fix: calibrate recommendation confidence scores

platform/backend/app/analytics/daily_card_generator.py

@@ -354,6 +354,103 @@ def _confidence_score(
   return round(_clamp(raw, 0, 96), 1)
 
 
+
+
+def _stable_confidence_jitter(*parts: object) -> float:
+  """Deterministic tie-breaker so equal model priors do not all display the same score."""
+
+  seed = '|'.join(str(part or '') for part in parts)
+  checksum = sum((index + 1) * ord(char) for index, char in enumerate(seed))
+  return ((checksum % 49) - 24) / 10.0
+
+
+def _finalize_confidence_score(
+  score: float,
+  *,
+  match_id: str,
+  market_type: str,
+  selection: str,
+  recommendation: str,
+  risk_level: str,
+  market_tier: str,
+  data_quality: str,
+  has_market_odds: bool,
+  odds_source_kind: str,
+  target_odds: float,
+  true_prob: float,
+  ev_percent: float,
+  edge_percent: float,
+) -> float:
+  """Turn raw model score into a public-facing confidence rating.
+
+  The score is not a hit probability. It is a review priority that must reflect
+  source quality, market availability, play volatility, and model/value strength.
+  """
+
+  adjusted = float(score)
+
+  if odds_source_kind == 'conditional_threshold':
+    adjusted -= 3.2
+  elif odds_source_kind == 'reference_market':
+    adjusted -= 1.7
+  elif odds_source_kind in {'mixed_market_source', 'single_provider_market'}:
+    adjusted -= 2.4
+  elif odds_source_kind in {'market', 'multi_book_market'}:
+    adjusted += 2.0
+
+  if data_quality == 'fallback_prior':
+    adjusted -= 4.5
+  elif data_quality == 'rank_elo_prior':
+    adjusted -= 1.6
+  elif data_quality == 'observed':
+    adjusted += 1.5
+
+  if not has_market_odds:
+    adjusted -= 1.8
+  if risk_level in {'speculative', 'sgp'} or market_tier in {'exact_score', 'speculative', 'sgp'}:
+    adjusted -= 3.5
+  elif risk_level == 'parlay' or market_tier == 'parlay':
+    adjusted -= 1.8
+
+  adjusted -= _clamp(max(target_odds - 3.2, 0.0) * 1.4, 0.0, 6.0)
+  adjusted += _clamp((true_prob - 0.56) * 10.0, -2.5, 3.0)
+  adjusted += _clamp((ev_percent - 3.0) * 0.10, -1.2, 2.6)
+  adjusted += _clamp((edge_percent - 2.0) * 0.08, -1.0, 2.4)
+  adjusted += _stable_confidence_jitter(match_id, market_type, selection, recommendation, target_odds)
+
+  if market_tier in {'exact_score', 'speculative', 'sgp'} or risk_level in {'speculative', 'sgp'}:
+    floor = 12.0
+  elif risk_level == 'parlay' or market_tier == 'parlay':
+    floor = 18.0
+  else:
+    floor = 24.0
+
+  if has_market_odds:
+    ceiling = 92.0
+  elif odds_source_kind == 'reference_market':
+    ceiling = 66.5
+  elif odds_source_kind == 'conditional_threshold':
+    ceiling = 61.5
+  else:
+    ceiling = 64.0
+
+  if data_quality == 'fallback_prior':
+    ceiling = min(ceiling, 56.0)
+  elif data_quality == 'rank_elo_prior':
+    ceiling = min(ceiling, 66.0)
+
+  if market_tier in {'exact_score', 'speculative'}:
+    ceiling = min(ceiling, 46.0)
+  elif market_tier == 'sgp':
+    ceiling = min(ceiling, 44.0)
+  elif market_tier == 'parlay':
+    ceiling = min(ceiling, 54.0 if not has_market_odds else 68.0)
+
+  if ceiling < floor:
+    floor = max(8.0, ceiling - 8.0)
+
+  return round(_clamp(adjusted, floor, ceiling), 1)
+
 def _confidence_band(score: float, has_market_odds: bool, risk_level: str) -> str:
   if not has_market_odds:
     if score >= 64:
@@ -584,6 +681,22 @@ def _build_pick(
     has_market_odds=has_market_odds,
     market_tier=market_tier,
   )
+  confidence = _finalize_confidence_score(
+    confidence,
+    match_id=match_id,
+    market_type=market_type,
+    selection=selection,
+    recommendation=recommendation,
+    risk_level=risk_level,
+    market_tier=market_tier,
+    data_quality=data_quality,
+    has_market_odds=has_market_odds,
+    odds_source_kind=odds_source_kind,
+    target_odds=odds,
+    true_prob=true_prob,
+    ev_percent=ev,
+    edge_percent=edge,
+  )
   implied = (1.0 / odds) * 100
   fair_note = ''
   enriched_checks = list(data_checks)
@@ -691,6 +804,22 @@ def _build_conditional_pick(
     has_market_odds=has_market_odds,
     market_tier=market_tier,
   )
+  confidence = _finalize_confidence_score(
+    confidence,
+    match_id=match_id,
+    market_type=market_type,
+    selection=selection,
+    recommendation=recommendation,
+    risk_level=risk_level,
+    market_tier=market_tier,
+    data_quality=data_quality,
+    has_market_odds=has_market_odds,
+    odds_source_kind=odds_source_kind,
+    target_odds=target_odds,
+    true_prob=true_prob,
+    ev_percent=min_ev,
+    edge_percent=edge,
+  )
   quality_note = _quality_note(data_quality, has_market_odds)
   confidence_band = _confidence_band(confidence, has_market_odds, risk_level)
   confidence_factors = _confidence_factors(
@@ -830,6 +959,22 @@ def _build_cross_match_parlays(safe_singles: list[dict[str, Any]]) -> list[dict[
         has_market_odds=combo_has_market_odds,
         market_tier='parlay',
       )
+      confidence = _finalize_confidence_score(
+        confidence,
+        match_id=f'PARLAY-CROSS-MATCH-{size}-{len(parlays) + 1}',
+        market_type='跨場串關',
+        selection=f'{size} 串 1',
+        recommendation='SAFE_PARLAY',
+        risk_level='parlay',
+        market_tier='parlay',
+        data_quality=combo_data_quality,
+        has_market_odds=combo_has_market_odds,
+        odds_source_kind=combo_source_kind,
+        target_odds=target_combo_odds,
+        true_prob=combined_prob,
+        ev_percent=ev,
+        edge_percent=edge,
+      )
       parlays.append(
         {
           'match_id': f'PARLAY-CROSS-MATCH-{size}-{len(parlays) + 1}',
@@ -939,6 +1084,22 @@ def _build_same_game_parlays(picks_by_match: dict[str, list[dict[str, Any]]]) ->
       has_market_odds=sgp_has_market_odds,
       market_tier='sgp',
     )
+    confidence = _finalize_confidence_score(
+      confidence,
+      match_id=match_id,
+      market_type='同場串關',
+      selection=f"{first['selection']} + {second['selection']}",
+      recommendation='SGP_LOTTERY',
+      risk_level='sgp',
+      market_tier='sgp',
+      data_quality=sgp_data_quality,
+      has_market_odds=sgp_has_market_odds,
+      odds_source_kind=sgp_source_kind,
+      target_odds=target_combo_odds,
+      true_prob=combo_prob,
+      ev_percent=ev,
+      edge_percent=edge,
+    )
 
     sgps.append(
       {