main

ai-engine/services/betting_brain.py

@@ -19,11 +19,26 @@ class BettingBrain:
     SOFT_DIVERGENCE = 0.14
     EXTREME_MODEL_PROB = 0.85
     EXTREME_GAP = 0.30
-    # Vetoes that is_value_sniper bypasses (does NOT bypass odds_below_minimum)
-    SNIPER_BYPASSABLE_VETOES = {"calibrated_confidence_too_low", "play_score_too_low"}
-    # Trap market: market implied probability massively exceeds historical band hit rate
+    SNIPER_BYPASSABLE_VETOES = {"play_score_too_low"}
     TRAP_MARKET_GAP = 0.10
 
+    MARKET_MIN_CONFIDENCE = {
+        "MS": 45.0,
+        "DC": 55.0,
+        "OU25": 48.0,
+        "OU15": 55.0,
+        "OU35": 42.0,
+        "BTTS": 48.0,
+        "HT": 55.0,
+        "HTFT": 65.0,
+        "OE": 55.0,
+        "CARDS": 50.0,
+        "HT_OU05": 55.0,
+        "HT_OU15": 50.0,
+    }
+
+    SNIPER_BLOCKED_MARKETS = {"HT", "HTFT", "OE", "CARDS", "HT_OU05", "HT_OU15"}
+
     MARKET_PRIORS = {
         "DC": 4.0,
         "OU15": 3.0,
@@ -31,10 +46,10 @@ class BettingBrain:
         "BTTS": 0.0,
         "MS": -2.0,
         "OU35": -2.0,
-        "HT": -6.0,
-        "HTFT": -12.0,
-        "CARDS": -5.0,
-        "OE": -8.0,
+        "HT": -10.0,
+        "HTFT": -18.0,
+        "CARDS": -8.0,
+        "OE": -12.0,
     }
 
     def judge(self, package: Dict[str, Any]) -> Dict[str, Any]:
@@ -182,8 +197,10 @@ class BettingBrain:
             issues.append("base_model_not_playable")
 
         is_value_sniper = bool(row.get("is_value_sniper"))
+        if market in self.SNIPER_BLOCKED_MARKETS:
+            is_value_sniper = False
         if is_value_sniper:
-            score += 35.0
+            score += 20.0
             positives.append("value_sniper_override")
 
         score += max(0.0, min(20.0, calibrated_conf * 0.22))
@@ -197,9 +214,31 @@ class BettingBrain:
         risk = str((package.get("risk") or {}).get("level") or "MEDIUM").upper()
         score += {"LOW": 5.0, "MEDIUM": 0.0, "HIGH": -12.0, "EXTREME": -22.0}.get(risk, -4.0)
 
+        # League reliability penalty: weak leagues produce unreliable raw probabilities.
+        # odds_reliability is pre-computed per-league from historical Brier score analysis.
+        odds_rel = self._safe_float(row.get("odds_reliability"), 0.35) or 0.35
+        if odds_rel < 0.30:
+            score -= 22.0
+            issues.append("very_low_reliability_league")
+            if market in {"MS", "DC", "OU25", "BTTS"} and not is_value_sniper:
+                vetoes.append("low_reliability_league_hard_block")
+        elif odds_rel < 0.45:
+            score -= 12.0
+            issues.append("low_reliability_league")
+        elif odds_rel < 0.55:
+            score -= 5.0
+
+        # Inferred features penalty: when ELO/form/H2H come from live enrichment
+        # (not pre-computed table), statistical quality is unknown — penalise hard.
+        dq_flags = list(data_quality.get("flags") or [])
+        if "ai_features_inferred_from_history" in dq_flags:
+            score -= 18.0
+            issues.append("inferred_statistical_features")
+
         if odds < self.MIN_ODDS:
             vetoes.append("odds_below_minimum")
-        if calibrated_conf < 38.0 and not is_value_sniper:
+        min_conf = self.MARKET_MIN_CONFIDENCE.get(market, 45.0)
+        if calibrated_conf < min_conf:
             vetoes.append("calibrated_confidence_too_low")
         if play_score < 50.0 and not is_value_sniper:
             vetoes.append("play_score_too_low")
@@ -270,7 +309,7 @@ class BettingBrain:
             score -= 24.0
             vetoes.append("extreme_probability_without_evidence")
 
-        if market in {"HT", "HTFT", "OE"} and score < 86.0 and not is_value_sniper:
+        if market in {"HT", "HTFT", "OE"} and score < 86.0:
             vetoes.append("volatile_market_requires_exceptional_evidence")
 
         # Sniper override: bypass eligible vetoes when value sniper triggered