feat(ai): expand training to 68K+ matches, add score model, backfill implied odds

- extract_training_data.py: switch from top_leagues.json (23) to qualified_leagues.json (265)
- update_implied_odds.py: new script to backfill implied odds from real market data
- train_score_model.py: rewrite with v25 102-feature set + temporal split
- single_match_orchestrator.py: integrate ML score model with heuristic fallback

ai-engine/scripts/extract_training_data.py

@@ -33,7 +33,7 @@ from features.upset_engine import get_upset_engine
 from features.referee_engine import get_referee_engine
 from features.momentum_engine import get_momentum_engine
 
-TOP_LEAGUES_PATH = os.path.join(AI_ENGINE_DIR, "..", "top_leagues.json")
+TOP_LEAGUES_PATH = os.path.join(AI_ENGINE_DIR, "..", "qualified_leagues.json")
 OUTPUT_CSV = os.path.join(AI_ENGINE_DIR, "data", "training_data.csv")
 
 # Ensure output dir exists

ai-engine/scripts/train_score_model.py

@@ -1,183 +1,271 @@
+"""
+V25-Compatible Score Prediction Model Trainer
+===============================================
+Trains 4 independent XGBoost regression models for:
+  - FT Home Goals
+  - FT Away Goals
+  - HT Home Goals
+  - HT Away Goals
 
+Uses the same 102-feature set as v25_ensemble for full compatibility.
+Temporal train/test split (80/20) to avoid future leakage.
+
+Usage:
+    python3 scripts/train_score_model.py
+"""
+
+import os
+import sys
+import pickle
+import numpy as np
 import pandas as pd
 import xgboost as xgb
-import pickle
+from datetime import datetime
-import os
+from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_absolute_error, r2_score
 
-# Paths
+# Add parent directory to path
-DATA_PATH = os.path.join(os.path.dirname(__file__), "../data/training_data.csv")
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-MODEL_PATH = os.path.join(os.path.dirname(__file__), "../models/xgb_score.pkl")
 
-# Import unified 56-feature array from markets trainer
+# Config
-from train_xgboost_markets import FEATURES
+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")
+MODEL_PATH = os.path.join(AI_ENGINE_DIR, "models", "xgb_score.pkl")
+
+# Import the EXACT same feature set as v25 market models
+from train_v25_clean import FEATURES
 
 TARGETS = ["score_home", "score_away", "ht_score_home", "ht_score_away"]
 
-def train():
+# Model hyperparameters (tuned for goal count regression)
-    print("🚀 Training Score Prediction Model (XGBoost) - Full Time & Half Time")
+XGB_PARAMS = {
-    print("=" * 60)
+    "objective": "reg:squarederror",
+    "n_estimators": 1200,
+    "learning_rate": 0.02,
+    "max_depth": 6,
+    "subsample": 0.8,
+    "colsample_bytree": 0.7,
+    "min_child_weight": 5,
+    "reg_alpha": 0.1,
+    "reg_lambda": 1.0,
+    "n_jobs": -1,
+    "random_state": 42,
+}
 
+
+def load_data() -> pd.DataFrame:
+    """Load and validate training data."""
     if not os.path.exists(DATA_PATH):
         print(f"❌ Data file not found: {DATA_PATH}")
-        return
+        print("   Run extract_training_data.py first")
+        sys.exit(1)
 
     print(f"📦 Loading data from {DATA_PATH}...")
     df = pd.read_csv(DATA_PATH)
-    
+
-    # Preprocessing
+    # Fill feature NaNs with 0 (same as v25 training)
-    # Drop rows where target is missing (should verify)
+    for col in FEATURES:
+        if col in df.columns:
+            df[col] = df[col].fillna(0)
+
+    # Backward-compatible: add odds presence flags if missing
+    odds_base_columns = [
+        "odds_ms_h", "odds_ms_d", "odds_ms_a",
+        "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",
+    ]
+    for base_col in odds_base_columns:
+        pres_col = f"{base_col}_present"
+        if pres_col not in df.columns and base_col in df.columns:
+            df[pres_col] = (df[base_col] > 1.0).astype(int)
+
+    # Drop rows where any target is missing
     df = df.dropna(subset=TARGETS)
-    
+
-    # Fill feature NaNs with median/mean or 0
+    # Filter: at least MS odds must be present
-    print(f"   Original rows: {len(df)}")
-    
-    # Filter valid odds (at least ms_h > 1.0)
     df = df[df["odds_ms_h"] > 1.0].copy()
-    print(f"   Rows with valid odds: {len(df)}")
-    
-    X = df[FEATURES]
-    y_home = df["score_home"]
-    y_away = df["score_away"]
-    y_ht_home = df["ht_score_home"]
-    y_ht_away = df["ht_score_away"]
-    
-    # Train/Test Split
-    X_train, X_test, y_h_train, y_h_test, y_a_train, y_a_test, y_ht_h_train, y_ht_h_test, y_ht_a_train, y_ht_a_test = train_test_split(
-        X, y_home, y_away, y_ht_home, y_ht_away, test_size=0.2, random_state=42
-    )
-    
-    print(f"   Training set: {len(X_train)} matches")
-    print(f"   Test set: {len(X_test)} matches")
 
-    # --- HOME GOALS MODEL ---
+    # Ensure all features exist
-    print("\n🏠 Training Home Goals Model...")
+    missing = [f for f in FEATURES if f not in df.columns]
-    xgb_home = xgb.XGBRegressor(
+    if missing:
-        objective='reg:squarederror',
+        print(f"⚠️  Missing {len(missing)} features, filling with 0: {missing[:5]}...")
-        n_estimators=1000,
+        for f in missing:
-        learning_rate=0.01,
+            df[f] = 0
-        max_depth=5,
-        subsample=0.7,
-        colsample_bytree=0.7,
-        n_jobs=-1,
-        random_state=42,
-        early_stopping_rounds=50 # Configure here for newer XGBoost or remove if not supported in constructor (depends on version)
-    )
-    # Actually, to be safe across versions, let's remove early stopping for now or use validation set properly
-    # Using 'eval_set' without early_stopping_rounds just prints metrics
-    xgb_home = xgb.XGBRegressor(
-        objective='reg:squarederror',
-        n_estimators=1000,
-        learning_rate=0.01,
-        max_depth=5,
-        subsample=0.7,
-        colsample_bytree=0.7,
-        n_jobs=-1,
-        random_state=42
-    )
-    xgb_home.fit(X_train, y_h_train, eval_set=[(X_test, y_h_test)], verbose=False)
-    
-    home_preds = xgb_home.predict(X_test)
-    mae_home = mean_absolute_error(y_h_test, home_preds)
-    r2_home = r2_score(y_h_test, home_preds)
-    print(f"   ✅ FT Home MAE: {mae_home:.4f} goals")
-    print(f"   ✅ FT Home R2: {r2_home:.4f}")
 
-    # --- AWAY GOALS MODEL ---
+    return df
-    print("\n✈️ Training FT Away Goals Model...")
-    xgb_away = xgb.XGBRegressor(
-        objective='reg:squarederror',
-        n_estimators=1000,
-        learning_rate=0.01,
-        max_depth=5,
-        subsample=0.7,
-        colsample_bytree=0.7,
-        n_jobs=-1,
-        random_state=42
-    )
-    xgb_away.fit(X_train, y_a_train, eval_set=[(X_test, y_a_test)], verbose=False)
-    
-    away_preds = xgb_away.predict(X_test)
-    mae_away = mean_absolute_error(y_a_test, away_preds)
-    r2_away = r2_score(y_a_test, away_preds)
-    print(f"   ✅ FT Away MAE: {mae_away:.4f} goals")
-    print(f"   ✅ FT Away R2: {r2_away:.4f}")
-    
-    # --- HT HOME GOALS MODEL ---
-    print("\n🏠 Training HT Home Goals Model...")
-    xgb_ht_home = xgb.XGBRegressor(
-        objective='reg:squarederror',
-        n_estimators=1000,
-        learning_rate=0.01,
-        max_depth=5,
-        subsample=0.7,
-        colsample_bytree=0.7,
-        n_jobs=-1,
-        random_state=42
-    )
-    xgb_ht_home.fit(X_train, y_ht_h_train, eval_set=[(X_test, y_ht_h_test)], verbose=False)
-    
-    ht_home_preds = xgb_ht_home.predict(X_test)
-    mae_ht_home = mean_absolute_error(y_ht_h_test, ht_home_preds)
-    print(f"   ✅ HT Home MAE: {mae_ht_home:.4f} goals")
 
-    # --- HT AWAY GOALS MODEL ---
+
-    print("\n✈️ Training HT Away Goals Model...")
+def temporal_split(df: pd.DataFrame, train_ratio: float = 0.80):
-    xgb_ht_away = xgb.XGBRegressor(
+    """
-        objective='reg:squarederror',
+    Temporal train/test split by match date.
-        n_estimators=1000,
+    Ensures no future information leaks into training.
-        learning_rate=0.01,
+    """
-        max_depth=5,
+    if "match_date" in df.columns:
-        subsample=0.7,
+        df = df.sort_values("match_date").reset_index(drop=True)
-        colsample_bytree=0.7,
+    elif "round" in df.columns:
-        n_jobs=-1,
+        df = df.sort_values("round").reset_index(drop=True)
-        random_state=42
+
+    split_idx = int(len(df) * train_ratio)
+    return df.iloc[:split_idx].copy(), df.iloc[split_idx:].copy()
+
+
+def train_single_model(X_train, y_train, X_test, y_test, name: str):
+    """Train a single XGBoost regression model with early stopping."""
+    print(f"\n🏗️  Training {name} model...")
+
+    model = xgb.XGBRegressor(**XGB_PARAMS)
+    model.fit(
+        X_train, y_train,
+        eval_set=[(X_test, y_test)],
+        verbose=False,
     )
-    xgb_ht_away.fit(X_train, y_ht_a_train, eval_set=[(X_test, y_ht_a_test)], verbose=False)
+
-    
+    preds = model.predict(X_test)
-    ht_away_preds = xgb_ht_away.predict(X_test)
+
-    mae_ht_away = mean_absolute_error(y_ht_a_test, ht_away_preds)
+    mae = mean_absolute_error(y_test, preds)
-    print(f"   ✅ HT Away MAE: {mae_ht_away:.4f} goals")
+    rmse = np.sqrt(mean_squared_error(y_test, preds))
-    
+    r2 = r2_score(y_test, preds)
-    # --- EVALUATE EXACT SCORE ACCURACY (ROUNDED) ---
+
-    print("\n🎯 Exact FT Score Accuracy (Test Set):")
+    print(f"   MAE:  {mae:.4f} goals")
-    correct = 0
+    print(f"   RMSE: {rmse:.4f}")
-    close = 0 # Within 1 goal diff for both
+    print(f"   R²:   {r2:.4f}")
-    
+
-    for h_true, a_true, h_pred, a_pred in zip(y_h_test, y_a_test, home_preds, away_preds):
+    return model, {"mae": mae, "rmse": rmse, "r2": r2}
-        h_p = round(h_pred)
+
-        a_p = round(a_pred)
+
-        if h_p == h_true and a_p == a_true:
+def evaluate_combined(models: dict, X_test, y_test_dict: dict):
-            correct += 1
+    """Evaluate combined score accuracy (FT and HT)."""
-        if abs(h_p - h_true) <= 1 and abs(a_p - a_true) <= 1:
+    print("\n🎯 Combined Score Evaluation (Test Set):")
+
+    # FT Score
+    ft_h_preds = models["ft_home"].predict(X_test)
+    ft_a_preds = models["ft_away"].predict(X_test)
+
+    y_ft_h = y_test_dict["score_home"].values
+    y_ft_a = y_test_dict["score_away"].values
+
+    exact = 0
+    close = 0
+    result_correct = 0
+    total = len(X_test)
+
+    for h_true, a_true, h_pred, a_pred in zip(y_ft_h, y_ft_a, ft_h_preds, ft_a_preds):
+        hp = max(0, round(h_pred))
+        ap = max(0, round(a_pred))
+
+        # Exact score
+        if hp == h_true and ap == a_true:
+            exact += 1
+
+        # Close (±1 each)
+        if abs(hp - h_true) <= 1 and abs(ap - a_true) <= 1:
             close += 1
-            
+
-    acc = correct / len(X_test) * 100
+        # Result direction (1X2)
-    close_acc = close / len(X_test) * 100
+        true_result = 1 if h_true > a_true else (0 if h_true == a_true else -1)
-    print(f"   Exact Match: {acc:.2f}%")
+        pred_result = 1 if hp > ap else (0 if hp == ap else -1)
-    print(f"   Close Match (+/- 1 goal): {close_acc:.2f}%")
+        if true_result == pred_result:
+            result_correct += 1
+
+    print(f"   FT Exact Score:    {exact / total * 100:.2f}% ({exact}/{total})")
+    print(f"   FT Close (±1):    {close / total * 100:.2f}% ({close}/{total})")
+    print(f"   FT Result (1X2):  {result_correct / total * 100:.2f}% ({result_correct}/{total})")
+
+    # HT Score
+    ht_h_preds = models["ht_home"].predict(X_test)
+    ht_a_preds = models["ht_away"].predict(X_test)
+
+    y_ht_h = y_test_dict["ht_score_home"].values
+    y_ht_a = y_test_dict["ht_score_away"].values
+
+    ht_exact = 0
+    ht_total = len(X_test)
+
+    for h_true, a_true, h_pred, a_pred in zip(y_ht_h, y_ht_a, ht_h_preds, ht_a_preds):
+        hp = max(0, round(h_pred))
+        ap = max(0, round(a_pred))
+        if hp == h_true and ap == a_true:
+            ht_exact += 1
+
+    print(f"   HT Exact Score:   {ht_exact / ht_total * 100:.2f}% ({ht_exact}/{ht_total})")
+
+    return {
+        "ft_exact_pct": exact / total * 100,
+        "ft_close_pct": close / total * 100,
+        "ft_result_pct": result_correct / total * 100,
+        "ht_exact_pct": ht_exact / ht_total * 100,
+    }
+
+
+def train():
+    """Main training pipeline."""
+    print("🚀 Score Prediction Model Trainer (V25-Compatible)")
+    print(f"   Feature count: {len(FEATURES)}")
+    print("=" * 60)
+
+    # Load data
+    df = load_data()
+    print(f"   Total valid rows: {len(df)}")
+
+    # Temporal split
+    train_df, test_df = temporal_split(df)
+    print(f"   Training set: {len(train_df)} matches")
+    print(f"   Test set:     {len(test_df)} matches (temporally after training)")
+
+    X_train = train_df[FEATURES]
+    X_test = test_df[FEATURES]
+
+    # Train 4 models
+    models = {}
+    metrics = {}
+
+    for target_name, model_key in [
+        ("score_home", "ft_home"),
+        ("score_away", "ft_away"),
+        ("ht_score_home", "ht_home"),
+        ("ht_score_away", "ht_away"),
+    ]:
+        model, metric = train_single_model(
+            X_train, train_df[target_name],
+            X_test, test_df[target_name],
+            model_key,
+        )
+        models[model_key] = model
+        metrics[model_key] = metric
+
+    # Combined evaluation
+    y_test_dict = {t: test_df[t] for t in TARGETS}
+    combined = evaluate_combined(models, X_test, y_test_dict)
 
     # Save
-    print(f"\n💾 Saving models to {MODEL_PATH}...")
+    print(f"\n💾 Saving to {MODEL_PATH}...")
     model_data = {
-        "home_model": xgb_home,
+        "home_model": models["ft_home"],
-        "away_model": xgb_away,
+        "away_model": models["ft_away"],
-        "ht_home_model": xgb_ht_home,
+        "ht_home_model": models["ht_home"],
-        "ht_away_model": xgb_ht_away,
+        "ht_away_model": models["ht_away"],
         "features": FEATURES,
         "meta": {
-            "mae_home": mae_home,
+            **{f"{k}_{mk}": mv for k, m in metrics.items() for mk, mv in m.items()},
-            "mae_away": mae_away,
+            **combined,
-            "mae_ht_home": mae_ht_home,
+            "trained_at": datetime.now().isoformat(),
-            "mae_ht_away": mae_ht_away,
+            "feature_count": len(FEATURES),
-            "acc": acc
+            "train_size": len(train_df),
-        }
+            "test_size": len(test_df),
+        },
     }
+
     with open(MODEL_PATH, "wb") as f:
         pickle.dump(model_data, f)
-    
+
-    print("✅ Done.")
+    print("\n✅ Score model training complete!")
+    print(f"   Saved: {MODEL_PATH}")
+
 
 if __name__ == "__main__":
     train()

ai-engine/scripts/update_implied_odds.py

@@ -0,0 +1,307 @@
+"""
+Update Implied Odds in football_ai_features
+=============================================
+Populates implied_home, implied_draw, implied_away, implied_over25, implied_btts
+from real odds data in odd_categories + odd_selections tables.
+
+Also backfills form-based features (home_goals_avg_5, away_goals_avg_5, etc.)
+from recent match history.
+
+Usage:
+    python3 scripts/update_implied_odds.py
+"""
+
+import os
+import sys
+import time
+import psycopg2
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def get_conn():
+    db_url = os.getenv("DATABASE_URL", "").split("?schema=")[0]
+    return psycopg2.connect(db_url)
+
+
+def update_implied_odds(conn):
+    """Update implied probabilities from real odds data."""
+    cur = conn.cursor()
+
+    print("📊 Phase 1: Updating implied odds from real market data...")
+    t0 = time.time()
+
+    # Step 1: Build odds lookup from odd_categories + odd_selections
+    print("  Loading odds data...")
+    cur.execute("""
+        SELECT oc.match_id, oc.name AS cat_name, os.name AS sel_name, os.odd_value
+        FROM odd_selections os
+        JOIN odd_categories oc ON os.odd_category_db_id = oc.db_id
+        WHERE os.odd_value IS NOT NULL
+          AND CAST(os.odd_value AS FLOAT) > 1.0
+    """)
+
+    odds_by_match = {}
+    row_count = 0
+    for match_id, cat_name, sel_name, odd_val in cur.fetchall():
+        try:
+            v = float(odd_val)
+            if v <= 1.0:
+                continue
+        except (ValueError, TypeError):
+            continue
+
+        if match_id not in odds_by_match:
+            odds_by_match[match_id] = {}
+
+        cat_lower = (cat_name or "").lower().strip()
+        sel_lower = (sel_name or "").lower().strip()
+
+        # Match Result (1X2)
+        if cat_lower == 'maç sonucu':
+            if sel_name == '1':
+                odds_by_match[match_id]['ms_h'] = v
+            elif sel_name in ('0', 'X'):
+                odds_by_match[match_id]['ms_d'] = v
+            elif sel_name == '2':
+                odds_by_match[match_id]['ms_a'] = v
+
+        # Over/Under 2.5
+        elif cat_lower == '2,5 alt/üst':
+            if 'üst' in sel_lower:
+                odds_by_match[match_id]['ou25_o'] = v
+            elif 'alt' in sel_lower:
+                odds_by_match[match_id]['ou25_u'] = v
+
+        # BTTS
+        elif cat_lower == 'karşılıklı gol':
+            if 'var' in sel_lower:
+                odds_by_match[match_id]['btts_y'] = v
+            elif 'yok' in sel_lower:
+                odds_by_match[match_id]['btts_n'] = v
+
+        row_count += 1
+
+    print(f"  Loaded odds for {len(odds_by_match)} matches ({row_count} selections) in {time.time()-t0:.1f}s")
+
+    # Step 2: Calculate implied probabilities and update
+    print("  Calculating implied probabilities...")
+
+    # Get all match_ids in football_ai_features
+    cur.execute("SELECT match_id FROM football_ai_features")
+    feature_match_ids = {row[0] for row in cur.fetchall()}
+
+    updated = 0
+    batch_size = 500
+    updates = []
+
+    for match_id in feature_match_ids:
+        odds = odds_by_match.get(match_id, {})
+        if not odds:
+            continue
+
+        # Implied MS probabilities (vig-free normalization)
+        ms_h = odds.get('ms_h', 0)
+        ms_d = odds.get('ms_d', 0)
+        ms_a = odds.get('ms_a', 0)
+
+        implied_home = 0.33
+        implied_draw = 0.33
+        implied_away = 0.33
+
+        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)
+            if raw_sum > 0:
+                implied_home = round((1 / ms_h) / raw_sum, 4)
+                implied_draw = round((1 / ms_d) / raw_sum, 4)
+                implied_away = round((1 / ms_a) / raw_sum, 4)
+
+        # Implied OU25
+        ou25_o = odds.get('ou25_o', 0)
+        ou25_u = odds.get('ou25_u', 0)
+        implied_over25 = 0.50
+
+        if ou25_o > 1.0 and ou25_u > 1.0:
+            raw_sum = (1 / ou25_o) + (1 / ou25_u)
+            if raw_sum > 0:
+                implied_over25 = round((1 / ou25_o) / raw_sum, 4)
+
+        # Implied BTTS
+        btts_y = odds.get('btts_y', 0)
+        btts_n = odds.get('btts_n', 0)
+        implied_btts = 0.50
+
+        if btts_y > 1.0 and btts_n > 1.0:
+            raw_sum = (1 / btts_y) + (1 / btts_n)
+            if raw_sum > 0:
+                implied_btts = round((1 / btts_y) / raw_sum, 4)
+
+        # Only update if we have real data (not all defaults)
+        has_real_data = (ms_h > 1.0 or ou25_o > 1.0 or btts_y > 1.0)
+        if not has_real_data:
+            continue
+
+        updates.append((
+            implied_home, implied_draw, implied_away,
+            implied_over25, implied_btts, match_id
+        ))
+
+        if len(updates) >= batch_size:
+            cur.executemany("""
+                UPDATE football_ai_features
+                SET implied_home = %s,
+                    implied_draw = %s,
+                    implied_away = %s,
+                    implied_over25 = %s,
+                    implied_btts = %s
+                WHERE match_id = %s
+            """, updates)
+            updated += len(updates)
+            updates = []
+
+    # Final batch
+    if updates:
+        cur.executemany("""
+            UPDATE football_ai_features
+            SET implied_home = %s,
+                implied_draw = %s,
+                implied_away = %s,
+                implied_over25 = %s,
+                implied_btts = %s
+            WHERE match_id = %s
+        """, updates)
+        updated += len(updates)
+
+    conn.commit()
+    print(f"  ✅ Updated implied odds for {updated} matches in {time.time()-t0:.1f}s")
+    return updated
+
+
+def update_form_features(conn):
+    """Backfill form-based features (goals avg, clean sheet rate) from match history."""
+    cur = conn.cursor()
+
+    print("\n📊 Phase 2: Updating form-based features...")
+    t0 = time.time()
+
+    # Load all finished football matches ordered by time
+    print("  Loading match history...")
+    cur.execute("""
+        SELECT id, home_team_id, away_team_id, score_home, score_away, mst_utc
+        FROM matches
+        WHERE status = 'FT'
+          AND score_home IS NOT NULL
+          AND sport = 'football'
+        ORDER BY mst_utc ASC
+    """)
+
+    matches = cur.fetchall()
+    print(f"  Loaded {len(matches)} finished matches")
+
+    # Build team history incrementally
+    from collections import defaultdict
+    team_history = defaultdict(list)  # team_id -> [(goals_scored, goals_conceded)]
+
+    # Get all feature match IDs
+    cur.execute("SELECT match_id FROM football_ai_features")
+    feature_match_ids = {row[0] for row in cur.fetchall()}
+
+    updated = 0
+    batch_size = 500
+    updates = []
+
+    for match_id, home_id, away_id, score_home, score_away, mst_utc in matches:
+        # Calculate features BEFORE updating history (pre-match features)
+        if match_id in feature_match_ids:
+            h_hist = team_history[home_id][-5:]  # last 5
+            a_hist = team_history[away_id][-5:]
+
+            # Home team form
+            if h_hist:
+                h_goals_avg = sum(g for g, _ in h_hist) / len(h_hist)
+                h_conceded_avg = sum(c for _, c in h_hist) / len(h_hist)
+                h_cs_rate = sum(1 for _, c in h_hist if c == 0) / len(h_hist)
+                h_scoring_rate = sum(1 for g, _ in h_hist if g > 0) / len(h_hist)
+            else:
+                h_goals_avg, h_conceded_avg = 1.3, 1.2
+                h_cs_rate, h_scoring_rate = 0.25, 0.75
+
+            # Away team form
+            if a_hist:
+                a_goals_avg = sum(g for g, _ in a_hist) / len(a_hist)
+                a_conceded_avg = sum(c for _, c in a_hist) / len(a_hist)
+                a_cs_rate = sum(1 for _, c in a_hist if c == 0) / len(a_hist)
+                a_scoring_rate = sum(1 for g, _ in a_hist if g > 0) / len(a_hist)
+            else:
+                a_goals_avg, a_conceded_avg = 1.3, 1.2
+                a_cs_rate, a_scoring_rate = 0.25, 0.75
+
+            updates.append((
+                round(h_goals_avg, 3), round(h_conceded_avg, 3),
+                round(h_cs_rate, 3), round(h_scoring_rate, 3),
+                round(a_goals_avg, 3), round(a_conceded_avg, 3),
+                round(a_cs_rate, 3), round(a_scoring_rate, 3),
+                match_id
+            ))
+
+            if len(updates) >= batch_size:
+                cur.executemany("""
+                    UPDATE football_ai_features
+                    SET home_goals_avg_5 = %s,
+                        home_conceded_avg_5 = %s,
+                        home_clean_sheet_rate = %s,
+                        home_scoring_rate = %s,
+                        away_goals_avg_5 = %s,
+                        away_conceded_avg_5 = %s,
+                        away_clean_sheet_rate = %s,
+                        away_scoring_rate = %s
+                    WHERE match_id = %s
+                """, updates)
+                updated += len(updates)
+                updates = []
+
+        # Update history AFTER feature extraction (maintains pre-match invariant)
+        team_history[home_id].append((score_home, score_away))
+        team_history[away_id].append((score_away, score_home))
+
+    # Final batch
+    if updates:
+        cur.executemany("""
+            UPDATE football_ai_features
+            SET home_goals_avg_5 = %s,
+                home_conceded_avg_5 = %s,
+                home_clean_sheet_rate = %s,
+                home_scoring_rate = %s,
+                away_goals_avg_5 = %s,
+                away_conceded_avg_5 = %s,
+                away_clean_sheet_rate = %s,
+                away_scoring_rate = %s
+            WHERE match_id = %s
+        """, updates)
+        updated += len(updates)
+
+    conn.commit()
+    print(f"  ✅ Updated form features for {updated} matches in {time.time()-t0:.1f}s")
+    return updated
+
+
+def main():
+    print("🚀 Football AI Features — Implied Odds & Form Backfill")
+    print("=" * 60)
+
+    conn = get_conn()
+
+    try:
+        odds_updated = update_implied_odds(conn)
+        form_updated = update_form_features(conn)
+
+        print(f"\n✅ DONE!")
+        print(f"   Implied odds updated: {odds_updated} matches")
+        print(f"   Form features updated: {form_updated} matches")
+    finally:
+        conn.close()
+
+
+if __name__ == "__main__":
+    main()

ai-engine/services/single_match_orchestrator.py

@@ -16,6 +16,7 @@ import re
 import time
 import math
 import os
+import pickle
 import pandas as pd
 import numpy as np
 from collections import defaultdict
@@ -258,6 +259,51 @@ class SingleMatchOrchestrator:
         self._v27 = None
         return None
 
+    def _get_score_model(self) -> Optional[Dict]:
+        """Load XGBoost score prediction model (non-fatal)."""
+        if hasattr(self, "_score_model_cache"):
+            return self._score_model_cache
+        score_model_path = os.path.join(
+            os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+            "models", "xgb_score.pkl",
+        )
+        try:
+            if os.path.exists(score_model_path):
+                with open(score_model_path, "rb") as f:
+                    model_data = pickle.load(f)
+                if all(k in model_data for k in ("home_model", "away_model", "ht_home_model", "ht_away_model", "features")):
+                    self._score_model_cache = model_data
+                    print(f"[SCORE] ✅ Score model loaded ({len(model_data['features'])} features)")
+                    return self._score_model_cache
+        except Exception as e:
+            print(f"[SCORE] ⚠ Load failed (non-fatal, using heuristic): {e}")
+        self._score_model_cache = None
+        return None
+
+    def _predict_score_with_model(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """Predict FT/HT scores using XGBoost score model."""
+        score_model = self._get_score_model()
+        if score_model is None:
+            return None
+        try:
+            import pandas as _pd
+            model_features = score_model["features"]
+            row = {f: float(features.get(f, 0)) for f in model_features}
+            df = _pd.DataFrame([row])
+            ft_home = max(0.0, float(score_model["home_model"].predict(df)[0]))
+            ft_away = max(0.0, float(score_model["away_model"].predict(df)[0]))
+            ht_home = max(0.0, float(score_model["ht_home_model"].predict(df)[0]))
+            ht_away = max(0.0, float(score_model["ht_away_model"].predict(df)[0]))
+            return {
+                "ft_home": round(ft_home, 2),
+                "ft_away": round(ft_away, 2),
+                "ht_home": round(ht_home, 2),
+                "ht_away": round(ht_away, 2),
+            }
+        except Exception as e:
+            print(f"[SCORE] ⚠ Prediction error (fallback to heuristic): {e}")
+            return None
+
     def _build_v25_features(self, data: MatchData) -> Dict[str, float]:
         """
         Build the single authoritative V25 pre-match feature vector.
@@ -869,27 +915,39 @@ class SingleMatchOrchestrator:
         prediction.handicap_pick, hcap_top = self._best_prob_pick(hcap_probs)
         prediction.handicap_confidence = hcap_top * 100.0
 
-        base_home_xg = max(0.25, (float(data.home_goals_avg) + float(features.get("away_xga", data.away_conceded_avg))) / 2.0)
+        # ── Score Prediction: Model-first, heuristic fallback ──────────
-        base_away_xg = max(0.25, (float(data.away_goals_avg) + float(features.get("home_xga", data.home_conceded_avg))) / 2.0)
+        score_result = self._predict_score_with_model(features)
-        ms_edge = prediction.ms_home_prob - prediction.ms_away_prob
+        if score_result is not None:
-        total_target = max(
+            # ML model predicted scores
-            1.4,
+            prediction.home_xg = score_result["ft_home"]
-            min(
+            prediction.away_xg = score_result["ft_away"]
-                4.8,
+            prediction.total_xg = round(prediction.home_xg + prediction.away_xg, 2)
-                (float(features.get("league_avg_goals", 2.7)) * 0.55)
+            ht_home_xg = score_result["ht_home"]
-                + ((float(data.home_goals_avg) + float(data.away_goals_avg)) * 0.45)
+            ht_away_xg = score_result["ht_away"]
-                + ((prediction.over_25_prob - prediction.under_25_prob) * 1.15),
+            prediction.predicted_ft_score = f"{int(round(prediction.home_xg))}-{int(round(prediction.away_xg))}"
-            ),
+            prediction.predicted_ht_score = f"{int(round(ht_home_xg))}-{int(round(ht_away_xg))}"
-        )
+        else:
-        home_xg = max(0.2, base_home_xg + (ms_edge * 0.55) + ((prediction.btts_yes_prob - 0.5) * 0.18))
+            # Heuristic fallback (original formula)
-        away_xg = max(0.2, base_away_xg - (ms_edge * 0.55) + ((prediction.btts_yes_prob - 0.5) * 0.18))
+            base_home_xg = max(0.25, (float(data.home_goals_avg) + float(features.get("away_xga", data.away_conceded_avg))) / 2.0)
-        scale = total_target / max(home_xg + away_xg, 0.1)
+            base_away_xg = max(0.25, (float(data.away_goals_avg) + float(features.get("home_xga", data.home_conceded_avg))) / 2.0)
-        prediction.home_xg = round(home_xg * scale, 2)
+            ms_edge = prediction.ms_home_prob - prediction.ms_away_prob
-        prediction.away_xg = round(away_xg * scale, 2)
+            total_target = max(
-        prediction.total_xg = round(prediction.home_xg + prediction.away_xg, 2)
+                1.4,
-
+                min(
-        prediction.predicted_ft_score = f"{int(round(prediction.home_xg))}-{int(round(prediction.away_xg))}"
+                    4.8,
-        prediction.predicted_ht_score = f"{int(round(prediction.home_xg * 0.45))}-{int(round(prediction.away_xg * 0.45))}"
+                    (float(features.get("league_avg_goals", 2.7)) * 0.55)
+                    + ((float(data.home_goals_avg) + float(data.away_goals_avg)) * 0.45)
+                    + ((prediction.over_25_prob - prediction.under_25_prob) * 1.15),
+                ),
+            )
+            home_xg = max(0.2, base_home_xg + (ms_edge * 0.55) + ((prediction.btts_yes_prob - 0.5) * 0.18))
+            away_xg = max(0.2, base_away_xg - (ms_edge * 0.55) + ((prediction.btts_yes_prob - 0.5) * 0.18))
+            scale = total_target / max(home_xg + away_xg, 0.1)
+            prediction.home_xg = round(home_xg * scale, 2)
+            prediction.away_xg = round(away_xg * scale, 2)
+            prediction.total_xg = round(prediction.home_xg + prediction.away_xg, 2)
+            prediction.predicted_ft_score = f"{int(round(prediction.home_xg))}-{int(round(prediction.away_xg))}"
+            prediction.predicted_ht_score = f"{int(round(prediction.home_xg * 0.45))}-{int(round(prediction.away_xg * 0.45))}"
         prediction.ft_scores_top5 = self._poisson_score_top5(prediction.home_xg, prediction.away_xg)
 
         max_market_conf = max(