple_edge = calibrated_probability - implied_prob if implied_prob > 0 else 0.0
 
         home_n = len(data.home_lineup or [])
         away_n = len(data.away_lineup or [])
@@ -5005,22 +5162,20 @@ class SingleMatchOrchestrator:
             lineup_conf = max(0.0, min(1.0, float(getattr(data, "lineup_confidence", 0.0) or 0.0)))
             lineup_penalty += max(1.0, (1.0 - lineup_conf) * 5.0)
 
-        # V31: edge contribution weighted by league odds reliability
-        base_score = calibrated_conf + (simple_edge * 100.0 * edge_multiplier)
-        play_score = max(
-            0.0,
-            min(100.0, base_score - risk_penalty - quality_penalty - lineup_penalty),
-        )
-
         # ── V20+ Safety gates (PRESERVED) ─────────────────────────────
         min_play_score = self.market_min_play_score.get(market, 68.0)
         min_edge = self.market_min_edge.get(market, 0.02)
         reasons: List[str] = []
         playable = True
+        
+        # V34: Broadened value_sniper bypass — odds-aware model rarely shows 3% EV edge
+        # Allow high-confidence predictions OR modest positive EV to bypass secondary gates
+        is_value_sniper = ev_edge >= 0.008 or calibrated_conf >= 55.0
 
         if calibrated_conf < min_conf:
-            playable = False
+            if not is_value_sniper:
-            reasons.append("below_calibrated_conf_threshold")
+                playable = False
+                reasons.append("below_calibrated_conf_threshold")
         if market in self.ODDS_REQUIRED_MARKETS and odd <= 1.01:
             playable = False
             reasons.append("market_odds_missing")
@@ -5037,18 +5192,52 @@ class SingleMatchOrchestrator:
             # Most pre-match predictions use probable_xi — blocking kills all output
             lineup_penalty += 6.0
             reasons.append("lineup_probable_xi_penalty")
-        # V31: negative edge threshold adapts to league reliability
+        # V34: Added confidence bonus — high raw model probability gets a boost
-        # Reliable league: stricter (-0.03), unreliable: looser (-0.08)
+        # This prevents over-penalization when edge is near-zero but model is confident
-        neg_edge_threshold = -0.03 - (1.0 - odds_rel) * 0.05
+        raw_top_prob = float(row.get("probability", 0.0))
+        confidence_bonus = 0.0
+        if raw_top_prob >= 0.65:
+            confidence_bonus = 15.0
+        elif raw_top_prob >= 0.55:
+            confidence_bonus = 10.0
+        elif raw_top_prob >= 0.45:
+            confidence_bonus = 5.0
+        base_score = calibrated_conf + (simple_edge * 100.0 * edge_multiplier) + confidence_bonus
+        play_score = max(
+            0.0,
+            min(100.0, base_score - risk_penalty - quality_penalty - lineup_penalty),
+        )
+        # V34: odds_band gate — only hard-block when band data is AVAILABLE and aligned=False
+        # When band data is sparse (available=False), skip alignment check entirely
+        band_available = bool(band_verdict.get("available", False))
+        if band_available and bool(band_verdict.get("required")) and not bool(band_verdict.get("aligned")):
+            if not is_value_sniper:
+                playable = False
+                reasons.append(str(band_verdict.get("reason") or "odds_band_not_aligned"))
+        elif not band_available and bool(band_verdict.get("required")):
+            # Sparse data — log but don't block
+            reasons.append("odds_band_data_sparse_skipped")
+        # V34: REMOVED model_not_above_market gate entirely
+        # V25 model is odds-informed BY DESIGN → model output ≈ market-implied probability
+        # Requiring model > market is mathematically impossible with this architecture
+        # The negative_model_edge gate below still catches truly anti-value picks
+        # V34: negative edge threshold relaxed — odds-aware model's edge is naturally near zero
+        # Reliable league: -0.08, unreliable: up to -0.15
+        # Only blocks truly anti-value picks (model significantly below market)
+        neg_edge_threshold = -0.08 - (1.0 - odds_rel) * 0.07
         if odd > 1.0 and simple_edge < neg_edge_threshold:
-            playable = False
+            if not is_value_sniper:
-            reasons.append(f"negative_model_edge_{simple_edge:+.3f}")
+                playable = False
+                reasons.append(f"negative_model_edge_{simple_edge:+.3f}")
+        # V34: Added value_sniper bypass — was missing before, causing hard blocks
         if odd > 1.0 and ev_edge < min_edge:
-            playable = False
+            if not is_value_sniper:
-            reasons.append(f"below_market_edge_threshold_{ev_edge:+.3f}")
+                playable = False
+                reasons.append(f"below_market_edge_threshold_{ev_edge:+.3f}")
         if play_score < min_play_score:
-            playable = False
+            if not is_value_sniper:
-            reasons.append("insufficient_play_score")
+                playable = False
+                reasons.append("insufficient_play_score")
 
         if not reasons:
             reasons.append("market_passed_all_gates")
@@ -5068,15 +5257,15 @@ class SingleMatchOrchestrator:
         elif ev_edge > 0.10:
             grade = "A"
             # V2 Quant: Fractional Kelly Criterion (¼ Kelly, 10-unit bankroll)
-            stake_units = self._kelly_stake(prob, odd)
+            stake_units = self._kelly_stake(calibrated_probability, odd)
             reasons.append(f"ev_edge_{ev_edge:+.1%}_grade_A")
         elif ev_edge > 0.05:
             grade = "B"
-            stake_units = self._kelly_stake(prob, odd)
+            stake_units = self._kelly_stake(calibrated_probability, odd)
             reasons.append(f"ev_edge_{ev_edge:+.1%}_grade_B")
         elif ev_edge > 0.02:
             grade = "C"
-            stake_units = self._kelly_stake(prob, odd)
+            stake_units = self._kelly_stake(calibrated_probability, odd)
             reasons.append(f"ev_edge_{ev_edge:+.1%}_grade_C")
         else:
             # Passes all V20+ gates but no mathematical edge over bookie
@@ -5093,8 +5282,16 @@ class SingleMatchOrchestrator:
                 "min_required_play_score": round(min_play_score, 1),
                 "min_required_edge": round(min_edge, 4),
                 "edge": round(ev_edge, 4),
+                "model_probability": round(prob, 4),
+                "model_edge": round(model_edge, 4),
+                "calibrated_probability": round(calibrated_probability, 4),
                 "implied_prob": round(implied_prob, 4),
                 "ev_edge": round(ev_edge, 4),
+                "is_value_sniper": is_value_sniper,
+                "odds_band_probability": round(float(band_verdict.get("band_prob", 0.0) or 0.0), 4),
+                "odds_band_sample": round(float(band_verdict.get("band_sample", 0.0) or 0.0), 1),
+                "odds_band_edge": round(float(band_verdict.get("band_edge", 0.0) or 0.0), 4),
+                "odds_band_aligned": bool(band_verdict.get("aligned")),
                 "odds_reliability": round(odds_rel, 4),
                 "play_score": round(play_score, 1),
                 "playable": playable,
@@ -5145,7 +5342,15 @@ class SingleMatchOrchestrator:
             "stake_units": float(row.get("stake_units", 0.0)),
             "play_score": row.get("play_score", 0.0),
             "ev_edge": row.get("ev_edge", row.get("edge", 0.0)),
+            "is_value_sniper": bool(row.get("is_value_sniper")),
+            "model_probability": row.get("model_probability", row.get("probability", 0.0)),
+            "model_edge": row.get("model_edge", 0.0),
+            "calibrated_probability": row.get("calibrated_probability", row.get("probability", 0.0)),
             "implied_prob": row.get("implied_prob", 0.0),
+            "odds_band_probability": row.get("odds_band_probability", 0.0),
+            "odds_band_sample": row.get("odds_band_sample", 0.0),
+            "odds_band_edge": row.get("odds_band_edge", 0.0),
+            "odds_band_aligned": bool(row.get("odds_band_aligned")),
             "odds_reliability": row.get("odds_reliability", 0.35),
             "odds": row.get("odds", 0.0),
             "reasons": row.get("decision_reasons", []),
@@ -5187,6 +5392,11 @@ class SingleMatchOrchestrator:
         ref_score = 1.0 if data.referee_name else 0.6
         if not data.referee_name:
             flags.append("missing_referee")
+        if data.source_table == "live_matches":
+            flags.append("live_match_pre_match_features")
+        feature_source = str(getattr(data, "feature_source", "") or "")
+        if feature_source == "live_prematch_enrichment":
+            flags.append("ai_features_inferred_from_history")
 
         total_score = (odds_score * 0.45) + (lineup_score * 0.45) + (ref_score * 0.10)
 
@@ -5196,6 +5406,10 @@ class SingleMatchOrchestrator:
             label = "MEDIUM"
         else:
             label = "LOW"
+        if label == "HIGH" and (
+            data.lineup_source == "probable_xi" or not data.referee_name
+        ):
+            label = "MEDIUM"
 
         return {
             "label": label,
@@ -5204,6 +5418,7 @@ class SingleMatchOrchestrator:
             "away_lineup_count": away_n,
             "lineup_source": data.lineup_source,
             "lineup_confidence": round(float(getattr(data, "lineup_confidence", 0.0) or 0.0), 3),
+            "feature_source": feature_source or "unknown",
             "flags": flags,
         }
 

prisma/schema.prisma

@@ -543,6 +543,7 @@ model User {
   analyses              Analysis[]
   refreshTokens         RefreshToken[]
   usageLimit            UsageLimit?
+  subscription          Subscription?
   coupons               UserCoupon[]
   totoCoupons           TotoCoupon[]
 
@@ -551,6 +552,27 @@ model User {
   @@map("users")
 }
 
+model Subscription {
+  id                   String              @id @default(uuid())
+  userId               String              @unique @map("user_id")
+  paddleSubscriptionId String?             @unique @map("paddle_subscription_id")
+  paddleCustomerId     String?             @map("paddle_customer_id")
+  plan                 SubscriptionStatus  @default(free)
+  billingInterval      BillingInterval?    @map("billing_interval")
+  currentPeriodStart   DateTime?           @map("current_period_start")
+  currentPeriodEnd     DateTime?           @map("current_period_end")
+  cancelledAt          DateTime?           @map("cancelled_at")
+  cancelEffectiveDate  DateTime?           @map("cancel_effective_date")
+  paddlePriceId        String?             @map("paddle_price_id")
+  createdAt            DateTime            @default(now()) @map("created_at")
+  updatedAt            DateTime            @updatedAt @map("updated_at")
+  user                 User                @relation(fields: [userId], references: [id], onDelete: Cascade)
+
+  @@index([paddleSubscriptionId])
+  @@index([paddleCustomerId])
+  @@map("subscriptions")
+}
+
 model RefreshToken {
   id        String   @id @default(uuid())
   token     String   @unique
@@ -569,6 +591,8 @@ model UsageLimit {
   userId        String   @unique @map("user_id")
   analysisCount Int      @default(0) @map("analysis_count")
   couponCount   Int      @default(0) @map("coupon_count")
+  maxAnalyses   Int      @default(3) @map("max_analyses")
+  maxCoupons    Int      @default(1) @map("max_coupons")
   lastResetDate DateTime @map("last_reset_date") @db.Date
   createdAt     DateTime @default(now()) @map("created_at")
   updatedAt     DateTime @updatedAt @map("updated_at")
@@ -765,8 +789,15 @@ enum UserRole {
 
 enum SubscriptionStatus {
   free
-  active
+  plus
-  expired
+  premium
+  past_due
+  cancelled
+}
+
+enum BillingInterval {
+  monthly
+  yearly
 }
 
 enum PlayerPosition {

prisma/seed.ts

@@ -24,7 +24,7 @@ async function main() {
         firstName: 'Super',
         lastName: 'Admin',
         role: UserRole.superadmin,
-        subscriptionStatus: SubscriptionStatus.active,
+        subscriptionStatus: SubscriptionStatus.free,
         isActive: true,
       },
     });

src/app.module.ts

@@ -51,6 +51,7 @@ import { AnalysisModule } from "./modules/analysis/analysis.module";
 import { CouponsModule } from "./modules/coupons/coupons.module";
 import { SporTotoModule } from "./modules/spor-toto/spor-toto.module";
 import { AiProxyModule } from "./modules/ai-proxy/ai-proxy.module";
+import { SubscriptionsModule } from "./modules/subscriptions/subscriptions.module";
 
 // Services and Tasks
 import { ServicesModule } from "./services/services.module";
@@ -204,6 +205,7 @@ const historicalFeederMode = process.env.FEEDER_MODE === "historical";
     CouponsModule,
     SporTotoModule,
     AiProxyModule,
+    SubscriptionsModule,
 
     // Services and Scheduled Tasks
     ServicesModule,

src/common/utils/ai-engine-client.ts

@@ -243,7 +243,7 @@ export class AiEngineClient {
     // - 502/503/504 (proxy/gateway errors) → infrastructure
     // Do NOT count 500 (app-level crash in AI Engine) — it may be
     // match-specific and shouldn't block all other matches.
-    if (error.code === 'ECONNABORTED') {
+    if (error.code === "ECONNABORTED") {
       return true;
     }
     const status = error.response.status;

src/common/utils/match-status.util.ts

@@ -81,7 +81,6 @@ export const LIVE_STATUS_VALUES_FOR_DB = [
   "Playing",
   "Half Time",
   "liveGame",
-  "minutes",
 ];
 
 export const LIVE_STATE_VALUES_FOR_DB = [
@@ -110,7 +109,6 @@ export const FINISHED_STATUS_VALUES_FOR_DB = [
   "postGame",
   "posted",
   "Posted",
-  "state",
 ];
 
 export const FINISHED_STATE_VALUES_FOR_DB = [

src/config/env.validation.ts

@@ -72,6 +72,16 @@ export const envSchema = z.object({
   OLLAMA_BASE_URL: z.string().url().optional(),
   OLLAMA_MODEL: z.string().optional(),
 
+  // Paddle (Subscription Billing)
+  PADDLE_API_KEY: z.string().optional(),
+  PADDLE_WEBHOOK_SECRET: z.string().optional(),
+  PADDLE_CLIENT_TOKEN: z.string().optional(),
+  PADDLE_ENVIRONMENT: z.enum(["sandbox", "production"]).default("sandbox"),
+  PADDLE_PLUS_MONTHLY_PRICE_ID: z.string().optional(),
+  PADDLE_PLUS_YEARLY_PRICE_ID: z.string().optional(),
+  PADDLE_PREMIUM_MONTHLY_PRICE_ID: z.string().optional(),
+  PADDLE_PREMIUM_YEARLY_PRICE_ID: z.string().optional(),
+
   // Optional Features
   ENABLE_MAIL: booleanString,
   ENABLE_S3: booleanString,

src/i18n/en/common.json

@@ -9,5 +9,12 @@
   "serverError": "An unexpected error occurred",
   "unauthorized": "You are not authorized to perform this action",
   "forbidden": "Access denied",
-  "badRequest": "Invalid request"
+  "badRequest": "Bad request",
+  "SUCCESS_USER_ROLE_UPDATED": "User role updated",
+  "SUCCESS_USER_SUBSCRIPTION_UPDATED": "User subscription updated",
+  "SUCCESS_USER_DELETED": "User deleted",
+  "SUCCESS_USER_STATUS_UPDATED": "User status updated",
+  "SUCCESS_SETTING_UPDATED": "Setting updated",
+  "SUCCESS_ALL_LIMITS_RESET": "All usage limits reset",
+  "SUCCESS_USER_LIMITS_RESET": "User usage limits reset"
 }

src/i18n/en/errors.json

@@ -10,5 +10,13 @@
   "TENANT_NOT_FOUND": "Tenant not found",
   "VALIDATION_FAILED": "Validation failed",
   "INTERNAL_ERROR": "An internal error occurred, please try again later",
-  "AUTH_REQUIRED": "Authentication required, please provide a valid token"
+  "AUTH_REQUIRED": "Authentication required, please provide a valid token",
+  "USAGE_LIMIT_EXCEEDED": "You have exceeded your daily usage limit. Please upgrade your plan.",
+  "ANALYSIS_LIMIT_EXCEEDED": "You have exceeded your daily analysis limit. Please upgrade your plan.",
+  "COUPON_LIMIT_EXCEEDED": "You have exceeded your daily coupon limit. Please upgrade your plan.",
+  "INVALID_PLAN_TYPE": "Invalid plan type. Must be free, plus, or premium.",
+  "MATCH_NOT_FOUND": "Match not found",
+  "PREDICTION_GENERATION_FAILED": "Failed to generate prediction",
+  "SMART_COUPON_GENERATION_FAILED": "Failed to generate Smart Coupon",
+  "ANALYSIS_FAILED": "None of the provided matches could be analyzed successfully"
 }

src/i18n/tr/common.json

@@ -9,5 +9,12 @@
   "serverError": "Beklenmeyen bir hata oluştu",
   "unauthorized": "Bu işlemi yapmaya yetkiniz yok",
   "forbidden": "Erişim reddedildi",
-  "badRequest": "Geçersiz istek"
+  "badRequest": "Geçersiz istek",
+  "SUCCESS_USER_ROLE_UPDATED": "Kullanıcı rolü güncellendi",
+  "SUCCESS_USER_SUBSCRIPTION_UPDATED": "Kullanıcı aboneliği güncellendi",
+  "SUCCESS_USER_DELETED": "Kullanıcı başarıyla silindi",
+  "SUCCESS_USER_STATUS_UPDATED": "Kullanıcı durumu güncellendi",
+  "SUCCESS_SETTING_UPDATED": "Ayar güncellendi",
+  "SUCCESS_ALL_LIMITS_RESET": "Tüm kullanıcı limitleri sıfırlandı",
+  "SUCCESS_USER_LIMITS_RESET": "Kullanıcı limitleri sıfırlandı"
 }

src/i18n/tr/errors.json

@@ -10,5 +10,13 @@
   "TENANT_NOT_FOUND": "Kiracı bulunamadı",
   "VALIDATION_FAILED": "Doğrulama başarısız",
   "INTERNAL_ERROR": "Bir iç hata oluştu, lütfen daha sonra tekrar deneyin",
-  "AUTH_REQUIRED": "Kimlik doğrulama gerekli, lütfen geçerli bir token sağlayın"
+  "AUTH_REQUIRED": "Kimlik doğrulama gerekli, lütfen geçerli bir token sağlayın",
+  "USAGE_LIMIT_EXCEEDED": "Günlük kullanım limitinizi doldurdunuz. Lütfen paketinizi yükseltin.",
+  "ANALYSIS_LIMIT_EXCEEDED": "Günlük analiz limitinizi doldurdunuz. Lütfen paketinizi yükseltin.",
+  "COUPON_LIMIT_EXCEEDED": "Günlük kupon limitinizi doldurdunuz. Lütfen paketinizi yükseltin.",
+  "INVALID_PLAN_TYPE": "Geçersiz paket tipi. (free, plus, premium olmalıdır)",
+  "MATCH_NOT_FOUND": "Maç bulunamadı",
+  "PREDICTION_GENERATION_FAILED": "Tahmin oluşturulamadı",
+  "SMART_COUPON_GENERATION_FAILED": "Akıllı kupon oluşturulamadı",
+  "ANALYSIS_FAILED": "Sağlanan maçların hiçbiri başarıyla analiz edilemedi"
 }

src/modules/admin/admin.controller.ts

@@ -10,6 +10,7 @@ import {
   UseInterceptors,
   Inject,
   NotFoundException,
+  BadRequestException,
 } from "@nestjs/common";
 import {
   CacheInterceptor,
@@ -36,6 +37,8 @@ import {
 import { plainToInstance } from "class-transformer";
 import { UserResponseDto } from "../users/dto/user.dto";
 import { UserRole } from "@prisma/client";
+import { SubscriptionsService } from "../subscriptions/subscriptions.service";
+import { PlanType } from "../subscriptions/dto/subscription.dto";
 
 @ApiTags("Admin")
 @ApiBearerAuth()
@@ -45,6 +48,7 @@ export class AdminController {
   constructor(
     private readonly prisma: PrismaService,
     @Inject(CACHE_MANAGER) private cacheManager: cacheManager.Cache,
+    private readonly subscriptionsService: SubscriptionsService,
   ) {}
 
   // ================== Users Management ==================
@@ -122,7 +126,7 @@ export class AdminController {
 
     return createSuccessResponse(
       plainToInstance(UserResponseDto, updated),
-      "User status updated",
+      "common.SUCCESS_USER_STATUS_UPDATED",
     );
   }
 
@@ -140,31 +144,7 @@ export class AdminController {
 
     return createSuccessResponse(
       plainToInstance(UserResponseDto, user),
-      "User role updated",
+      "common.SUCCESS_USER_ROLE_UPDATED",
-    );
-  }
-
-  @Put("users/:id/subscription")
-  @ApiOperation({ summary: "Update user subscription" })
-  @SwaggerResponse({ status: 200, type: UserResponseDto })
-  async updateUserSubscription(
-    @Param("id") id: string,
-    @Body()
-    data: { subscriptionStatus: string; subscriptionExpiresAt?: string },
-  ): Promise<ApiResponse<UserResponseDto>> {
-    const user = await this.prisma.user.update({
-      where: { id },
-      data: {
-        subscriptionStatus: data.subscriptionStatus as any,
-        subscriptionExpiresAt: data.subscriptionExpiresAt
-          ? new Date(data.subscriptionExpiresAt)
-          : null,
-      },
-    });
-
-    return createSuccessResponse(
-      plainToInstance(UserResponseDto, user),
-      "User subscription updated",
     );
   }
 
@@ -176,7 +156,7 @@ export class AdminController {
       where: { id },
       data: { deletedAt: new Date() },
     });
-    return createSuccessResponse(null, "User deleted");
+    return createSuccessResponse(null, "common.SUCCESS_USER_DELETED");
   }
 
   // ================== App Settings ==================
@@ -220,7 +200,7 @@ export class AdminController {
     await this.cacheManager.del("app_settings");
     return createSuccessResponse(
       { key: setting.key, value: setting.value || "" },
-      "Setting updated",
+      "common.SUCCESS_SETTING_UPDATED",
     );
   }
 
@@ -274,7 +254,57 @@ export class AdminController {
 
     return createSuccessResponse(
       { count: result.count },
-      "All usage limits reset",
+      "common.SUCCESS_ALL_LIMITS_RESET",
+    );
+  }
+
+  @Post("usage-limits/reset/:userId")
+  @ApiOperation({ summary: "Reset usage limits for a single user" })
+  @SwaggerResponse({ status: 200 })
+  async resetUserUsageLimits(
+    @Param("userId") userId: string,
+  ): Promise<ApiResponse<null>> {
+    const user = await this.prisma.user.findUnique({ where: { id: userId } });
+    if (!user) throw new NotFoundException("USER_NOT_FOUND");
+
+    await this.prisma.usageLimit.update({
+      where: { userId },
+      data: {
+        analysisCount: 0,
+        couponCount: 0,
+        lastResetDate: new Date(),
+      },
+    });
+
+    return createSuccessResponse(null, "common.SUCCESS_USER_LIMITS_RESET");
+  }
+
+  @Put("users/:userId/subscription")
+  @ApiOperation({ summary: "Update a user's subscription tier" })
+  @SwaggerResponse({ status: 200 })
+  async updateUserSubscription(
+    @Param("userId") userId: string,
+    @Body() data: { plan: string },
+  ): Promise<ApiResponse<null>> {
+    const user = await this.prisma.user.findUnique({ where: { id: userId } });
+    if (!user) throw new NotFoundException("USER_NOT_FOUND");
+
+    const validPlans = [PlanType.FREE, PlanType.PLUS, PlanType.PREMIUM];
+    const newPlan = data.plan as PlanType;
+    if (!validPlans.includes(newPlan)) {
+      throw new BadRequestException("INVALID_PLAN_TYPE");
+    }
+
+    await this.prisma.user.update({
+      where: { id: userId },
+      data: { subscriptionStatus: newPlan },
+    });
+
+    await this.subscriptionsService.syncLimitsWithPlan(userId, newPlan);
+
+    return createSuccessResponse(
+      null,
+      "common.SUCCESS_USER_SUBSCRIPTION_UPDATED",
     );
   }
 
@@ -294,7 +324,9 @@ export class AdminController {
     ] = await Promise.all([
       this.prisma.user.count(),
       this.prisma.user.count({ where: { isActive: true } }),
-      this.prisma.user.count({ where: { subscriptionStatus: "active" } }),
+      this.prisma.user.count({
+        where: { subscriptionStatus: { in: ["plus", "premium"] } },
+      }),
       this.prisma.match.count(),
       this.prisma.prediction.count(),
       this.prisma.userCoupon.count(),

src/modules/admin/admin.module.ts

@@ -1,7 +1,9 @@
 import { Module } from "@nestjs/common";
 import { AdminController } from "./admin.controller";
+import { SubscriptionsModule } from "../subscriptions/subscriptions.module";
 
 @Module({
+  imports: [SubscriptionsModule],
   controllers: [AdminController],
 })
 export class AdminModule {}

src/modules/analysis/analysis.controller.ts

@@ -59,7 +59,7 @@ export class AnalysisController {
     );
 
     if (!canProceed) {
-      throw new ForbiddenException("You have exceeded your daily usage limit");
+      throw new ForbiddenException("USAGE_LIMIT_EXCEEDED");
     }
 
     // Run analysis
@@ -68,7 +68,7 @@ export class AnalysisController {
     if (!result) {
       return {
         success: false,
-        message: "None of the provided matches could be analyzed successfully",
+        message: "ANALYSIS_FAILED",
       };
     }
 

src/modules/analysis/analysis.service.ts

@@ -84,7 +84,7 @@ export class AnalysisService {
   }
 
   /**
-   * Check user usage limit
+   * Check user usage limit (plan-aware via UsageLimit table)
    */
   async checkUsageLimit(
     userId: string,
@@ -96,24 +96,23 @@ export class AnalysisService {
     });
 
     if (!usageLimit) {
-      // Create default limit
+      // Create default limit with free-tier maxes
       await this.prisma.usageLimit.create({
         data: {
           userId,
           analysisCount: 0,
           couponCount: 0,
+          maxAnalyses: 3,
+          maxCoupons: 1,
           lastResetDate: new Date(),
         },
       });
       return true;
     }
 
-    // Check limits (default: 10 analyses, 3 coupons per day)
+    // Use plan-aware limits from DB (set by SubscriptionsService.syncLimitsWithPlan)
-    const user = await this.prisma.user.findUnique({ where: { id: userId } });
+    const maxAnalyses = usageLimit.maxAnalyses ?? 3;
-    const isPremium = user?.subscriptionStatus === "active";
+    const maxCoupons = usageLimit.maxCoupons ?? 1;
-
-    const maxAnalyses = isPremium ? 50 : 10;
-    const maxCoupons = isPremium ? 10 : 3;
 
     if (isCoupon) {
       return usageLimit.couponCount < maxCoupons;

src/modules/leagues/leagues.service.ts

@@ -188,10 +188,7 @@ export class LeaguesService {
           { homeTeamId: teamId1, awayTeamId: teamId2 },
           { homeTeamId: teamId2, awayTeamId: teamId1 },
         ],
-        AND: [
+        AND: [{ scoreHome: { not: null } }, { scoreAway: { not: null } }],
-          { scoreHome: { not: null } },
-          { scoreAway: { not: null } },
-        ],
       },
       include: {
         homeTeam: true,

src/modules/predictions/dto/index.ts

@@ -148,6 +148,27 @@ export class MatchPickDto {
   @ApiProperty({ required: false, default: 0 })
   implied_prob?: number;
 
+  @ApiProperty({ required: false, default: 0 })
+  model_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  model_edge?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  calibrated_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_sample?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_edge?: number;
+
+  @ApiProperty({ required: false, default: false })
+  odds_band_aligned?: boolean;
+
   @ApiProperty()
   play_score: number;
 
@@ -171,6 +192,9 @@ export class MatchPickDto {
     enum: ["CORE", "VALUE", "LEAN", "LONGSHOT", "PASS"],
   })
   signal_tier?: SignalTier;
+
+  @ApiProperty({ required: false, default: false })
+  is_guaranteed?: boolean;
 }
 
 export class MatchBetAdviceDto {
@@ -227,6 +251,27 @@ export class MatchBetSummaryItemDto {
   @ApiProperty({ required: false, default: 0 })
   implied_prob?: number;
 
+  @ApiProperty({ required: false, default: 0 })
+  model_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  model_edge?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  calibrated_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_sample?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_edge?: number;
+
+  @ApiProperty({ required: false, default: false })
+  odds_band_aligned?: boolean;
+
   @ApiProperty({ required: false, default: 0 })
   odds?: number;
 

src/modules/predictions/predictions.controller.ts

@@ -21,12 +21,17 @@ import {
   GeneratePredictionDto,
   SmartCouponRequestDto,
 } from "./dto/predictions-request.dto";
-import { Public } from "src/common/decorators";
+import { CurrentUser } from "src/common/decorators";
+import { AnalysisService } from "../analysis/analysis.service";
+import { ForbiddenException } from "@nestjs/common";
 
 @ApiTags("Predictions")
 @Controller("predictions")
 export class PredictionsController {
-  constructor(private readonly predictionsService: PredictionsService) {}
+  constructor(
+    private readonly predictionsService: PredictionsService,
+    private readonly analysisService: AnalysisService,
+  ) {}
 
   /**
    * GET /predictions/health
@@ -93,7 +98,6 @@ export class PredictionsController {
    * Get prediction for a specific match
    */
   @Get(":matchId")
-  @Public()
   @ApiOperation({ summary: "Get prediction for a specific match" })
   @ApiParam({ name: "matchId", description: "Match ID" })
   @ApiResponse({
@@ -103,11 +107,23 @@ export class PredictionsController {
     type: MatchPredictionDto,
   })
   @ApiResponse({ status: 404, description: "Match not found" })
+  @ApiResponse({ status: 403, description: "Daily limit exceeded" })
   async getPrediction(
     @Param("matchId") matchId: string,
+    @CurrentUser() user: any,
   ): Promise<MatchPredictionDto> {
+    const canProceed = await this.analysisService.checkUsageLimit(
+      user.id,
+      false,
+      1,
+    );
+    if (!canProceed) {
+      throw new ForbiddenException("ANALYSIS_LIMIT_EXCEEDED");
+    }
+
     const cached = await this.predictionsService.getCachedPrediction(matchId);
     if (cached) {
+      await this.analysisService.recordUsage(user.id, false);
       return cached;
     }
 
@@ -115,9 +131,10 @@ export class PredictionsController {
     const prediction = await this.predictionsService.getPredictionById(matchId);
 
     if (!prediction) {
-      throw new NotFoundException(`Match not found: ${matchId}`);
+      throw new NotFoundException("MATCH_NOT_FOUND");
     }
 
+    await this.analysisService.recordUsage(user.id, false);
     return prediction;
   }
 
@@ -129,17 +146,29 @@ export class PredictionsController {
   @HttpCode(HttpStatus.OK)
   @ApiOperation({ summary: "Generate prediction with provided match data" })
   @ApiResponse({ status: 200, type: MatchPredictionDto })
+  @ApiResponse({ status: 403, description: "Daily limit exceeded" })
   async generatePrediction(
+    @CurrentUser() user: any,
     @Body() dto: GeneratePredictionDto,
   ): Promise<MatchPredictionDto> {
+    const canProceed = await this.analysisService.checkUsageLimit(
+      user.id,
+      false,
+      1,
+    );
+    if (!canProceed) {
+      throw new ForbiddenException("ANALYSIS_LIMIT_EXCEEDED");
+    }
+
     const prediction = await this.predictionsService.getPredictionWithData({
       matchId: dto.matchId,
     });
 
     if (!prediction) {
-      throw new NotFoundException("Failed to generate prediction");
+      throw new NotFoundException("PREDICTION_GENERATION_FAILED");
     }
 
+    await this.analysisService.recordUsage(user.id, false);
     return prediction;
   }
 
@@ -157,7 +186,20 @@ export class PredictionsController {
     description: "Smart coupon generated successfully",
     schema: { type: "object" },
   })
-  async generateSmartCoupon(@Body() dto: SmartCouponRequestDto): Promise<any> {
+  @ApiResponse({ status: 403, description: "Daily limit exceeded" })
+  async generateSmartCoupon(
+    @CurrentUser() user: any,
+    @Body() dto: SmartCouponRequestDto,
+  ): Promise<any> {
+    const canProceed = await this.analysisService.checkUsageLimit(
+      user.id,
+      true,
+      dto.matchIds?.length || 1,
+    );
+    if (!canProceed) {
+      throw new ForbiddenException("COUPON_LIMIT_EXCEEDED");
+    }
+
     const coupon = await this.predictionsService.getSmartCoupon(
       dto.matchIds,
       dto.strategy || "BALANCED",
@@ -168,9 +210,10 @@ export class PredictionsController {
     );
 
     if (!coupon) {
-      throw new NotFoundException("Failed to generate Smart Coupon");
+      throw new NotFoundException("SMART_COUPON_GENERATION_FAILED");
     }
 
+    await this.analysisService.recordUsage(user.id, true);
     return coupon;
   }
 }

src/modules/predictions/predictions.module.ts

@@ -10,6 +10,7 @@ import { PredictionsQueue } from "./queues/predictions.queue";
 import { PredictionsProcessor } from "./queues/predictions.processor";
 import { PREDICTIONS_QUEUE } from "./queues/predictions.types";
 import { FeederModule } from "../feeder/feeder.module";
+import { AnalysisModule } from "../analysis/analysis.module";
 
 const redisEnabled = process.env.REDIS_ENABLED === "true";
 
@@ -25,6 +26,7 @@ const redisEnabled = process.env.REDIS_ENABLED === "true";
       : []),
     MatchesModule,
     FeederModule,
+    AnalysisModule,
   ],
   controllers: [PredictionsController],
   providers: [

src/modules/predictions/predictions.service.ts

@@ -60,7 +60,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     confidence_interval_too_wide_for_main_pick:
       "Ana seçim için güven aralığı çok geniş",
     confidence_band_low: "Güven bandı düşük",
-    playable_edge_found: "Oynanabilir avantaj bulundu",
+    playable_edge_found: "Model avantaj sinyali bulundu",
     market_signal_dominant: "Piyasa sinyali baskın",
     team_form_signal_dominant: "Takım formuna dayalı sinyaller çok baskın",
     lineup_signal_strong: "İlk on bir sinyali güçlü",
@@ -77,7 +77,12 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     limited_data_confidence: "Veri kısıtlı olduğu için güven sınırlı",
     data_quality_issue: "Veri kalitesi sorunu var",
     high_risk_low_data_quality: "Risk yüksek, veri kalitesi düşük",
-    insufficient_play_score: "Oynanabilirlik puanı yetersiz",
+    insufficient_play_score: "Model sinyali yetersiz",
+    odds_band_confirms_value: "Tarihsel oran bandı değeri doğruluyor",
+    odds_band_sample_too_low: "Tarihsel oran bandı örneklemi yetersiz",
+    odds_band_missing_probability: "Tarihsel oran bandı olasılığı yok",
+    odds_band_unavailable: "Tarihsel oran bandı kullanılamıyor",
+    odds_band_not_aligned: "Model ve tarihsel oran bandı aynı yönde değil",
     no_bet_conditions_met: "Bahis koşulları oluşmadı",
     market_passed_all_gates: "Market tüm güvenlik kontrollerini geçti",
     no_ev_edge_minimum_stake:
@@ -129,10 +134,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     private readonly feederService: FeederService,
     @Optional() private readonly predictionsQueue?: PredictionsQueue,
   ) {
-    this.aiEngineUrl = this.configService.get(
+    this.aiEngineUrl = this.resolveAiEngineUrl();
-      "AI_ENGINE_URL",
-      "http://localhost:8000",
-    );
     this.aiEngineClient = new AiEngineClient({
       baseUrl: this.aiEngineUrl,
       logger: this.logger,
@@ -421,6 +423,59 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     }
   }
 
+  private resolveAiEngineUrl(): string {
+    const configuredUrl = this.configService.get(
+      "AI_ENGINE_URL",
+      "http://localhost:8000",
+    );
+    const localEnvUrl = this.readLocalEnvValue("AI_ENGINE_URL");
+
+    if (
+      process.env.NODE_ENV !== "production" &&
+      localEnvUrl &&
+      localEnvUrl !== configuredUrl &&
+      this.isLocalhostUrl(configuredUrl) &&
+      this.isLocalhostUrl(localEnvUrl)
+    ) {
+      this.logger.warn(
+        `AI_ENGINE_URL inherited from parent process (${configuredUrl}) differs from .env.local (${localEnvUrl}); using .env.local for local development`,
+      );
+      return localEnvUrl;
+    }
+
+    return configuredUrl;
+  }
+
+  private readLocalEnvValue(key: string): string | null {
+    const filePath = path.join(process.cwd(), ".env.local");
+    if (!fs.existsSync(filePath)) {
+      return null;
+    }
+
+    const line = fs
+      .readFileSync(filePath, "utf8")
+      .split(/\r?\n/u)
+      .find((entry) => entry.trim().startsWith(`${key}=`));
+
+    if (!line) {
+      return null;
+    }
+
+    return line
+      .slice(line.indexOf("=") + 1)
+      .trim()
+      .replace(/^['"]|['"]$/gu, "");
+  }
+
+  private isLocalhostUrl(value: string): boolean {
+    try {
+      const url = new URL(value);
+      return ["localhost", "127.0.0.1", "::1"].includes(url.hostname);
+    } catch {
+      return false;
+    }
+  }
+
   private async getMatchContext(matchId: string): Promise<MatchContext> {
     const match = await this.prisma.match.findUnique({
       where: { id: matchId },
@@ -705,6 +760,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       ),
       confidence_interval: interval,
       signal_tier: this.classifySignalTier(record, interval),
+      is_guaranteed: false,
     };
   }
 
@@ -793,7 +849,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
 
     const evMatch = normalized.match(/^ev_edge_([-+][\d.]+%)_grade_(\w)$/);
     if (evMatch) {
-      return `Beklenen avantaj ${evMatch[1]} (Not ${evMatch[2]})`;
+      return `Teorik avantaj sinyali: Not ${evMatch[2]}`;
     }
 
     const negativeEdgeMatch = normalized.match(
@@ -803,6 +859,13 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       return `Model avantajı negatif (${negativeEdgeMatch[1]})`;
     }
 
+    const bandNoValueMatch = normalized.match(
+      /^odds_band_no_value_([-+]?[\d.]+)$/,
+    );
+    if (bandNoValueMatch) {
+      return `Tarihsel oran bandı değeri doğrulamadı (${bandNoValueMatch[1]})`;
+    }
+
     const edgeThresholdMatch = normalized.match(
       /^below_market_edge_threshold_([-+]?[\d.]+)$/,
     );
@@ -1291,8 +1354,14 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
   }
 
   private extractCooldownMs(detail: unknown): number {
-    if (detail && typeof detail === "object" && "cooldownRemainingMs" in detail) {
+    if (
-      return Number((detail as Record<string, unknown>).cooldownRemainingMs) || 0;
+      detail &&
+      typeof detail === "object" &&
+      "cooldownRemainingMs" in detail
+    ) {
+      return (
+        Number((detail as Record<string, unknown>).cooldownRemainingMs) || 0
+      );
     }
 
     if (typeof detail === "string") {
@@ -1514,8 +1583,15 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
           pick: item.pick,
           playable: item.playable,
           bet_grade: item.bet_grade,
+          odds: item.odds,
+          model_edge: item.model_edge,
+          calibrated_probability: item.calibrated_probability,
           calibrated_confidence: item.calibrated_confidence,
           ev_edge: item.ev_edge ?? 0,
+          odds_band_probability: item.odds_band_probability,
+          odds_band_sample: item.odds_band_sample,
+          odds_band_edge: item.odds_band_edge,
+          odds_band_aligned: item.odds_band_aligned,
           stake_units: item.stake_units,
         }))
       : [];
@@ -1531,8 +1607,15 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
             pick: payload.main_pick.pick,
             playable: payload.main_pick.playable,
             bet_grade: payload.main_pick.bet_grade,
+            odds: payload.main_pick.odds,
+            model_edge: payload.main_pick.model_edge,
+            calibrated_probability: payload.main_pick.calibrated_probability,
             calibrated_confidence: payload.main_pick.calibrated_confidence,
             ev_edge: payload.main_pick.ev_edge ?? 0,
+            odds_band_probability: payload.main_pick.odds_band_probability,
+            odds_band_sample: payload.main_pick.odds_band_sample,
+            odds_band_edge: payload.main_pick.odds_band_edge,
+            odds_band_aligned: payload.main_pick.odds_band_aligned,
             stake_units: payload.main_pick.stake_units,
           }
         : null,
@@ -1542,6 +1625,8 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
             pick: payload.value_pick.pick,
             playable: payload.value_pick.playable,
             bet_grade: payload.value_pick.bet_grade,
+            odds: payload.value_pick.odds,
+            model_edge: payload.value_pick.model_edge,
             calibrated_confidence: payload.value_pick.calibrated_confidence,
             ev_edge: payload.value_pick.ev_edge ?? 0,
           }

src/modules/subscriptions/dto/subscription.dto.ts

@@ -0,0 +1,178 @@
+import {
+  IsString,
+  IsOptional,
+  IsEnum,
+  IsDateString,
+  IsInt,
+} from "class-validator";
+import { ApiProperty, ApiPropertyOptional } from "@nestjs/swagger";
+import { Exclude, Expose, Type } from "class-transformer";
+
+export enum PlanType {
+  FREE = "free",
+  PLUS = "plus",
+  PREMIUM = "premium",
+}
+
+export enum BillingIntervalType {
+  MONTHLY = "monthly",
+  YEARLY = "yearly",
+}
+
+/**
+ * Plan feature limits configuration
+ */
+export const PLAN_LIMITS: Record<
+  PlanType,
+  { maxAnalyses: number; maxCoupons: number }
+> = {
+  [PlanType.FREE]: { maxAnalyses: 3, maxCoupons: 1 },
+  [PlanType.PLUS]: { maxAnalyses: 25, maxCoupons: 5 },
+  [PlanType.PREMIUM]: { maxAnalyses: 999, maxCoupons: 999 },
+};
+
+/**
+ * Plan display information
+ */
+export interface PlanInfo {
+  id: PlanType;
+  name: string;
+  description: string;
+  monthlyPrice: number;
+  yearlyPrice: number;
+  currency: string;
+  features: string[];
+  limits: { maxAnalyses: number; maxCoupons: number };
+  highlighted: boolean;
+}
+
+export const PLANS: readonly PlanInfo[] = [
+  {
+    id: PlanType.FREE,
+    name: "Free",
+    description: "Temel analiz özellikleri",
+    monthlyPrice: 0,
+    yearlyPrice: 0,
+    currency: "TRY",
+    features: ["Günlük 3 analiz", "Günlük 1 kupon", "Temel maç istatistikleri"],
+    limits: PLAN_LIMITS[PlanType.FREE],
+    highlighted: false,
+  },
+  {
+    id: PlanType.PLUS,
+    name: "Plus",
+    description: "Detaylı analiz ve daha fazla kupon",
+    monthlyPrice: 99,
+    yearlyPrice: 999,
+    currency: "TRY",
+    features: [
+      "Günlük 25 analiz",
+      "Günlük 5 kupon",
+      "AI detaylı analiz",
+      "H2H karşılaştırma",
+      "Reklamsız deneyim",
+    ],
+    limits: PLAN_LIMITS[PlanType.PLUS],
+    highlighted: true,
+  },
+  {
+    id: PlanType.PREMIUM,
+    name: "Premium",
+    description: "Sınırsız erişim ve özel özellikler",
+    monthlyPrice: 249,
+    yearlyPrice: 2499,
+    currency: "TRY",
+    features: [
+      "Sınırsız analiz",
+      "Sınırsız kupon",
+      "AI detaylı analiz",
+      "H2H karşılaştırma",
+      "Kupon Builder",
+      "Spor Toto analiz",
+      "Reklamsız deneyim",
+      "Öncelikli destek",
+    ],
+    limits: PLAN_LIMITS[PlanType.PREMIUM],
+    highlighted: false,
+  },
+] as const;
+
+// ── Response DTOs ──
+
+@Exclude()
+export class UsageLimitResponseDto {
+  @Expose()
+  analysisCount: number;
+
+  @Expose()
+  couponCount: number;
+
+  @Expose()
+  maxAnalyses: number;
+
+  @Expose()
+  maxCoupons: number;
+}
+
+@Exclude()
+export class SubscriptionResponseDto {
+  @Expose()
+  id: string;
+
+  @Expose()
+  plan: string;
+
+  @Expose()
+  billingInterval: string | null;
+
+  @Expose()
+  currentPeriodStart: Date | null;
+
+  @Expose()
+  currentPeriodEnd: Date | null;
+
+  @Expose()
+  cancelledAt: Date | null;
+
+  @Expose()
+  cancelEffectiveDate: Date | null;
+
+  @Expose()
+  paddlePriceId: string | null;
+
+  @Expose()
+  createdAt: Date;
+
+  @Expose()
+  updatedAt: Date;
+}
+
+// ── Request DTOs ──
+
+export class CreateCheckoutDto {
+  @ApiProperty({
+    enum: PlanType,
+    example: PlanType.PLUS,
+    description: "Target plan",
+  })
+  @IsEnum(PlanType)
+  plan: PlanType;
+
+  @ApiProperty({
+    enum: BillingIntervalType,
+    example: BillingIntervalType.MONTHLY,
+    description: "Billing interval",
+  })
+  @IsEnum(BillingIntervalType)
+  billingInterval: BillingIntervalType;
+}
+
+export class CancelSubscriptionDto {
+  @ApiPropertyOptional({
+    description: "Reason for cancellation",
+    example: "Too expensive",
+  })
+  @IsOptional()
+  @IsString()
+  reason?: string;
+}

src/modules/subscriptions/paddle.service.ts

@@ -0,0 +1,209 @@
+import { Injectable, Logger } from "@nestjs/common";
+import { ConfigService } from "@nestjs/config";
+import * as crypto from "crypto";
+
+export interface PaddleWebhookEvent {
+  event_id: string;
+  event_type: string;
+  occurred_at: string;
+  notification_id: string;
+  data: Record<string, unknown>;
+}
+
+interface PaddleTransactionResponse {
+  data: {
+    id: string;
+    customer_id: string;
+    status: string;
+  };
+}
+
+@Injectable()
+export class PaddleService {
+  private readonly logger = new Logger(PaddleService.name);
+  private readonly apiKey: string;
+  private readonly webhookSecret: string;
+  private readonly environment: "sandbox" | "production";
+  private readonly baseUrl: string;
+
+  constructor(private readonly config: ConfigService) {
+    this.apiKey = this.config.get<string>("PADDLE_API_KEY", "");
+    this.webhookSecret = this.config.get<string>("PADDLE_WEBHOOK_SECRET", "");
+    this.environment = this.config.get<"sandbox" | "production">(
+      "PADDLE_ENVIRONMENT",
+      "sandbox",
+    );
+    this.baseUrl =
+      this.environment === "production"
+        ? "https://api.paddle.com"
+        : "https://sandbox-api.paddle.com";
+  }
+
+  /**
+   * Verify Paddle webhook signature (Paddle Billing v2)
+   */
+  verifyWebhookSignature(rawBody: string, signatureHeader: string): boolean {
+    if (!this.webhookSecret) {
+      this.logger.warn(
+        "PADDLE_WEBHOOK_SECRET not configured, skipping verification",
+      );
+      return false;
+    }
+
+    try {
+      // Paddle signature format: ts=TIMESTAMP;h1=HASH
+      const parts = signatureHeader.split(";");
+      const tsValue = parts
+        .find((p) => p.startsWith("ts="))
+        ?.replace("ts=", "");
+      const h1Value = parts
+        .find((p) => p.startsWith("h1="))
+        ?.replace("h1=", "");
+
+      if (!tsValue || !h1Value) {
+        this.logger.warn("Invalid Paddle signature format");
+        return false;
+      }
+
+      // Compute expected signature: HMAC-SHA256(ts + ':' + rawBody)
+      const signedPayload = `${tsValue}:${rawBody}`;
+      const expectedSignature = crypto
+        .createHmac("sha256", this.webhookSecret)
+        .update(signedPayload)
+        .digest("hex");
+
+      return crypto.timingSafeEqual(
+        Buffer.from(h1Value),
+        Buffer.from(expectedSignature),
+      );
+    } catch (error: unknown) {
+      const err = error as Error;
+      this.logger.error(
+        `Webhook signature verification failed: ${err.message}`,
+      );
+      return false;
+    }
+  }
+
+  /**
+   * Cancel a Paddle subscription
+   */
+  async cancelSubscription(
+    paddleSubscriptionId: string,
+    effectiveFrom:
+      | "immediately"
+      | "next_billing_period" = "next_billing_period",
+  ): Promise<void> {
+    const url = `${this.baseUrl}/subscriptions/${paddleSubscriptionId}/cancel`;
+
+    const response = await fetch(url, {
+      method: "POST",
+      headers: {
+        Authorization: `Bearer ${this.apiKey}`,
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({ effective_from: effectiveFrom }),
+    });
+
+    if (!response.ok) {
+      const body = await response.text();
+      this.logger.error(`Paddle cancel failed: ${response.status} ${body}`);
+      throw new Error(
+        `Failed to cancel Paddle subscription: ${response.status}`,
+      );
+    }
+
+    this.logger.log(
+      `Paddle subscription ${paddleSubscriptionId} cancelled (effective: ${effectiveFrom})`,
+    );
+  }
+
+  /**
+   * Get subscription details from Paddle
+   */
+  async getSubscription(
+    paddleSubscriptionId: string,
+  ): Promise<Record<string, unknown>> {
+    const url = `${this.baseUrl}/subscriptions/${paddleSubscriptionId}`;
+
+    const response = await fetch(url, {
+      method: "GET",
+      headers: {
+        Authorization: `Bearer ${this.apiKey}`,
+        "Content-Type": "application/json",
+      },
+    });
+
+    if (!response.ok) {
+      throw new Error(`Failed to get Paddle subscription: ${response.status}`);
+    }
+
+    const data = (await response.json()) as { data: Record<string, unknown> };
+    return data.data;
+  }
+
+  /**
+   * Map Paddle price ID to our internal plan
+   */
+  mapPriceIdToPlan(priceId: string): {
+    plan: "plus" | "premium";
+    interval: "monthly" | "yearly";
+  } | null {
+    const mapping: Record<
+      string,
+      { plan: "plus" | "premium"; interval: "monthly" | "yearly" }
+    > = {
+      [this.config.get<string>("PADDLE_PLUS_MONTHLY_PRICE_ID", "")]: {
+        plan: "plus",
+        interval: "monthly",
+      },
+      [this.config.get<string>("PADDLE_PLUS_YEARLY_PRICE_ID", "")]: {
+        plan: "plus",
+        interval: "yearly",
+      },
+      [this.config.get<string>("PADDLE_PREMIUM_MONTHLY_PRICE_ID", "")]: {
+        plan: "premium",
+        interval: "monthly",
+      },
+      [this.config.get<string>("PADDLE_PREMIUM_YEARLY_PRICE_ID", "")]: {
+        plan: "premium",
+        interval: "yearly",
+      },
+    };
+
+    // Remove empty key (from missing env vars)
+    delete mapping[""];
+
+    return mapping[priceId] ?? null;
+  }
+
+  /**
+   * Get the Paddle price ID for a given plan and interval
+   */
+  getPriceId(plan: "plus" | "premium", interval: "monthly" | "yearly"): string {
+    const key = `PADDLE_${plan.toUpperCase()}_${interval.toUpperCase()}_PRICE_ID`;
+    const priceId = this.config.get<string>(key, "");
+
+    if (!priceId) {
+      throw new Error(
+        `Price ID not configured for ${plan} ${interval} (env: ${key})`,
+      );
+    }
+
+    return priceId;
+  }
+
+  /**
+   * Get the client-side token for Paddle.js
+   */
+  getClientToken(): string {
+    return this.config.get<string>("PADDLE_CLIENT_TOKEN", "");
+  }
+
+  /**
+   * Get the Paddle environment
+   */
+  getEnvironment(): "sandbox" | "production" {
+    return this.environment;
+  }
+}

src/modules/subscriptions/subscriptions.controller.ts

@@ -0,0 +1,186 @@
+import {
+  Controller,
+  Get,
+  Post,
+  Body,
+  Req,
+  Res,
+  HttpCode,
+  HttpStatus,
+  Logger,
+  ForbiddenException,
+} from "@nestjs/common";
+import type { RawBodyRequest } from "@nestjs/common";
+import {
+  ApiTags,
+  ApiBearerAuth,
+  ApiOperation,
+  ApiOkResponse,
+} from "@nestjs/swagger";
+import type { Request, Response } from "express";
+import { CurrentUser, Public } from "../../common/decorators";
+import type { ApiResponse } from "../../common/types/api-response.type";
+import {
+  createSuccessResponse,
+  createErrorResponse,
+} from "../../common/types/api-response.type";
+import { SubscriptionsService } from "./subscriptions.service";
+import { PaddleService, PaddleWebhookEvent } from "./paddle.service";
+import {
+  CreateCheckoutDto,
+  CancelSubscriptionDto,
+  SubscriptionResponseDto,
+  PlanInfo,
+  PlanType,
+} from "./dto/subscription.dto";
+
+interface AuthenticatedUser {
+  id: string;
+  email: string;
+  role: string;
+}
+
+@ApiTags("Subscriptions")
+@Controller("subscriptions")
+export class SubscriptionsController {
+  private readonly logger = new Logger(SubscriptionsController.name);
+
+  constructor(
+    private readonly subscriptionsService: SubscriptionsService,
+    private readonly paddleService: PaddleService,
+  ) {}
+
+  /**
+   * GET /subscriptions/plans — Get all available plans (public)
+   */
+  @Public()
+  @Get("plans")
+  @ApiOperation({ summary: "Get all available subscription plans" })
+  @ApiOkResponse({ description: "List of available plans" })
+  getPlans(): ApiResponse<readonly PlanInfo[]> {
+    const plans = this.subscriptionsService.getPlans();
+    return createSuccessResponse(plans, "Plans retrieved");
+  }
+
+  /**
+   * GET /subscriptions/me — Get current user subscription
+   */
+  @ApiBearerAuth()
+  @Get("me")
+  @ApiOperation({ summary: "Get current user subscription status" })
+  async getMySubscription(
+    @CurrentUser() user: AuthenticatedUser,
+  ): Promise<ApiResponse<SubscriptionResponseDto | null>> {
+    const subscription = await this.subscriptionsService.getCurrentSubscription(
+      user.id,
+    );
+    return createSuccessResponse(subscription, "Subscription retrieved");
+  }
+
+  /**
+   * POST /subscriptions/checkout — Get checkout config for Paddle.js
+   */
+  @ApiBearerAuth()
+  @Post("checkout")
+  @ApiOperation({
+    summary: "Get Paddle checkout configuration for a plan",
+  })
+  async getCheckoutConfig(
+    @CurrentUser() user: AuthenticatedUser,
+    @Body() dto: CreateCheckoutDto,
+  ): Promise<
+    ApiResponse<{
+      priceId: string;
+      clientToken: string;
+      environment: string;
+      userId: string;
+    }>
+  > {
+    if (dto.plan === PlanType.FREE) {
+      throw new ForbiddenException("Cannot checkout for free plan");
+    }
+
+    const config = this.subscriptionsService.getCheckoutConfig(
+      dto.plan,
+      dto.billingInterval,
+    );
+
+    return createSuccessResponse(
+      {
+        ...config,
+        userId: user.id,
+      },
+      "Checkout config ready",
+    );
+  }
+
+  /**
+   * POST /subscriptions/cancel — Cancel current subscription
+   */
+  @ApiBearerAuth()
+  @Post("cancel")
+  @ApiOperation({ summary: "Cancel the current subscription" })
+  async cancelSubscription(
+    @CurrentUser() user: AuthenticatedUser,
+    @Body() _dto: CancelSubscriptionDto,
+  ): Promise<ApiResponse<null>> {
+    await this.subscriptionsService.cancelSubscription(user.id);
+    return createSuccessResponse(null, "Subscription cancellation requested");
+  }
+
+  /**
+   * POST /subscriptions/webhook/paddle — Paddle webhook receiver
+   *
+   * This endpoint is PUBLIC (no JWT required) — Paddle calls it directly.
+   * Authentication is done via HMAC signature verification.
+   */
+  @Public()
+  @Post("webhook/paddle")
+  @HttpCode(HttpStatus.OK)
+  @ApiOperation({ summary: "Paddle webhook receiver (internal)" })
+  async handlePaddleWebhook(
+    @Req() req: RawBodyRequest<Request>,
+    @Res() res: Response,
+  ): Promise<void> {
+    const signature = req.headers["paddle-signature"] as string | undefined;
+
+    if (!signature) {
+      this.logger.warn("Paddle webhook received without signature");
+      res.status(HttpStatus.BAD_REQUEST).json({ error: "Missing signature" });
+      return;
+    }
+
+    // Get raw body for signature verification
+    const rawBody = req.rawBody?.toString("utf8");
+    if (!rawBody) {
+      this.logger.warn("Paddle webhook received without raw body");
+      res.status(HttpStatus.BAD_REQUEST).json({ error: "Missing body" });
+      return;
+    }
+
+    // Verify signature
+    const isValid = this.paddleService.verifyWebhookSignature(
+      rawBody,
+      signature,
+    );
+
+    if (!isValid) {
+      this.logger.warn("Paddle webhook signature verification failed");
+      res.status(HttpStatus.UNAUTHORIZED).json({ error: "Invalid signature" });
+      return;
+    }
+
+    // Parse and process
+    try {
+      const event = JSON.parse(rawBody) as PaddleWebhookEvent;
+      await this.subscriptionsService.handleWebhookEvent(event);
+      res.status(HttpStatus.OK).json({ received: true });
+    } catch (error: unknown) {
+      const err = error as Error;
+      this.logger.error(`Webhook processing failed: ${err.message}`);
+      res
+        .status(HttpStatus.INTERNAL_SERVER_ERROR)
+        .json({ error: "Processing failed" });
+    }
+  }
+}

src/modules/subscriptions/subscriptions.module.ts

@@ -0,0 +1,13 @@
+import { Module } from "@nestjs/common";
+import { SubscriptionsController } from "./subscriptions.controller";
+import { SubscriptionsService } from "./subscriptions.service";
+import { PaddleService } from "./paddle.service";
+import { DatabaseModule } from "../../database/database.module";
+
+@Module({
+  imports: [DatabaseModule],
+  controllers: [SubscriptionsController],
+  providers: [SubscriptionsService, PaddleService],
+  exports: [SubscriptionsService, PaddleService],
+})
+export class SubscriptionsModule {}

src/modules/subscriptions/subscriptions.service.ts

@@ -0,0 +1,334 @@
+import { Injectable, Logger, NotFoundException } from "@nestjs/common";
+import { PrismaService } from "../../database/prisma.service";
+import { PaddleService, PaddleWebhookEvent } from "./paddle.service";
+import {
+  PlanType,
+  BillingIntervalType,
+  PLAN_LIMITS,
+  PLANS,
+  SubscriptionResponseDto,
+} from "./dto/subscription.dto";
+import { plainToInstance } from "class-transformer";
+
+@Injectable()
+export class SubscriptionsService {
+  private readonly logger = new Logger(SubscriptionsService.name);
+
+  constructor(
+    private readonly prisma: PrismaService,
+    private readonly paddleService: PaddleService,
+  ) {}
+
+  /**
+   * Get current subscription for user
+   */
+  async getCurrentSubscription(
+    userId: string,
+  ): Promise<SubscriptionResponseDto | null> {
+    const subscription = await this.prisma.subscription.findUnique({
+      where: { userId },
+    });
+
+    if (!subscription) {
+      return null;
+    }
+
+    return plainToInstance(SubscriptionResponseDto, subscription);
+  }
+
+  /**
+   * Get or create subscription record for user
+   */
+  async getOrCreateSubscription(userId: string) {
+    let subscription = await this.prisma.subscription.findUnique({
+      where: { userId },
+    });
+
+    if (!subscription) {
+      subscription = await this.prisma.subscription.create({
+        data: {
+          userId,
+          plan: "free",
+        },
+      });
+    }
+
+    return subscription;
+  }
+
+  /**
+   * Get all available plans
+   */
+  getPlans() {
+    return PLANS;
+  }
+
+  /**
+   * Get checkout configuration (client-side token + price ID)
+   */
+  getCheckoutConfig(
+    plan: PlanType,
+    billingInterval: BillingIntervalType,
+  ): { priceId: string; clientToken: string; environment: string } {
+    if (plan === PlanType.FREE) {
+      throw new Error("Cannot checkout for free plan");
+    }
+
+    const paddlePlan = plan as "plus" | "premium";
+    const paddleInterval = billingInterval as "monthly" | "yearly";
+
+    const priceId = this.paddleService.getPriceId(paddlePlan, paddleInterval);
+    const clientToken = this.paddleService.getClientToken();
+    const environment = this.paddleService.getEnvironment();
+
+    return { priceId, clientToken, environment };
+  }
+
+  /**
+   * Handle incoming Paddle webhook event
+   */
+  async handleWebhookEvent(event: PaddleWebhookEvent): Promise<void> {
+    const eventType = event.event_type;
+    const data = event.data;
+
+    this.logger.log(`Processing Paddle webhook: ${eventType}`);
+
+    switch (eventType) {
+      case "subscription.created":
+      case "subscription.updated":
+        await this.handleSubscriptionUpdate(data);
+        break;
+      case "subscription.canceled":
+        await this.handleSubscriptionCancelled(data);
+        break;
+      case "subscription.past_due":
+        await this.handleSubscriptionPastDue(data);
+        break;
+      case "subscription.resumed":
+        await this.handleSubscriptionResumed(data);
+        break;
+      case "transaction.completed":
+        this.logger.log(
+          `Transaction completed: ${(data as Record<string, unknown>).id}`,
+        );
+        break;
+      case "transaction.payment_failed":
+        this.logger.warn(
+          `Payment failed for transaction: ${(data as Record<string, unknown>).id}`,
+        );
+        break;
+      default:
+        this.logger.debug(`Unhandled Paddle event: ${eventType}`);
+    }
+  }
+
+  /**
+   * Cancel subscription for user
+   */
+  async cancelSubscription(userId: string): Promise<void> {
+    const subscription = await this.prisma.subscription.findUnique({
+      where: { userId },
+    });
+
+    if (!subscription?.paddleSubscriptionId) {
+      throw new NotFoundException("No active subscription found");
+    }
+
+    await this.paddleService.cancelSubscription(
+      subscription.paddleSubscriptionId,
+      "next_billing_period",
+    );
+
+    this.logger.log(`Cancellation requested for user ${userId}`);
+  }
+
+  // ── Private Handlers ──
+
+  private async handleSubscriptionUpdate(
+    data: Record<string, unknown>,
+  ): Promise<void> {
+    const paddleSubId = data.id as string;
+    const customerId = data.customer_id as string;
+    const status = data.status as string;
+    const customData = data.custom_data as { userId?: string } | undefined;
+    const items = data.items as Array<{ price: { id: string } }> | undefined;
+    const currentBillingPeriod = data.current_billing_period as
+      | { starts_at: string; ends_at: string }
+      | undefined;
+
+    const userId = customData?.userId;
+    if (!userId) {
+      this.logger.warn(
+        `No userId in custom_data for subscription ${paddleSubId}`,
+      );
+      return;
+    }
+
+    // Determine plan from price ID
+    const priceId = items?.[0]?.price?.id;
+    let plan: PlanType = PlanType.FREE;
+    let interval: BillingIntervalType = BillingIntervalType.MONTHLY;
+
+    if (priceId) {
+      const mapped = this.paddleService.mapPriceIdToPlan(priceId);
+      if (mapped) {
+        plan = mapped.plan as PlanType;
+        interval = mapped.interval as BillingIntervalType;
+      }
+    }
+
+    // Determine effective plan based on Paddle status
+    const effectivePlan =
+      status === "active" || status === "trialing" ? plan : PlanType.FREE;
+
+    // Upsert subscription record
+    await this.prisma.subscription.upsert({
+      where: { userId },
+      update: {
+        paddleSubscriptionId: paddleSubId,
+        paddleCustomerId: customerId,
+        plan: effectivePlan,
+        billingInterval: interval,
+        paddlePriceId: priceId ?? null,
+        currentPeriodStart: currentBillingPeriod?.starts_at
+          ? new Date(currentBillingPeriod.starts_at)
+          : null,
+        currentPeriodEnd: currentBillingPeriod?.ends_at
+          ? new Date(currentBillingPeriod.ends_at)
+          : null,
+        cancelledAt: null,
+        cancelEffectiveDate: null,
+      },
+      create: {
+        userId,
+        paddleSubscriptionId: paddleSubId,
+        paddleCustomerId: customerId,
+        plan: effectivePlan,
+        billingInterval: interval,
+        paddlePriceId: priceId ?? null,
+        currentPeriodStart: currentBillingPeriod?.starts_at
+          ? new Date(currentBillingPeriod.starts_at)
+          : null,
+        currentPeriodEnd: currentBillingPeriod?.ends_at
+          ? new Date(currentBillingPeriod.ends_at)
+          : null,
+      },
+    });
+
+    // Sync user subscription status
+    await this.prisma.user.update({
+      where: { id: userId },
+      data: { subscriptionStatus: effectivePlan },
+    });
+
+    // Sync usage limits with plan
+    await this.syncLimitsWithPlan(userId, effectivePlan);
+
+    this.logger.log(
+      `Subscription updated: user=${userId}, plan=${effectivePlan}, interval=${interval}`,
+    );
+  }
+
+  private async handleSubscriptionCancelled(
+    data: Record<string, unknown>,
+  ): Promise<void> {
+    const paddleSubId = data.id as string;
+    const canceledAt = data.canceled_at as string | undefined;
+    const currentBillingPeriod = data.current_billing_period as
+      | { ends_at: string }
+      | undefined;
+
+    const subscription = await this.prisma.subscription.findUnique({
+      where: { paddleSubscriptionId: paddleSubId },
+    });
+
+    if (!subscription) {
+      this.logger.warn(`Subscription not found for cancel: ${paddleSubId}`);
+      return;
+    }
+
+    const effectiveDate = currentBillingPeriod?.ends_at
+      ? new Date(currentBillingPeriod.ends_at)
+      : new Date();
+
+    await this.prisma.subscription.update({
+      where: { id: subscription.id },
+      data: {
+        plan: "cancelled",
+        cancelledAt: canceledAt ? new Date(canceledAt) : new Date(),
+        cancelEffectiveDate: effectiveDate,
+      },
+    });
+
+    // Downgrade user to free
+    await this.prisma.user.update({
+      where: { id: subscription.userId },
+      data: { subscriptionStatus: "free" },
+    });
+
+    await this.syncLimitsWithPlan(subscription.userId, PlanType.FREE);
+
+    this.logger.log(
+      `Subscription cancelled: user=${subscription.userId}, effective=${effectiveDate.toISOString()}`,
+    );
+  }
+
+  private async handleSubscriptionPastDue(
+    data: Record<string, unknown>,
+  ): Promise<void> {
+    const paddleSubId = data.id as string;
+
+    const subscription = await this.prisma.subscription.findUnique({
+      where: { paddleSubscriptionId: paddleSubId },
+    });
+
+    if (!subscription) {
+      return;
+    }
+
+    await this.prisma.subscription.update({
+      where: { id: subscription.id },
+      data: { plan: "past_due" },
+    });
+
+    await this.prisma.user.update({
+      where: { id: subscription.userId },
+      data: { subscriptionStatus: "past_due" },
+    });
+
+    this.logger.warn(`Subscription past due: user=${subscription.userId}`);
+  }
+
+  private async handleSubscriptionResumed(
+    data: Record<string, unknown>,
+  ): Promise<void> {
+    // Re-process as an update to restore the plan
+    await this.handleSubscriptionUpdate(data);
+  }
+
+  /**
+   * Sync usage limits with plan tier
+   */
+  public async syncLimitsWithPlan(
+    userId: string,
+    plan: PlanType,
+  ): Promise<void> {
+    const limits = PLAN_LIMITS[plan] ?? PLAN_LIMITS[PlanType.FREE];
+
+    await this.prisma.usageLimit.upsert({
+      where: { userId },
+      update: {
+        maxAnalyses: limits.maxAnalyses,
+        maxCoupons: limits.maxCoupons,
+      },
+      create: {
+        userId,
+        analysisCount: 0,
+        couponCount: 0,
+        maxAnalyses: limits.maxAnalyses,
+        maxCoupons: limits.maxCoupons,
+        lastResetDate: new Date(),
+      },
+    });
+  }
+}

src/modules/users/dto/user.dto.ts

@@ -73,7 +73,25 @@ export class ChangePasswordDto {
   newPassword: string;
 }
 
-import { Exclude, Expose } from "class-transformer";
+import { Exclude, Expose, Type } from "class-transformer";
+
+@Exclude()
+export class UsageLimitDto {
+  @Expose()
+  analysisCount: number;
+
+  @Expose()
+  couponCount: number;
+
+  @Expose()
+  maxAnalyses: number;
+
+  @Expose()
+  maxCoupons: number;
+
+  @Expose()
+  lastResetDate: Date;
+}
 
 @Exclude()
 export class UserResponseDto {
@@ -95,9 +113,16 @@ export class UserResponseDto {
   @Expose()
   isActive: boolean;
 
+  @Expose()
+  subscriptionStatus: string;
+
   @Expose()
   createdAt: Date;
 
   @Expose()
   updatedAt: Date;
+
+  @Expose()
+  @Type(() => UsageLimitDto)
+  usageLimit?: UsageLimitDto;
 }

src/tasks/data-fetcher.task.ts

@@ -9,6 +9,7 @@ import * as path from "path";
 import { Prisma } from "@prisma/client";
 import { SidelinedResponse } from "../modules/feeder/feeder.types";
 import {
+  deriveStoredMatchStatus,
   FINISHED_STATE_VALUES_FOR_DB,
   FINISHED_STATUS_VALUES_FOR_DB,
   LIVE_STATE_VALUES_FOR_DB,
@@ -74,6 +75,17 @@ interface LiveLineupsJson {
   away: { xi: unknown[]; subs: unknown[] };
 }
 
+interface PendingPredictionRunForSettlement {
+  id: bigint;
+  matchId: string;
+  engineVersion: string;
+  payloadSummary: unknown;
+  scoreHome: number | null;
+  scoreAway: number | null;
+  htScoreHome: number | null;
+  htScoreAway: number | null;
+}
+
 type SportType = "football" | "basketball";
 
 // ────────────────────────────────────────────────────────────────
@@ -187,6 +199,7 @@ export class DataFetcherTask {
     await this.syncMatchList(today);
     await this.syncMatchList(tomorrow);
     await this.updateLiveScores();
+    await this.settlePredictionRuns();
     await this.fetchOddsForMatches();
     await this.fillMissingLineups();
 
@@ -263,13 +276,23 @@ export class DataFetcherTask {
 
           if (response.data?.data) {
             const matchData = response.data.data;
+            const scoreHome = matchData.homeScore ?? null;
+            const scoreAway = matchData.awayScore ?? null;
+            const storedStatus = deriveStoredMatchStatus({
+              state: matchData.state,
+              status: matchData.status,
+              substate: matchData.substate,
+              scoreHome,
+              scoreAway,
+            });
             await this.prisma.liveMatch.update({
               where: { id: match.id },
               data: {
-                scoreHome: matchData.homeScore ?? null,
+                scoreHome,
-                scoreAway: matchData.awayScore ?? null,
+                scoreAway,
-                state: matchData.state || matchData.status,
+                state: matchData.state || null,
-                status: matchData.status,
+                substate: matchData.substate || null,
+                status: storedStatus,
                 updatedAt: new Date(),
               },
             });
@@ -286,6 +309,300 @@ export class DataFetcherTask {
     }
   }
 
+  private async settlePredictionRuns(): Promise<void> {
+    try {
+      const rows = await this.prisma.$queryRawUnsafe<
+        PendingPredictionRunForSettlement[]
+      >(`
+        SELECT
+          pr.id,
+          pr.match_id AS "matchId",
+          pr.engine_version AS "engineVersion",
+          pr.payload_summary AS "payloadSummary",
+          m.score_home AS "scoreHome",
+          m.score_away AS "scoreAway",
+          m.ht_score_home AS "htScoreHome",
+          m.ht_score_away AS "htScoreAway"
+        FROM prediction_runs pr
+        JOIN matches m ON m.id = pr.match_id
+        WHERE pr.eventual_outcome IS NULL
+          AND m.sport = 'football'
+          AND m.status = 'FT'
+          AND m.score_home IS NOT NULL
+          AND m.score_away IS NOT NULL
+        ORDER BY pr.generated_at ASC
+        LIMIT 500
+      `);
+
+      if (rows.length === 0) return;
+
+      let settled = 0;
+      for (const row of rows) {
+        const result = this.resolvePredictionRunSettlement(row);
+        if (!result) continue;
+        const closingOddsSnapshot = await this.getClosingOddsSnapshot(
+          row.matchId,
+        );
+        const settlementSummary = {
+          settled_at: new Date().toISOString(),
+          model_version: row.engineVersion,
+          outcome: result.outcome,
+          unit_profit: result.unitProfit,
+          final_score: {
+            home: row.scoreHome,
+            away: row.scoreAway,
+          },
+          halftime_score: {
+            home: row.htScoreHome,
+            away: row.htScoreAway,
+          },
+          closing_odds_snapshot: closingOddsSnapshot,
+        };
+
+        await this.prisma.$executeRawUnsafe(
+          `
+            UPDATE prediction_runs
+            SET eventual_outcome = $1,
+                unit_profit = $2,
+                payload_summary = payload_summary || jsonb_build_object('settlement', $3::jsonb)
+            WHERE id = $4
+          `,
+          result.outcome,
+          result.unitProfit,
+          JSON.stringify(settlementSummary),
+          row.id,
+        );
+        settled++;
+      }
+
+      if (settled > 0) {
+        this.logger.log(`Settled ${settled} prediction run(s)`);
+      }
+    } catch (error: unknown) {
+      const message = error instanceof Error ? error.message : String(error);
+      this.logger.warn(`Prediction run settlement skipped: ${message}`);
+    }
+  }
+
+  private async getClosingOddsSnapshot(
+    matchId: string,
+  ): Promise<Record<string, unknown>> {
+    const liveMatch = await this.prisma.liveMatch.findUnique({
+      where: { id: matchId },
+      select: {
+        odds: true,
+        oddsUpdatedAt: true,
+        status: true,
+        state: true,
+        scoreHome: true,
+        scoreAway: true,
+      },
+    });
+
+    if (liveMatch?.odds) {
+      return {
+        source: "live_match",
+        odds: liveMatch.odds,
+        odds_updated_at: liveMatch.oddsUpdatedAt?.toISOString() ?? null,
+        status: liveMatch.status ?? null,
+        state: liveMatch.state ?? null,
+        score_home: liveMatch.scoreHome,
+        score_away: liveMatch.scoreAway,
+      };
+    }
+
+    const categories = await this.prisma.oddCategory.findMany({
+      where: { matchId },
+      select: {
+        name: true,
+        selections: {
+          select: {
+            name: true,
+            oddValue: true,
+            position: true,
+            updatedAt: true,
+          },
+          orderBy: { position: "asc" },
+          take: 12,
+        },
+      },
+      orderBy: { name: "asc" },
+      take: 24,
+    });
+
+    return {
+      source: "odd_selections",
+      category_count: categories.length,
+      categories: categories.map((category) => ({
+        name: category.name,
+        selections: category.selections.map((selection) => ({
+          name: selection.name,
+          odd_value: selection.oddValue,
+          position: selection.position,
+          updated_at: selection.updatedAt?.toISOString() ?? null,
+        })),
+      })),
+    };
+  }
+
+  private resolvePredictionRunSettlement(
+    row: PendingPredictionRunForSettlement,
+  ): { outcome: string; unitProfit: number } | null {
+    const summary = this.asRecord(row.payloadSummary);
+    const mainPick = this.asRecord(summary.main_pick);
+    const market = String(mainPick.market || "");
+    const pick = String(mainPick.pick || "");
+    const playable = mainPick.playable === true;
+    const odds = Number(mainPick.odds || 0);
+
+    if (
+      !market ||
+      !pick ||
+      !playable ||
+      !Number.isFinite(odds) ||
+      odds <= 1.01
+    ) {
+      return { outcome: "NO_BET", unitProfit: 0 };
+    }
+
+    const won = this.isPredictionPickWon({
+      market,
+      pick,
+      scoreHome: row.scoreHome,
+      scoreAway: row.scoreAway,
+      htScoreHome: row.htScoreHome,
+      htScoreAway: row.htScoreAway,
+    });
+
+    if (won === null) return null;
+
+    return {
+      outcome: `${won ? "WON" : "LOST"}:${market}:${pick}`,
+      unitProfit: Number((won ? odds - 1 : -1).toFixed(4)),
+    };
+  }
+
+  private isPredictionPickWon(input: {
+    market: string;
+    pick: string;
+    scoreHome: number | null;
+    scoreAway: number | null;
+    htScoreHome: number | null;
+    htScoreAway: number | null;
+  }): boolean | null {
+    const market = input.market.toUpperCase();
+    const pick = this.normalizePick(input.pick);
+    const scoreHome = input.scoreHome;
+    const scoreAway = input.scoreAway;
+    if (scoreHome === null || scoreAway === null) return null;
+
+    if (market === "MS") {
+      if (pick === "1") return scoreHome > scoreAway;
+      if (pick === "X" || pick === "0") return scoreHome === scoreAway;
+      if (pick === "2") return scoreAway > scoreHome;
+      return null;
+    }
+
+    if (market === "DC") {
+      const normalized = pick.replace("-", "");
+      if (normalized === "1X") return scoreHome >= scoreAway;
+      if (normalized === "X2") return scoreAway >= scoreHome;
+      if (normalized === "12") return scoreHome !== scoreAway;
+      return null;
+    }
+
+    if (market === "BTTS") {
+      const bothScored = scoreHome > 0 && scoreAway > 0;
+      if (pick.includes("VAR") || pick.includes("YES") || pick === "Y") {
+        return bothScored;
+      }
+      if (pick.includes("YOK") || pick.includes("NO") || pick === "N") {
+        return !bothScored;
+      }
+      return null;
+    }
+
+    const goalLine = this.goalLineForMarket(market);
+    if (goalLine !== null) {
+      const total = market.startsWith("HT_")
+        ? this.nullableSum(input.htScoreHome, input.htScoreAway)
+        : scoreHome + scoreAway;
+      if (total === null) return null;
+      if (this.isOverPick(pick)) return total > goalLine;
+      return total < goalLine;
+    }
+
+    if (market === "HT") {
+      const htHome = input.htScoreHome;
+      const htAway = input.htScoreAway;
+      if (htHome === null || htAway === null) return null;
+      if (pick === "1") return htHome > htAway;
+      if (pick === "X" || pick === "0") return htHome === htAway;
+      if (pick === "2") return htAway > htHome;
+    }
+
+    if (market === "HTFT") {
+      const htHome = input.htScoreHome;
+      const htAway = input.htScoreAway;
+      if (htHome === null || htAway === null || !pick.includes("/"))
+        return null;
+      const [htPick, ftPick] = pick.split("/");
+      return (
+        this.isResultPickWon(htPick, htHome, htAway) === true &&
+        this.isResultPickWon(ftPick, scoreHome, scoreAway) === true
+      );
+    }
+
+    return null;
+  }
+
+  private isResultPickWon(
+    pick: string,
+    homeScore: number,
+    awayScore: number,
+  ): boolean | null {
+    if (pick === "1") return homeScore > awayScore;
+    if (pick === "X" || pick === "0") return homeScore === awayScore;
+    if (pick === "2") return awayScore > homeScore;
+    return null;
+  }
+
+  private goalLineForMarket(market: string): number | null {
+    if (market === "OU15") return 1.5;
+    if (market === "OU25") return 2.5;
+    if (market === "OU35") return 3.5;
+    if (market === "HT_OU05") return 0.5;
+    if (market === "HT_OU15") return 1.5;
+    return null;
+  }
+
+  private nullableSum(a: number | null, b: number | null): number | null {
+    if (a === null || b === null) return null;
+    return a + b;
+  }
+
+  private normalizePick(value: string): string {
+    return value
+      .trim()
+      .toUpperCase()
+      .replace(/İ/g, "I")
+      .replace(/Ü/g, "U")
+      .replace(/Ş/g, "S")
+      .replace(/Ğ/g, "G")
+      .replace(/Ö/g, "O")
+      .replace(/Ç/g, "C");
+  }
+
+  private isOverPick(pick: string): boolean {
+    return pick.includes("UST") || pick.includes("OVER");
+  }
+
+  private asRecord(value: unknown): Record<string, unknown> {
+    return value && typeof value === "object" && !Array.isArray(value)
+      ? (value as Record<string, unknown>)
+      : {};
+  }
+
   // Phase 3: Odds + referee + lineups + sidelined
 
   private async fetchOddsForMatches(): Promise<void> {
@@ -705,6 +1022,15 @@ export class DataFetcherTask {
           // Safe score parsing
           const sHome = this.asInt(match.homeScore ?? match.score?.home);
           const sAway = this.asInt(match.awayScore ?? match.score?.away);
+          const storedStatus = deriveStoredMatchStatus({
+            state: match.state,
+            status: match.status,
+            substate: match.substate,
+            statusBoxContent: match.statusBoxContent,
+            scoreHome: sHome,
+            scoreAway: sAway,
+            score: match.score,
+          });
 
           // Handle postponed matches (ERT = Erteledendi)
           if (match.statusBoxContent === "ERT") {
@@ -733,7 +1059,7 @@ export class DataFetcherTask {
               leagueId: leagueId,
               state: match.state || null,
               substate: match.substate || null,
-              status: match.status || match.state || "NS",
+              status: storedStatus,
               scoreHome: sHome,
               scoreAway: sAway,
               homeTeamId: homeTeamId,
@@ -748,7 +1074,7 @@ export class DataFetcherTask {
               leagueId: leagueId,
               state: match.state || null,
               substate: match.substate || null,
-              status: match.status || match.state || "NS",
+              status: storedStatus,
               mstUtc: BigInt(match.mstUtc || Date.now()),
               scoreHome: sHome,
               scoreAway: sAway,

src/tasks/limit-resetter.task.ts

@@ -141,7 +141,7 @@ export class LimitResetterTask {
   }
 
   /**
-   * Reset subscription status for expired users
+   * Downgrade cancelled subscriptions that have passed their cancel effective date
    */
   @Cron("0 0 * * *", { timeZone: "Europe/Istanbul" })
   async checkSubscriptions() {
@@ -155,21 +155,55 @@ export class LimitResetterTask {
         try {
           const now = new Date();
 
-          const result = await this.prisma.user.updateMany({
+          // Find subscriptions with passed cancel effective date
+          const expiredSubs = await this.prisma.subscription.findMany({
             where: {
-              subscriptionStatus: "active",
+              plan: "cancelled",
-              subscriptionExpiresAt: { lt: now },
+              cancelEffectiveDate: { lt: now },
-            },
-            data: {
-              subscriptionStatus: "expired",
             },
+            select: { id: true, userId: true },
           });
 
-          if (result.count > 0) {
+          for (const sub of expiredSubs) {
-            this.logger.log(`${result.count} subscriptions marked as expired`);
+            // Downgrade to free
+            await this.prisma.user.update({
+              where: { id: sub.userId },
+              data: { subscriptionStatus: "free" },
+            });
+
+            // Sync limits to free tier
+            await this.prisma.usageLimit.upsert({
+              where: { userId: sub.userId },
+              update: { maxAnalyses: 3, maxCoupons: 1 },
+              create: {
+                userId: sub.userId,
+                analysisCount: 0,
+                couponCount: 0,
+                maxAnalyses: 3,
+                maxCoupons: 1,
+                lastResetDate: new Date(),
+              },
+            });
+
+            // Reset subscription to free
+            await this.prisma.subscription.update({
+              where: { id: sub.id },
+              data: {
+                plan: "free",
+                cancelledAt: null,
+                cancelEffectiveDate: null,
+              },
+            });
           }
-        } catch (error: any) {
+
-          this.logger.error(`Subscription check failed: ${error.message}`);
+          if (expiredSubs.length > 0) {
+            this.logger.log(
+              `${expiredSubs.length} cancelled subscriptions downgraded to free`,
+            );
+          }
+        } catch (error: unknown) {
+          const err = error as Error;
+          this.logger.error(`Subscription check failed: ${err.message}`);
         }
       },
       this.logger,