gg3

ai-engine/models/calibration/btts_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.2444,
+  "calibration_error": 0.0,
+  "sample_count": 5000,
+  "last_trained": "2026-05-24T23:10:58.117788",
+  "mean_predicted": 0.4015,
+  "mean_actual": 0.5454
+}

ai-engine/models/calibration/ht_away_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.1852,
+  "calibration_error": 0.0,
+  "sample_count": 4989,
+  "last_trained": "2026-05-24T23:10:58.129841",
+  "mean_predicted": 0.2305,
+  "mean_actual": 0.263
+}

ai-engine/models/calibration/ht_draw_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.2383,
+  "calibration_error": 0.0,
+  "sample_count": 4989,
+  "last_trained": "2026-05-24T23:10:58.142015",
+  "mean_predicted": 0.4549,
+  "mean_actual": 0.4001
+}

ai-engine/models/calibration/ht_home_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.2137,
+  "calibration_error": 0.0,
+  "sample_count": 4989,
+  "last_trained": "2026-05-24T23:10:58.153390",
+  "mean_predicted": 0.3146,
+  "mean_actual": 0.3369
+}

ai-engine/models/calibration/ms_away_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.1956,
+  "calibration_error": 0.0,
+  "sample_count": 5000,
+  "last_trained": "2026-05-24T23:10:58.164629",
+  "mean_predicted": 0.3215,
+  "mean_actual": 0.3196
+}

ai-engine/models/calibration/ms_draw_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.1816,
+  "calibration_error": 0.0,
+  "sample_count": 5000,
+  "last_trained": "2026-05-24T23:10:58.175615",
+  "mean_predicted": 0.2453,
+  "mean_actual": 0.2426
+}

ai-engine/models/calibration/ms_home_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.2241,
+  "calibration_error": 0.0,
+  "sample_count": 5000,
+  "last_trained": "2026-05-24T23:10:58.186473",
+  "mean_predicted": 0.4332,
+  "mean_actual": 0.4378
+}

ai-engine/models/calibration/ou15_metrics.json

@@ -1,8 +1,8 @@
 {
-  "brier_score": 0.196,
+  "brier_score": 0.1708,
   "calibration_error": 0.0,
-  "sample_count": 13,
+  "sample_count": 5000,
-  "last_trained": "2026-05-11T23:38:11.500966",
+  "last_trained": "2026-05-24T23:10:58.196981",
-  "mean_predicted": 0.7239,
+  "mean_predicted": 0.7586,
-  "mean_actual": 0.6154
+  "mean_actual": 0.7732
 }

ai-engine/models/calibration/ou25_metrics.json

@@ -1,8 +1,8 @@
 {
-  "brier_score": 0.2222,
+  "brier_score": 0.2386,
   "calibration_error": 0.0,
-  "sample_count": 18,
+  "sample_count": 5000,
-  "last_trained": "2026-05-11T23:38:11.508353",
+  "last_trained": "2026-05-24T23:10:58.207151",
-  "mean_predicted": 0.6258,
+  "mean_predicted": 0.5013,
-  "mean_actual": 0.5
+  "mean_actual": 0.5538
 }

ai-engine/models/calibration/ou35_metrics.json

@@ -0,0 +1,8 @@
+{
+  "brier_score": 0.215,
+  "calibration_error": 0.0,
+  "sample_count": 5000,
+  "last_trained": "2026-05-24T23:10:58.217663",
+  "mean_predicted": 0.305,
+  "mean_actual": 0.3408
+}

ai-engine/services/betting_brain.py

@@ -312,6 +312,52 @@ class BettingBrain:
         if market in {"HT", "HTFT", "OE"} and score < 86.0:
             vetoes.append("volatile_market_requires_exceptional_evidence")
 
+        # Cross-market reversal risk for MS/DC picks.
+        # If HTFT model says the *opposite* of our MS pick is likely (a
+        # reversal scenario like 1/2 or 2/1), the MS pick is fragile even
+        # when its own calibrated_confidence is high. The Manchester City
+        # 1-0 → 1-2 case is exactly this: MS=1 looked solid but HTFT 1/2
+        # carried real probability mass that the MS scorer ignored.
+        if market == "MS" and pick in ("1", "2"):
+            board = package.get("market_board") or {}
+            htft_payload = board.get("HTFT") if isinstance(board, dict) else None
+            htft_probs = (
+                htft_payload.get("probs", {})
+                if isinstance(htft_payload, dict) else {}
+            )
+            risk_payload = package.get("risk") or {}
+            if not htft_probs and isinstance(risk_payload, dict):
+                htft_probs = risk_payload.get("ht_ft_probs", {}) or {}
+            # Reversal outcomes that would make this MS pick lose despite
+            # the team leading at half-time / trailing at half-time.
+            if pick == "1":
+                # Picked home win. Threats: 1/2 (led, lost) and 1/X (led, drew).
+                reversal_keys = ("1/2", "1/X")
+                drift_keys = ("X/2",)
+            else:
+                # Picked away win. Threats: 2/1, 2/X. Drift: X/1.
+                reversal_keys = ("2/1", "2/X")
+                drift_keys = ("X/1",)
+            reversal_prob = sum(
+                self._safe_float(htft_probs.get(key), 0.0) or 0.0
+                for key in reversal_keys
+            )
+            drift_prob = sum(
+                self._safe_float(htft_probs.get(key), 0.0) or 0.0
+                for key in drift_keys
+            )
+            combined_risk = reversal_prob + 0.5 * drift_prob
+            if combined_risk >= 0.25:
+                score -= 28.0
+                vetoes.append("htft_reversal_risk_high")
+                issues.append(f"htft_reversal_prob={combined_risk:.2f}")
+            elif combined_risk >= 0.15:
+                score -= 14.0
+                issues.append(f"htft_reversal_prob_moderate={combined_risk:.2f}")
+            elif combined_risk >= 0.10:
+                score -= 6.0
+                issues.append(f"htft_reversal_prob_minor={combined_risk:.2f}")
+
         # Sniper override: bypass eligible vetoes when value sniper triggered
         sniper_bypassed: List[str] = []
         if is_value_sniper and vetoes: