gg

.gitignore

@@ -48,6 +48,9 @@ public/uploads/
 ai-engine/models/*
 !ai-engine/models/*.py
 !ai-engine/models/v25/
+!ai-engine/models/v27/
+!ai-engine/models/basketball_v25/
+!ai-engine/models/calibration/
 models/*
 !models/*.py
 colab_export/

ai-engine/models/basketball_v25/__init__.py

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

ai-engine/models/basketball_v25/feature_cols.json

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

ai-engine/models/basketball_v25/lgb_basketball_v25_ml.txt

@@ -0,0 +1,655 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=98
+objective=binary sigmoid:1
+feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98
+feature_infos=none none none none none none none [0:100] [0:100] [-100:100] [54:128.875] [61:138] [-41.333333333333329:24] [61:130.125] [59:133] [-50:34.916666666666671] [-28.666666666666657:32.5] [-24.5:27] [-35.5:31.833333333333329] [0:1] [0:1] [-1:1] [0:12] [0:12] [-12:10] [0.91666666666666663:186.1875] [0.85416666666666663:185.08333333333337] [-179.02083333333334:184.16666666666663] [20:54] [22:56] [-21:15.833333333333332] [11:34.5] [9:36] [-15:13.333333333333332] [2:13] [3:13] [-8:5.5] [0.875:9] [0:12] [-9:4.875] [7:22] [8:23] [-10:10] [0.41025641025641019:0.67647058823529416] [0.39730639730639727:0.76744186046511631] [-0.3174418604651163:0.22647058823529409] [0.20419254658385089:0.4825000000000001] [0.1875:0.54166666666666663] [-0.22171945701357459:0.17923280423280419] [0.47826086956521741:0.87898457080200498] [0.42857142857142849:0.88636363636363635] [-0.29647435897435898:0.38962801311591622] [11:39.666666666666664] [15.5:40] [10:31.875] [11.5:34] [15:56] [17:57] [12:32.125] [10:38] [8:20] [7:22] [0.40000000000000002:0.69047619047619047] [0.3902439024390244:0.71739130434782605] [0.23753976670201479:0.51851851851851849] [0.1707317073170731:0.54545454545454541] [0:8] [0:1] [120:295] [-38:36] [0:1] [0:1] [140.39130434782609:233.09999999999999] [0:1] [0:1] [0:1] [1.05:4.1100000000000003] [1.05:4.1299999999999999] [0.2034883720930232:0.79729729729729726] [0.20270270270270269:0.79651162790697683] [145.5:257.5] [1.3:7.7599999999999998] [1.4099999999999999:7.5599999999999996] [0.1789976133651551:0.75977653631284914] [0.24022346368715081:0.82100238663484482] [-15.5:12.5] [1.3:2.7200000000000002] [1.1799999999999999:2.7799999999999998] [0.36298076923076922:0.64953271028037374] [0.35046728971962621:0.63701923076923073] [0.1918833727344364:0.21555133935749429] none none none none [132:250.6875] [126.25:256.625] [-63.5:88.6875] [-35.5:27.5]
+tree_sizes=2312 2651 2758 2331 2348 2534 2554 2662 2359 2770 1932 1926 2552 2145 2460 2555 1931 2453 1831 2020 2352 2348 2035
+
+Tree=0
+num_leaves=21
+num_cat=0
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=12 2 -2 4 6 -6 -4 8 -3 -9 11 -11 13 -1 -14 -15 -17
+right_child=1 7 3 -5 5 -7 -8 9 -10 10 -12 -13 14 15 -16 16 -18
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+internal_count=1039 874 645 606 567 111 456 229 74 155 116 85 165 107 58 76 51
+is_linear=0
+shrinkage=0.04
+
+
+end of trees
+
+feature_importances:
+Column_77=25
+Column_46=15
+Column_76=15
+Column_97=14
+Column_43=13
+Column_50=13
+Column_49=12
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+Column_34=1
+Column_39=1
+Column_53=1
+Column_69=1
+Column_79=1
+Column_89=1
+Column_96=1
+
+parameters:
+[boosting: gbdt]
+[objective: binary]
+[metric: binary_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1200]
+[learning_rate: 0.04]
+[num_leaves: 31]
+[num_threads: 4]
+[seed: 42]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 6]
+[min_data_in_leaf: 24]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.84]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 5]
+[bagging_seed: 400]
+[bagging_by_query: 0]
+[feature_fraction: 0.82]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 30056]
+[extra_trees: 0]
+[extra_seed: 12879]
+[early_stopping_round: 0]
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+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 17869]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: -1]
+[saved_feature_importance_type: 0]
+[use_quantized_grad: 0]
+[num_grad_quant_bins: 4]
+[quant_train_renew_leaf: 0]
+[stochastic_rounding: 1]
+[linear_tree: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 175]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[parser_config_file: ]
+[objective_seed: 16083]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[lambdarank_position_bias_regularization: 0]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:null

ai-engine/models/basketball_v25/lgb_basketball_v25_spread.txt

@@ -0,0 +1,790 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=98
+objective=binary sigmoid:1
+feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98
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+
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+
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+
+Tree=3
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+
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+[label_gain: ]
+[lambdarank_position_bias_regularization: 0]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:null

ai-engine/models/basketball_v25/lgb_basketball_v25_total.txt

@@ -0,0 +1,202 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=98
+objective=binary sigmoid:1
+feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98
+feature_infos=none none none none none none none [0:100] [0:100] [-100:100] [54:128.875] [61:138] [-41.333333333333329:24] [61:130.125] [59:133] [-50:34.916666666666671] [-28.666666666666657:32.5] [-24.5:27] [-35.5:31.833333333333329] [0:1] [0:1] [-1:1] [0:12] [0:12] [-12:10] [0.91666666666666663:186.1875] [0.85416666666666663:185.08333333333337] [-179.02083333333334:184.16666666666663] [20:54] [22:56] [-21:15.833333333333332] [11:34.5] [9:36] [-15:13.333333333333332] [2:13] [3:13] [-8:5.5] [0.875:9] [0:12] [-9:4.875] [7:22] [8:23] [-10:10] [0.41025641025641019:0.67647058823529416] [0.39730639730639727:0.76744186046511631] [-0.3174418604651163:0.22647058823529409] [0.20419254658385089:0.4825000000000001] [0.1875:0.54166666666666663] [-0.22171945701357459:0.17923280423280419] [0.47826086956521741:0.87898457080200498] [0.42857142857142849:0.88636363636363635] [-0.29647435897435898:0.38962801311591622] [11:39.666666666666664] [15.5:40] [10:31.875] [11.5:34] [15:56] [17:57] [12:32.125] [10:38] [8:20] [7:22] [0.40000000000000002:0.69047619047619047] [0.3902439024390244:0.71739130434782605] [0.23753976670201479:0.51851851851851849] [0.1707317073170731:0.54545454545454541] [0:8] [0:1] [120:295] [-38:36] [0:1] [0:1] [140.39130434782609:233.09999999999999] [0:1] [0:1] [0:1] [1.05:4.1100000000000003] [1.05:4.1299999999999999] [0.2034883720930232:0.79729729729729726] [0.20270270270270269:0.79651162790697683] [145.5:257.5] [1.3:7.7599999999999998] [1.4099999999999999:7.5599999999999996] [0.1789976133651551:0.75977653631284914] [0.24022346368715081:0.82100238663484482] [-15.5:12.5] [1.3:2.7200000000000002] [1.1799999999999999:2.7799999999999998] [0.36298076923076922:0.64953271028037374] [0.35046728971962621:0.63701923076923073] [0.1918833727344364:0.21555133935749429] none none none none [132:250.6875] [126.25:256.625] [-63.5:88.6875] [-35.5:27.5]
+tree_sizes=1714 1917
+
+Tree=0
+num_leaves=15
+num_cat=0
+split_feature=72 82 49 25 36 25 17 77 34 69 25 40 80 40
+split_gain=11.2305 10.9895 14.5557 11.7291 11.5678 6.991 6.42947 6.52716 10.0318 9.34909 5.86638 5.89396 5.34606 8.20903
+threshold=232.51500000000001 1.8250000000000004 0.74446554478882077 2.8645833333333335 -0.46874999999999994 1.0104166666666667 12.562500000000002 2.6750000000000003 5.6339285714285721 -5.4166666666666652 3.1145833333333335 12.803571428571431 162.00000000000003 13.937500000000002
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 5 10 4 -4 -1 7 8 -7 -9 -3 -12 -5 -14
+right_child=-2 2 3 12 -6 6 -8 9 -10 -11 11 -13 13 -15
+leaf_value=-0.014742735215146609 -0.095823069621212592 -0.030752227995962191 -0.086785452728816673 0.017792685597478597 -0.021483574280753166 -0.0010203128315903962 -0.085759203191584921 -0.020079232808751797 -0.05043362279072628 -0.074223259488395713 -0.046761720776777753 -0.098557138597063443 -0.031433483007911781 0.014451045738105336
+leaf_weight=7.4955528974533072 7.7454046607017508 6.2462940812110928 7.995256423950198 7.7454046607017508 9.4943670034408552 6.99584937095642 9.7442187666892988 5.9964423179626456 108.93536877632141 34.229691565036774 5.9964423179626456 8.4949599504470807 23.486065745353702 8.4949599504470807
+leaf_count=30 31 25 32 31 38 28 39 24 436 137 24 34 94 34
+internal_value=-0.0483827 -0.0469209 -0.0344467 -0.0240408 -0.0513359 -0.0525288 -0.054236 -0.052269 -0.0474518 -0.0661521 -0.063157 -0.0771246 -0.0120241 -0.0192454
+internal_weight=259.096 251.351 77.9538 57.2161 17.4896 173.397 165.902 156.157 115.931 40.2261 20.7377 14.4914 39.7264 31.981
+internal_count=1037 1006 312 229 70 694 664 625 464 161 83 58 159 128
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=17
+num_cat=0
+split_feature=52 63 82 49 63 11 9 22 47 27 26 50 60 68 37 64
+split_gain=9.46149 12.0286 10.1725 11.5657 11.422 11.4943 11.2254 9.35335 10.3758 7.46163 7.15253 10.8161 5.87382 5.6308 5.10807 3.94362
+threshold=18.267857142857146 0.57978414386394084 1.7650000000000003 0.74446554478882077 0.52121781606433182 81.062500000000014 8.7121212121212164 1.5000000000000002 0.36820747200853426 0.06249999999999991 7.0312500000000009 0.79401716042796011 12.35416666666667 161.75000000000003 4.5500000000000007 0.34986080718052731
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=15 2 4 9 5 -2 -7 8 -6 13 11 -5 -12 -4 -9 -1
+right_child=1 -3 3 10 7 6 -8 14 -10 -11 12 -13 -14 -15 -16 -17
+leaf_value=-0.057339030773059012 0.028542958096860364 -0.044782355190073488 0.032618410031766333 0.0057409124284739923 0.013389736272609485 -0.026136292703093016 0.010233031617588078 0.056167817502913067 -0.029699787865729181 -0.038327130963158276 0.074132616447759023 0.052492105838845886 0.026735863001827433 -0.01725653802460729 0.0087599672849197358 -0.011453089463718463
+leaf_weight=5.9920798838138563 9.7432544529438001 8.4919032305479032 7.2431264519691485 42.224371567368507 31.477784723043442 57.952298685908318 17.733558729290962 9.2429284304380399 12.488689020276068 9.2367470711469633 6.4985252916812923 9.7452246546745283 11.743822261691092 7.2440863251686096 5.9950847774744025 5.9950532913208008
+leaf_count=24 39 34 29 169 126 232 71 37 50 37 26 39 47 29 24 24
+internal_value=0.000314777 0.00199863 0.0036638 0.0139129 -0.00299273 -0.0123505 -0.0176148 0.01051 0.00115015 -0.010233 0.0220716 0.0145076 0.0436201 0.00767928 0.0375162 -0.0343904
+internal_weight=259.049 247.061 238.57 93.9359 144.634 85.4291 75.6859 59.2045 43.9665 23.724 70.2119 51.9696 18.2423 14.4872 15.238 11.9871
+internal_count=1037 989 955 376 579 342 303 237 176 95 281 208 73 58 61 48
+is_linear=0
+shrinkage=0.04
+
+
+end of trees
+
+feature_importances:
+Column_25=3
+Column_40=2
+Column_49=2
+Column_63=2
+Column_82=2
+Column_9=1
+Column_11=1
+Column_17=1
+Column_22=1
+Column_26=1
+Column_27=1
+Column_34=1
+Column_36=1
+Column_37=1
+Column_47=1
+Column_50=1
+Column_52=1
+Column_60=1
+Column_64=1
+Column_68=1
+Column_69=1
+Column_72=1
+Column_77=1
+Column_80=1
+
+parameters:
+[boosting: gbdt]
+[objective: binary]
+[metric: binary_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1200]
+[learning_rate: 0.04]
+[num_leaves: 31]
+[num_threads: 4]
+[seed: 42]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 6]
+[min_data_in_leaf: 24]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.84]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 5]
+[bagging_seed: 400]
+[bagging_by_query: 0]
+[feature_fraction: 0.82]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 30056]
+[extra_trees: 0]
+[extra_seed: 12879]
+[early_stopping_round: 0]
+[early_stopping_min_delta: 0]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 17869]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: -1]
+[saved_feature_importance_type: 0]
+[use_quantized_grad: 0]
+[num_grad_quant_bins: 4]
+[quant_train_renew_leaf: 0]
+[stochastic_rounding: 1]
+[linear_tree: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 175]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[parser_config_file: ]
+[objective_seed: 16083]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[lambdarank_position_bias_regularization: 0]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:null

ai-engine/models/calibration/dc_metrics.json

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

ai-engine/models/calibration/ht_ft_metrics.json

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

ai-engine/models/calibration/ou15_metrics.json

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

ai-engine/models/calibration/ou25_metrics.json

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

ai-engine/models/v27/v27_feature_cols.json

@@ -0,0 +1,46 @@
+[
+  "mst_utc",
+  "home_overall_elo",
+  "away_overall_elo",
+  "elo_diff",
+  "home_home_elo",
+  "away_away_elo",
+  "home_form_elo",
+  "away_form_elo",
+  "form_elo_diff",
+  "home_goals_avg",
+  "home_conceded_avg",
+  "away_goals_avg",
+  "away_conceded_avg",
+  "home_clean_sheet_rate",
+  "away_clean_sheet_rate",
+  "home_scoring_rate",
+  "away_scoring_rate",
+  "home_winning_streak",
+  "away_winning_streak",
+  "home_unbeaten_streak",
+  "away_unbeaten_streak",
+  "h2h_total_matches",
+  "h2h_home_win_rate",
+  "h2h_draw_rate",
+  "h2h_avg_goals",
+  "h2h_btts_rate",
+  "h2h_over25_rate",
+  "home_avg_possession",
+  "away_avg_possession",
+  "home_avg_shots_on_target",
+  "away_avg_shots_on_target",
+  "home_shot_conversion",
+  "away_shot_conversion",
+  "home_avg_corners",
+  "away_avg_corners",
+  "home_xga",
+  "away_xga",
+  "league_avg_goals",
+  "league_zero_goal_rate",
+  "upset_atmosphere",
+  "upset_potential",
+  "home_momentum_score",
+  "away_momentum_score",
+  "momentum_diff"
+]

ai-engine/models/v27/v27_feature_importance.csv

@@ -0,0 +1,45 @@
+feature,importance
+elo_diff,53432.7142560482
+form_elo_diff,18292.591957569122
+league_zero_goal_rate,12051.094537258148
+home_avg_possession,10697.8805205822
+league_avg_goals,9966.535993814468
+home_home_elo,9413.749160051346
+home_avg_shots_on_target,9224.094612121582
+away_avg_possession,9000.678180932999
+away_overall_elo,8931.0359852314
+away_away_elo,8706.092560768127
+away_avg_shots_on_target,8604.977019071579
+home_shot_conversion,8132.601410150528
+mst_utc,7753.055005311966
+home_form_elo,7741.581079244614
+home_overall_elo,7505.358977794647
+away_shot_conversion,7395.852544546127
+home_momentum_score,6919.510271787643
+away_momentum_score,6735.586140155792
+away_form_elo,6666.001588344574
+momentum_diff,6153.129729270935
+home_conceded_avg,6057.355793476105
+away_goals_avg,5786.6727023124695
+home_goals_avg,5578.407870054245
+away_conceded_avg,5390.642956972122
+h2h_avg_goals,2773.622985124588
+upset_potential,2514.9942483901978
+h2h_home_win_rate,2294.2361340522766
+h2h_draw_rate,2289.916918992996
+away_xga,1799.381638288498
+home_xga,1677.825749874115
+h2h_total_matches,1582.4059388637543
+away_unbeaten_streak,1362.1424307823181
+home_scoring_rate,1311.8162519931793
+h2h_btts_rate,1311.6885483264923
+away_scoring_rate,1230.6326246261597
+home_clean_sheet_rate,1225.2984809875488
+away_clean_sheet_rate,1224.717898607254
+home_unbeaten_streak,1203.53671169281
+h2h_over25_rate,1174.4284386634827
+away_winning_streak,1110.759458065033
+home_winning_streak,935.4682683944702
+upset_atmosphere,221.20963144302368
+home_avg_corners,23.363860368728638
+away_avg_corners,0.0

ai-engine/models/v27/v27_metadata.json

@@ -0,0 +1,18 @@
+{
+  "version": "v27-pro",
+  "trained_at": "2026-05-06 15:51:05",
+  "approach": "odds-free fundamentals + value edge detection",
+  "feature_count": 44,
+  "total_samples": 90432,
+  "val_acc": 0.489,
+  "val_ll": 1.0252,
+  "best_config": {
+    "edge": 0.12,
+    "min_odds": 1.7
+  },
+  "markets": [
+    "ms",
+    "ou25",
+    "btts"
+  ]
+}