""" Deterministic betting judge for prediction packages. The model layer estimates event probabilities. BettingBrain decides whether those probabilities are trustworthy enough to risk money. """ from __future__ import annotations from typing import Any, Dict, List, Optional, Tuple class BettingBrain: MIN_ODDS = 1.30 MIN_BET_SCORE = 72.0 MIN_WATCH_SCORE = 62.0 MIN_BAND_SAMPLE = 8 HARD_DIVERGENCE = 0.22 SOFT_DIVERGENCE = 0.14 EXTREME_MODEL_PROB = 0.85 EXTREME_GAP = 0.30 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, "OU25": 2.0, "BTTS": 0.0, "MS": -2.0, "OU35": -2.0, "HT": -10.0, "HTFT": -18.0, "CARDS": -8.0, "OE": -12.0, } def judge(self, package: Dict[str, Any]) -> Dict[str, Any]: v27_engine = package.get("v27_engine") if not isinstance(v27_engine, dict): return package guarded = dict(package) rows = self._collect_rows(guarded) if not rows: return guarded judged_rows: Dict[str, Dict[str, Any]] = {} decisions: List[Dict[str, Any]] = [] for row in rows: key = self._row_key(row) judged = self._judge_row(dict(row), guarded) judged_rows[key] = judged decisions.append(judged["betting_brain"]) approved = [ row for row in judged_rows.values() if row.get("betting_brain", {}).get("action") == "BET" ] watchlist = [ row for row in judged_rows.values() if row.get("betting_brain", {}).get("action") == "WATCH" ] no_value = [ row for row in judged_rows.values() if row.get("betting_brain", {}).get("action") == "WATCH_NO_VALUE" ] approved.sort(key=self._candidate_sort_key, reverse=True) watchlist.sort(key=self._candidate_sort_key, reverse=True) no_value.sort(key=self._candidate_sort_key, reverse=True) original_main = guarded.get("main_pick") or {} main_pick = None decision = "NO_BET" decision_reason = "No candidate passed the betting brain evidence gates." if approved: main_pick = dict(approved[0]) main_pick["is_guaranteed"] = bool(main_pick.get("betting_brain", {}).get("score", 0.0) >= 82.0) main_pick["pick_reason"] = "betting_brain_approved" decision = "BET" decision_reason = main_pick.get("betting_brain", {}).get("summary", "Evidence is aligned.") elif watchlist: main_pick = dict(watchlist[0]) self._force_no_bet(main_pick, "betting_brain_watchlist") decision = "WATCHLIST" decision_reason = main_pick.get("betting_brain", {}).get("summary", "Interesting but not clean enough.") elif no_value: # B-1: model agrees with a low-odds market — surface it so the user # sees the read, but explicitly mark as not-playable. main_pick = dict(no_value[0]) self._force_no_bet(main_pick, "betting_brain_no_value_odds_below_minimum") decision = "WATCH_NO_VALUE" decision_reason = "Model favoriyle hemfikir ama oran bahis için çok düşük — bilgi amaçlı gösteriliyor." elif original_main: main_pick = dict(judged_rows.get(self._row_key(original_main), original_main)) self._force_no_bet(main_pick, "betting_brain_no_safe_pick") main_key = self._row_key(main_pick) if main_pick else "" supporting = [ dict(row) for row in judged_rows.values() if self._row_key(row) != main_key ] supporting.sort(key=self._candidate_sort_key, reverse=True) bet_summary = [ self._summary_item(row) for row in sorted(judged_rows.values(), key=self._candidate_sort_key, reverse=True) ] guarded["main_pick"] = main_pick guarded["value_pick"] = self._pick_value_candidate(judged_rows, main_key) guarded["supporting_picks"] = supporting[:6] guarded["bet_summary"] = bet_summary playable = decision == "BET" and bool(main_pick and main_pick.get("playable")) advice = dict(guarded.get("bet_advice") or {}) advice["playable"] = playable advice["suggested_stake_units"] = float(main_pick.get("stake_units", 0.0)) if playable and main_pick else 0.0 advice["reason"] = "betting_brain_approved" if playable else "betting_brain_no_bet" advice["decision"] = decision advice["confidence_band"] = self._decision_band(main_pick) guarded["bet_advice"] = advice rejected = [d for d in decisions if d.get("action") == "REJECT"] guarded["betting_brain"] = { "version": "judge-v1", "decision": decision, "reason": decision_reason, "main_pick_key": main_key or None, "approved_count": len(approved), "watchlist_count": len(watchlist), "rejected_count": len(rejected), "top_candidates": self._top_decisions(decisions), "rules": { "min_bet_score": self.MIN_BET_SCORE, "min_watch_score": self.MIN_WATCH_SCORE, "min_band_sample": self.MIN_BAND_SAMPLE, "hard_divergence": self.HARD_DIVERGENCE, "soft_divergence": self.SOFT_DIVERGENCE, "extreme_model_probability": self.EXTREME_MODEL_PROB, "extreme_model_market_gap": self.EXTREME_GAP, }, } guarded["upper_brain"] = guarded["betting_brain"] guarded.setdefault("analysis_details", {}) guarded["analysis_details"]["betting_brain_applied"] = True guarded["analysis_details"]["betting_brain_decision"] = decision return guarded def _judge_row(self, row: Dict[str, Any], package: Dict[str, Any]) -> Dict[str, Any]: market = str(row.get("market") or "") pick = str(row.get("pick") or "") model_prob = self._market_probability(row, package) odds = self._safe_float(row.get("odds"), 0.0) or 0.0 implied = (1.0 / odds) if odds > 1.0 else 0.0 model_gap = (model_prob - implied) if model_prob is not None and implied > 0 else None calibrated_conf = self._safe_float(row.get("calibrated_confidence", row.get("confidence")), 0.0) or 0.0 play_score = self._safe_float(row.get("play_score"), 0.0) or 0.0 ev_edge = self._safe_float(row.get("ev_edge", row.get("edge")), 0.0) or 0.0 v27_prob = self._v27_probability(market, pick, package.get("v27_engine") or {}) divergence = abs(model_prob - v27_prob) if model_prob is not None and v27_prob is not None else None triple_key = self._triple_key(market, pick) triple = self._triple_value(package, triple_key) band_sample = int(self._safe_float((triple or {}).get("band_sample"), 0.0) or 0.0) triple_is_value = bool((triple or {}).get("is_value")) consensus = str((package.get("v27_engine") or {}).get("consensus") or "").upper() positives: List[str] = [] issues: List[str] = [] vetoes: List[str] = [] score = 0.0 if row.get("playable"): score += 18.0 positives.append("base_model_playable") else: score -= 18.0 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 += 20.0 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)) score += self.MARKET_PRIORS.get(market, -3.0) data_quality = package.get("data_quality") or {} quality_score = self._safe_float(data_quality.get("score"), 0.6) or 0.6 score += max(-8.0, min(6.0, (quality_score - 0.55) * 16.0)) 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") 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") if divergence is not None: if divergence >= self.HARD_DIVERGENCE and not is_value_sniper: score -= 42.0 vetoes.append("v25_v27_hard_disagreement") elif divergence >= self.SOFT_DIVERGENCE: score -= 18.0 issues.append("v25_v27_soft_disagreement") else: score += 11.0 positives.append("v25_v27_aligned") # Trap market detection: market overpriced vs historical band hit rate trap_market_flag = False trap_market_gap = None if isinstance(triple, dict): band_rate_val = self._safe_float(triple.get("band_rate")) implied_val = self._safe_float(triple.get("implied_prob")) if ( band_rate_val is not None and implied_val is not None and band_sample >= self.MIN_BAND_SAMPLE and (implied_val - band_rate_val) > self.TRAP_MARKET_GAP ): trap_market_flag = True trap_market_gap = round(implied_val - band_rate_val, 4) score -= 14.0 issues.append("trap_market_market_overpriced") if isinstance(triple, dict): if triple_is_value: score += 18.0 positives.append("triple_value_confirmed") elif market in {"DC", "MS", "OU25", "BTTS"}: score -= 18.0 issues.append("triple_value_not_confirmed") if band_sample >= 25: score += 8.0 positives.append("strong_historical_sample") elif band_sample >= self.MIN_BAND_SAMPLE: score += 3.0 positives.append("usable_historical_sample") else: score -= 16.0 issues.append("historical_sample_too_low") if market == "DC" and not is_value_sniper: vetoes.append("dc_without_historical_sample") elif market in {"MS", "DC", "OU25"}: score -= 10.0 issues.append("missing_triple_value_evidence") if consensus == "DISAGREE" and market in {"MS", "DC"}: score -= 12.0 issues.append("engine_consensus_disagree") if ( model_prob is not None and model_gap is not None 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: vetoes.append("volatile_market_requires_exceptional_evidence") # Sniper override: bypass eligible vetoes when value sniper triggered sniper_bypassed: List[str] = [] if is_value_sniper and vetoes: remaining = [] for v in vetoes: if v in self.SNIPER_BYPASSABLE_VETOES: sniper_bypassed.append(v) else: remaining.append(v) vetoes = remaining if sniper_bypassed: positives.append("sniper_bypassed_soft_vetoes") score = max(0.0, min(100.0, score)) action = "BET" if vetoes: # B-1: when only veto is odds_below_minimum, switch to WATCH_NO_VALUE # so user still sees model commentary instead of blank rejection. if vetoes == ["odds_below_minimum"]: action = "WATCH_NO_VALUE" else: action = "REJECT" elif score < self.MIN_WATCH_SCORE and not is_value_sniper: action = "REJECT" elif score < self.MIN_BET_SCORE and not is_value_sniper: action = "WATCH" row["betting_brain"] = { "action": action, "score": round(score, 1), "summary": self._summary(action, market, pick, positives, issues, vetoes), "positives": positives[:5], "issues": issues[:6], "vetoes": vetoes[:6], "sniper_bypassed": sniper_bypassed, "trap_market_flag": trap_market_flag, "trap_market_gap": trap_market_gap, "model_prob": round(model_prob, 4) if model_prob is not None else None, "implied_prob": round(implied, 4), "model_market_gap": round(model_gap, 4) if model_gap is not None else None, "v27_prob": round(v27_prob, 4) if v27_prob is not None else None, "divergence": round(divergence, 4) if divergence is not None else None, "triple_key": triple_key, "triple_value": triple, } if action != "BET": self._force_no_bet(row, f"betting_brain_{action.lower()}") else: row["is_guaranteed"] = bool(score >= 82.0) row["pick_reason"] = "betting_brain_approved" row["stake_units"] = self._brain_stake(row, score) row["bet_grade"] = "A" if score >= 82.0 else "B" row["playable"] = True self._append_reason(row, f"betting_brain_{action.lower()}_{round(score)}") return row def _collect_rows(self, package: Dict[str, Any]) -> List[Dict[str, Any]]: rows: Dict[str, Dict[str, Any]] = {} 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"): for item in package.get(source) or []: if isinstance(item, dict) and item.get("market"): key = self._row_key(item) rows[key] = self._merge_row(rows.get(key), item) # B-2: ensure both MS sides (and DC sides) have an entry — give user the # model's read on the opposite outcome even when upstream filtered it out. self._inject_reference_rows(rows, package) return list(rows.values()) def _inject_reference_rows( self, rows: Dict[str, Dict[str, Any]], package: Dict[str, Any], ) -> None: market_board = package.get("market_board") or {} ms_board = market_board.get("MS") if isinstance(market_board, dict) else None if not isinstance(ms_board, dict): return probs = ms_board.get("probs") if isinstance(ms_board.get("probs"), dict) else {} if not probs: return # Pull MS odds from any existing MS row to estimate the missing side's odds existing_odds_by_pick: Dict[str, float] = {} for row in rows.values(): if str(row.get("market")) == "MS": pick = str(row.get("pick")) odd = self._safe_float(row.get("odds"), 0.0) or 0.0 if pick and odd > 1.0: existing_odds_by_pick[pick] = odd for pick in ("1", "X", "2"): key = f"MS:{pick}" if key in rows: continue prob = self._safe_float(probs.get(pick), 0.0) if prob is None or prob <= 0.0: continue implied_odd = round(1.0 / prob, 2) if prob > 0.01 else 0.0 ref_odd = existing_odds_by_pick.get(pick) or implied_odd rows[key] = { "market": "MS", "pick": pick, "probability": round(prob, 4), "confidence": round(prob * 100.0, 1), "raw_confidence": round(prob * 100.0, 1), "calibrated_confidence": round(prob * 100.0, 1), "odds": ref_odd, "is_underdog_reference": True, "playable": False, "stake_units": 0.0, "bet_grade": "PASS", "decision_reasons": ["underdog_reference_for_completeness"], } @staticmethod def _merge_row(existing: Optional[Dict[str, Any]], incoming: Dict[str, Any]) -> Dict[str, Any]: if existing is None: return dict(incoming) merged = dict(incoming) merged.update({k: v for k, v in existing.items() if v is not None}) for key in ("decision_reasons", "reasons"): reasons = list(existing.get(key) or []) + list(incoming.get(key) or []) if reasons: merged[key] = list(dict.fromkeys(reasons)) return merged def _pick_value_candidate(self, rows: Dict[str, Dict[str, Any]], main_key: str) -> Optional[Dict[str, Any]]: candidates = [ row for key, row in rows.items() if key != main_key and row.get("betting_brain", {}).get("action") in {"BET", "WATCH"} and (self._safe_float(row.get("odds"), 0.0) or 0.0) >= 1.60 ] candidates.sort(key=self._candidate_sort_key, reverse=True) return dict(candidates[0]) if candidates else None def _summary_item(self, row: Dict[str, Any]) -> Dict[str, Any]: reasons = list(row.get("decision_reasons") or row.get("reasons") or []) return { "market": row.get("market"), "pick": row.get("pick"), "raw_confidence": row.get("raw_confidence", row.get("confidence")), "calibrated_confidence": row.get("calibrated_confidence", row.get("confidence")), "bet_grade": row.get("bet_grade", "PASS"), "playable": bool(row.get("playable")), "stake_units": float(row.get("stake_units", 0.0) or 0.0), "play_score": row.get("play_score", 0.0), "ev_edge": row.get("ev_edge", row.get("edge", 0.0)), "implied_prob": row.get("implied_prob", 0.0), "odds_reliability": row.get("odds_reliability", 0.35), "odds": row.get("odds", 0.0), "reasons": reasons[:6], "is_underdog_reference": bool(row.get("is_underdog_reference")), "betting_brain": row.get("betting_brain"), } @staticmethod def _candidate_sort_key(row: Dict[str, Any]) -> Tuple[float, float, float]: brain = row.get("betting_brain") or {} action_boost = {"BET": 2.0, "WATCH": 1.0, "REJECT": 0.0}.get(str(brain.get("action")), 0.0) return ( action_boost, float(brain.get("score", 0.0) or 0.0), float(row.get("play_score", 0.0) or 0.0), ) @staticmethod def _row_key(row: Optional[Dict[str, Any]]) -> str: if not isinstance(row, dict): return "" return f"{row.get('market')}:{row.get('pick')}" def _force_no_bet(self, row: Dict[str, Any], reason: str) -> None: row["playable"] = False row["stake_units"] = 0.0 row["bet_grade"] = "PASS" row["is_guaranteed"] = False row["pick_reason"] = reason if row.get("signal_tier") == "CORE": row["signal_tier"] = "PASS" self._append_reason(row, reason) @staticmethod def _append_reason(row: Dict[str, Any], reason: str) -> None: key = "decision_reasons" if "decision_reasons" in row else "reasons" reasons = list(row.get(key) or []) if reason not in reasons: reasons.append(reason) row[key] = reasons[:6] def _brain_stake(self, row: Dict[str, Any], score: float) -> float: existing = self._safe_float(row.get("stake_units"), 0.0) or 0.0 odds = self._safe_float(row.get("odds"), 0.0) or 0.0 if odds <= 1.0: return 0.0 cap = 2.0 if score >= 82.0 else 1.2 if score < 78.0: cap = 0.8 return round(max(0.25, min(existing if existing > 0 else cap, cap)), 1) @staticmethod def _decision_band(main_pick: Optional[Dict[str, Any]]) -> str: if not main_pick: return "LOW" score = float((main_pick.get("betting_brain") or {}).get("score", 0.0) or 0.0) if score >= 82.0: return "HIGH" if score >= 72.0: return "MEDIUM" return "LOW" @staticmethod def _top_decisions(decisions: List[Dict[str, Any]]) -> List[Dict[str, Any]]: ordered = sorted(decisions, key=lambda d: float(d.get("score", 0.0) or 0.0), reverse=True) return [ { "action": item.get("action"), "score": item.get("score"), "summary": item.get("summary"), "vetoes": item.get("vetoes", []), "issues": item.get("issues", []), } for item in ordered[:5] ] @staticmethod def _summary(action: str, market: str, pick: str, positives: List[str], issues: List[str], vetoes: List[str]) -> str: if action == "BET": return f"{market} {pick} approved: evidence is aligned enough for a controlled stake." if action == "WATCH": return f"{market} {pick} is interesting but not clean enough for stake." if action == "WATCH_NO_VALUE": return f"{market} {pick}: model favoriyle hemfikir, fakat oran ({', '.join(vetoes[:1]) or 'düşük'}) bahis için yetersiz." if vetoes: return f"{market} {pick} rejected: {', '.join(vetoes[:3])}." if issues: return f"{market} {pick} rejected: {', '.join(issues[:3])}." return f"{market} {pick} rejected by evidence score." def _market_probability(self, row: Dict[str, Any], package: Dict[str, Any]) -> Optional[float]: direct = self._safe_float(row.get("probability")) if direct is not None: return direct board = package.get("market_board") or {} payload = board.get(str(row.get("market") or "")) if isinstance(board, dict) else None probs = payload.get("probs") if isinstance(payload, dict) else None if not isinstance(probs, dict): return None key = self._prob_key(str(row.get("market") or ""), str(row.get("pick") or "")) return self._safe_float(probs.get(key)) if key else None def _v27_probability(self, market: str, pick: str, v27_engine: Dict[str, Any]) -> Optional[float]: predictions = v27_engine.get("predictions") or {} ms = predictions.get("ms") or {} ou25 = predictions.get("ou25") or {} if market == "MS": return self._safe_float(ms.get({"1": "home", "X": "draw", "2": "away"}.get(pick, ""))) if market == "DC": home = self._safe_float(ms.get("home"), 0.0) or 0.0 draw = self._safe_float(ms.get("draw"), 0.0) or 0.0 away = self._safe_float(ms.get("away"), 0.0) or 0.0 return {"1X": home + draw, "X2": draw + away, "12": home + away}.get(pick) if market == "OU25": key = self._prob_key(market, pick) return self._safe_float(ou25.get(key)) if key else None return None def _triple_value(self, package: Dict[str, Any], key: Optional[str]) -> Optional[Dict[str, Any]]: if not key: return None value = ((package.get("v27_engine") or {}).get("triple_value") or {}).get(key) return value if isinstance(value, dict) else None def _triple_key(self, market: str, pick: str) -> Optional[str]: prob_key = self._prob_key(market, pick) if market == "MS": return {"1": "home", "2": "away"}.get(pick) if market == "DC" and pick.upper() in {"1X", "X2", "12"}: return f"dc_{pick.lower()}" if market in {"OU15", "OU25", "OU35"} and prob_key == "over": return f"{market.lower()}_over" if market == "BTTS" and prob_key == "yes": return "btts_yes" if market == "HT": return {"1": "ht_home", "2": "ht_away"}.get(pick) if market in {"HT_OU05", "HT_OU15"} and prob_key == "over": return f"{market.lower()}_over" if market == "OE" and prob_key == "odd": return "oe_odd" if market == "CARDS" and prob_key == "over": return "cards_over" if market == "HTFT" and "/" in pick: return f"htft_{pick.replace('/', '').lower()}" return None @staticmethod def _prob_key(market: str, pick: str) -> Optional[str]: norm = str(pick or "").strip().casefold() if market in {"MS", "HT", "HCAP"}: return pick if pick in {"1", "X", "2"} else None if market == "DC": return pick.upper() if pick.upper() in {"1X", "X2", "12"} else None if market in {"OU15", "OU25", "OU35", "HT_OU05", "HT_OU15", "CARDS"}: if "over" in norm or "ust" in norm or "üst" in norm: return "over" if "under" in norm or "alt" in norm: return "under" if market == "BTTS": if "yes" in norm or "var" in norm: return "yes" if "no" in norm or "yok" in norm: return "no" if market == "OE": if "odd" in norm or "tek" in norm: return "odd" if "even" in norm or "cift" in norm or "çift" in norm: return "even" if market == "HTFT" and "/" in pick: return pick return None @staticmethod def _safe_float(value: Any, default: Optional[float] = None) -> Optional[float]: try: return float(value) except (TypeError, ValueError): return default