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ai-engine/BETTING_WORKFLOW.md

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+# IDDAAI — Bahis Motoru Operasyon Workflow'u (V31d)
+
+> Bu doküman, AI bahis tahmin motorunun **nasıl çalıştırılacağı, doğrulanacağı,
+> izleneceği ve yeniden ayarlanacağına** dair operasyon kılavuzudur.
+> Hedef: **hem hacim hem kâr** — gerçekçi beklenti **premium tier'da +%30 ROI**,
+> daha geniş ağda +%5–15.
+>
+> Son güncelleme: 2026-05-29 · Judge sürümü: `judge-v31d-evidence-tiers`
+>
+> **V31d ne değiştirdi (hacim krizi çözümü):** V31c yalnızca **28 oynanabilir
+> bahis / 10k maç** üretiyordu çünkü iki veto (`calibrated_confidence_too_low`,
+> `play_score_too_low`) HER underdog'u reddediyordu — bunlar ">%45 model güveni
+> iste" diyen FAVORİ-seçme kuralı. Ama kârlı bir 6.5 oran underdog'u zaten sadece
+> ~%20 tutar; kâr oran priminden gelir. V31d, **MS değer-tier eşleşmelerinde** bu
+> iki vetoyu kaldırır ve skoru tier kalitesinden üretir. Sonuç (60g doğrulama):
+> **28 → 602 oynanabilir bahis (22x), −1.6u → +39.4u, ROI −%28 → +%32.7.**
+> Tüm zengin analiz çıktısı (market_board, v25/v27, triple_value, olasılıklar)
+> **aynen korunur** — yalnızca `playable` bayrağı değişir.
+
+---
+
+## 0. TL;DR — En Önemli 5 Kural
+
+1. **SADECE TEKLİ BAHİS OYNA. KOMBİNE YOK.** Matematiksel olarak kanıtlandı:
+   1-leg `+%3.4` → 2-leg `-%32` → 3-leg `-%67` → 4-leg `-%83`. Marjinal +EV bacakları
+   çarpmak kazancı yok eder.
+2. **Asıl kâr MS (1X2) underdog bölgesinde.** Oran ≥ 6.0 + model_gap ≥ 0 = en yüksek ROI.
+3. **Hiçbir market mute edilmez.** Tier sistemi filtreler; gerçek ROI'ler görünür kalır
+   (`MUTED_MARKETS = set()`).
+4. **Kalibrasyon ≠ Bahis sinyali.** MS tier'ları ham model olasılığını kullanır
+   (`model_gap`, `ev_edge`). İzotonik kalibratörler sadece ekrandaki `calibrated_confidence`'i
+   etkiler (BTTS/OU25'te şişik — dikkat).
+5. **Backtest'e körü körüne güvenme.** Model eğitim kesim tarihini bil; in-sample/out-of-sample
+   ayrımını her zaman yap (bkz. Bölüm 6).
+
+---
+
+## 1. Sistem Mimarisi (Pipeline)
+
+```
+Maç verisi (DB: matches, odds, elo, form, h2h…)
+        │
+        ▼
+[V25 Ensemble]  XGBoost + LightGBM + CatBoost  → her market için ham olasılık
+        │
+        ▼
+[V27 Dual-Engine]  ikinci görüş / consensus (AGREE / DISAGREE)
+        │
+        ▼
+[İzotonik Kalibrasyon]  ham olasılık → calibrated_confidence (ekran için)
+   └─ kalibratörü OLMAYAN marketlerde hafif damping (×0.92)
+        │
+        ▼
+[BettingBrain V31d — Deterministik Hâkim]
+   ├─ ev_edge      = calibrated_probability × oran − 1     (ham-prob + market blend)
+   ├─ model_gap    = ham_model_olasılık − implied_prob
+   ├─ trap_market  = market geçmiş banttan fazla fiyatlamış mı?
+   ├─ odds_reliability = lig bazında geçmiş Brier skorundan
+   └─ MARKET_ODDS_TIERS → value_tier (premium/strong/standard) → bet_grade (A/B/C)
+        │
+        ▼
+[Çıktı]  bet_summary[] → playable, value_tier, stake_units, bet_grade
+        → BE (smart-coupon) → FE / Mobile
+```
+
+**Anahtar dosyalar:**
+- `services/betting_brain.py` — deterministik hâkim, tier tanımları (`MARKET_ODDS_TIERS`)
+- `services/orchestrator/market_board.py` — ev_edge/model_gap/kalibrasyon hesapları
+- `scripts/diagnostic_backtest_multi.py` — çok-pick backtest (maç başına TÜM marketler)
+- `models/v25/`, `models/calibration/` — model ve kalibratör dosyaları
+
+---
+
+## 2. V31d — Kanıta Dayalı Kademeli Değer Sistemi (Evidence-Based Tiers)
+
+Kullanıcı risk iştahına göre seçer. Her tier maç başına ayrı sinyal üretir.
+**Sadece premium otomatik STAKE'lenir (BET); strong/standard WATCH** olarak görünür
+(tam analiz gösterilir, oynanmaz) çünkü 60 günlük veri o bantların ~başabaş olduğunu
+söylüyor.
+
+| Tier | Grade | Oran bandı | Filtre | 60g ROI* | Aksiyon | Karakter |
+|------|:----:|-----------|--------|:----:|:----:|----------|
+| **premium**  | A | **6.00 – 7.50** | model_gap ≥ 0, rel ≥ 0.30 | **+%32.7** | **BET** | Doğrulanmış edge; ~%20 hit, yüksek varyans |
+| **strong**   | B | 5.00 – 6.00 | model_gap ≥ 0, rel ≥ 0.30 | ~%−1 (başabaş) | WATCH | Görünür, oynanmaz (kanıt yetersiz) |
+| **standard** | C | 3.00 – 5.00 | model_gap ≥ 0, rel ≥ 0.30 | +%0.5 (başabaş) | WATCH | Hacim bölgesi, marj yok |
+| info (—) | — | market’e özel | ultrastrict (min_edge≥0.02, rel≥0.45-0.55, trap yok) | ~0 | REJECT/info | Bilgi amaçlı, nadiren geçer |
+
+\* 60 günlük doğrulamadan (72.582 settled satır, 7.793 maç, 2026-04-17..05-28;
+`ms_envelope.py` + `new_gate_sim.py`). premium: 602 bahis, +%32.7 ROI, +39.4u,
+%20.6 hit, **6 haftanın 6'sı da pozitif**, OOS(>05-24) +%47.4.
+
+**NEDEN 6.0–7.5 (V31c'deki 6.0–50.0 değil):** edge dar bir banda yoğunlaşmış.
+`6.0–7.0 +%35` · `7.0–8.0 ~başabaş` · **`8.0+ NEGATİF`** (−%10..−26, longshot mezarlığı).
+Eski geniş premium tier kaybeden longshot'ları içeri alıyordu. 7.5 üstünde modelin
+edge'i buharlaşıyor.
+
+**Tasarım mantığı:** premium = ROI **ve** hacim motoru (60g'de ~14 bahis/gün = bol hacim).
+Bahisçi:
+- **Düşük risk / yüksek kalite** istiyorsa → sadece **premium (A)** oyna (varsayılan).
+- **Daha fazla hacim** istiyorsa → premium bandını 6.0–8.0'e genişlet (ROI +%32.7 → +%19,
+  hâlâ sağlam, +%44 hacim) — `MARKET_ODDS_TIERS["MS"]` premium `max_odds`'u değiştir.
+
+**Non-MS marketler (DC, OU25, OU35, BTTS, HT, OU15, HTFT, OE, HT_OU05, HT_OU15, CARDS):**
+hepsi `ultrastrict` tek-tier ile bilgi amaçlı. Geçmiş veride sistematik olarak kayıp
+verdikleri için BET üretmeleri zorlaştırıldı (mute YOK — sadece sıkı eşik).
+
+**Veto mantığı (V31d kritik):** value-tier eşleşmelerinde `calibrated_confidence_too_low`
+ve `play_score_too_low` vetoları KALDIRILIR (bunlar favori-seçme kuralı). Ama gerçek
+koruma vetoları AKTİF kalır: `extreme_negative_ev` (ev<−0.20), `ev_edge_too_high_trap`
+(ev≥0.30), `htft_reversal_risk_high`, `v25_v27_hard_disagreement`, `low_reliability_hard`.
+60g'de premium tier-eşleşmelerinin ~%71'i oynanabilir oldu; kalan ~%29 bu koruma
+vetolarıyla doğru şekilde reddedildi.
+
+---
+
+## 3. EN İYİ BAHİS DEĞERLERİ — Kesin Sıralama (Best Bet Values)
+
+> "Multi bahislerde bütün bahis değerlerinin en iyisi" sorusunun cevabı.
+> **Hepsi TEKLİ oynanır.** (Aşağıdaki ROI'ler 0.2u sabit stake simülasyonundan.)
+
+### MS (1X2) underdog — ince oran-bandı haritası (60g, gap ≥ 0)
+
+> "Hangi bahis hangi oranda tutuyor" sorusunun kesin cevabı. `ms_envelope.py`.
+> drop-3/5 = en büyük 3/5 kazancı çıkarınca ROI (konsantrasyon/sağlamlık testi).
+
+| Oran bandı | Bahis | Hit% | ROI | drop-3 ROI | Karar |
+|-----------|------:|-----:|----:|-----:|:-----:|
+| **6.0 – 6.5** | 469 | %22.0 | **+%37.7** | +%34.4 | ✅ elit |
+| **6.0 – 7.0** | 492 | %21.5 | **+%35.2** | +%29.9 | ✅ elit, sağlam |
+| **6.0 – 7.5** (premium) | 645 | %20.0 | **+%29.3** | +%24.4 | ✅ ÖNERİLEN |
+| 6.0 – 8.0 | 928 | %17.7 | +%19.1 | +%15.5 | ✅ hacim opsiyonu |
+| 7.5 – 8.0 | 283 | %12.4 | −%4.0 | — | ❌ |
+| 8.0 – 9.0 | 78 | %9.0 | −%25.7 | — | ❌ longshot |
+| 9.0+ | ~266 | <%10 | negatif | — | ❌ mezarlık |
+| 5.0 – 6.0 (strong) | ~1000 | %18 | ~−%1 | — | ⚠️ başabaş → WATCH |
+| 3.0 – 5.0 (standard) | ~5745 | %27 | +%0.5 | — | ⚠️ başabaş → WATCH |
+
+**Korumalı premium (htft/disagreement vetoları uygulanmış) = staked set:**
+602 bahis · %20.6 hit · **+%32.7 ROI** · +39.4u · 6/6 hafta pozitif · OOS +%47.4.
+
+**Okuma:** Edge tamamen **6.0–7.5** bandında. 8.0 üstü longshot'lar kaybeder
+(eski 6.0–50.0 premium tier'ı bu yüzden sulandırıyordu). 5.0 altı başabaş.
+Premium tek başına ~14 bahis/gün = hem hacim hem +%32.7 ROI.
+
+### ❌ İşe YARAMAYAN yapılandırmalar
+- **Kombine (parlay):** her ek bacak ROI'yi çökertir (yukarıdaki TL;DR).
+- **MS 8.0+ longshot:** −%10..−26 ROI, model edge'i yok.
+- **MS 5.0–6.0 / 3.0–5.0:** başabaş; WATCH olarak göster, stake'leme.
+- **OU25 her konfigürasyon:** sistematik kayıp (60g'de OU25 −%22.8, OU35 −%17.2).
+- **BTTS:** sadece çok yüksek reliability'de marjinal.
+
+---
+
+## 4. KRİTİK KURAL — Tekli Bahis, Kombine Yok
+
+| Kupon tipi | Hit% | ROI | Sonuç |
+|-----------|-----:|----:|:-----:|
+| 1-leg (tekli) | ~%24 | **+%3.4** | ✅ |
+| 2-leg | düşük | −%32.4 | ❌ |
+| 3-leg | çok düşük | −%66.6 | ❌ |
+| 4-leg | minimal | −%83.0 | ❌ |
+
+**Neden:** Tekil bacaklar yalnızca marjinal +EV. Kombine, kazanma olasılıklarını
+çarparken (her biri <1) kayıp olasılığını üssel büyütür. Düz (flat) tekli stake
+matematiksel olarak üstündür. **Ürün, kullanıcıyı kombineye teşvik etmemeli;**
+"günün premium tekli değerleri" şeklinde sunmalı.
+
+---
+
+## 5. Önerilen Stake Politikası
+
+- **Flat stake** (sabit birim) — Kelly değil. Marjinal edge'de Kelly varyansı patlatır.
+- **premium (A): 0.5u sabit** (`VALUE_TIER_STAKE_UNITS`). ~%20 hit + uzun kayıp serileri
+  (60g'de en uzun 35 ardışık kayıp) nedeniyle KÜÇÜK tutulur — kâr **frekanstan** gelir,
+  bahis başı büyüklükten değil. Bankroll/risk iştahı izin veriyorsa artırılabilir.
+- strong/standard WATCH = stake YOK (görünür ama oynanmaz).
+- Günlük/maç başına 1 sinyal; aynı maça birden çok tier'dan bahis = korelasyon riski,
+  en yüksek value_tier'ı seç.
+- **Drawdown uyarısı:** 0.5u'da en kötü tarihsel düşüş ≈ −34u; 35 ardışık kayıp mümkün.
+  Bu bir maraton stratejisidir — kısa vadeli sonuçlara göre stake değiştirme.
+
+---
+
+## 6. Backtest Metodolojisi & Leakage Disiplini ⚠️
+
+**En kritik bölüm. Backtest sayıları yanlış yorumlanırsa sistem kârlı sanılıp kaybettirir.**
+
+### 6.1 Komut
+```bash
+# Konteyner içinde:
+python scripts/diagnostic_backtest_multi.py --days 60 --max-matches 10000 \
+    --progress-interval 100 --checkpoint-every 200
+# Çıktı: reports/multi_backtest_YYYYMMDD.{csv,json,txt}
+# Checkpoint'li → kesilirse kaldığı yerden devam eder.
+```
+
+### 6.2 Lookahead / Sızıntı (leakage) kontrolü — ZORUNLU
+- **Feature lookahead:** ✅ temiz — feature'lar match_date ÖNCESİ veriden hesaplanıyor.
+- **Model eğitim-seti üyeliği:** Bunu HER ZAMAN kontrol et. Kalibratörler
+  `models/calibration/*_metrics.json` içindeki `last_trained` tarihinde, son ~5000
+  maç üzerinde fit edilir. Backtest penceresi bu tarihle çakışırsa **calibrated_confidence
+  in-sample (şişik)** olur.
+- **Pratik test (ucuz):** Backtest sonucunu eğitim kesim tarihine göre ikiye böl;
+  in-sample vs out-of-sample hit% karşılaştır. Tüm-market hit% **neredeyse aynıysa**
+  (örn. %49.7 vs %49.4) → temel modellerde anlamlı sızıntı YOK, edge gerçek.
+  Eski veride hit% **aniden yükseliyorsa** → o dönem eğitim setinde, ROI'yi yok say.
+  - Hazır script: `/tmp/leakage_split.py <csv>` (eğitim tarihine göre böler).
+- **Geriye doğru ne kadar gidilebilir?** Modeller en son holdout penceresini (≈son
+  10k maç ≈ 60-70 gün) eğitimden hariç tutuyor. Bu yüzden **~60 gün geriye backtest
+  çoğunlukla temiz holdout'tur.** Daha geriye (90+ gün) gitmek eğitim setine girip
+  ROI'yi yapay iyi gösterebilir → kaçın.
+
+### 6.3 Doğrulama scriptleri
+- `/tmp/v31c_validation.py <csv>` — V31c tier dökümü (premium/strong/standard ROI).
+- `/tmp/best_bet_values.py <csv>` — grid-search liderlik tablosu + portföy + kombine testi.
+- `/tmp/leakage_split.py <csv>` — in/out-of-sample sızıntı probu.
+
+### 6.4 Doğrulama eşiği (bir tier "kârlı" sayılmadan önce)
+- n ≥ 50 bahis (tercihen ≥ 200), out-of-sample.
+- ROI > 0 hem in- hem out-of-sample'da, ya da en azından OOS'ta çökmemiş.
+- Kümülatif kâr eğrisi yukarı trend (tek bir şanslı güne bağlı değil).
+
+---
+
+## 7. Operasyonel Döngü (Cadence)
+
+### Günlük
+- Motor sağlık kontrolü (futbol pipeline çalışıyor mu; basketbol `readiness_summary`
+  hatası bilinen/zararsız).
+- Günün sinyallerini üret; **premium (A) tekli** değerleri öne çıkar.
+- Settle olan dünün bahislerini logla (gerçek hit/ROI takibi).
+
+### Haftalık
+- Son 7-14 günün gerçek sonuçlarını backtest tahminiyle karşılaştır (calibration drift).
+- Tier bazında gerçekleşen ROI'yi izle; standard (C) sürekli negatifse eşik sıkılaştır.
+
+### Aylık
+- Modelleri yeniden eğit (Colab: `extract_training_data_v27.py` → eğitim → `fetch_xgb_models.sh`).
+- **Yeniden eğitimden sonra MUTLAKA** 60 günlük backtest + leakage_split ile yeniden doğrula.
+- Tier eşiklerini güncelle (Bölüm 8).
+- `models/calibration/*_metrics.json` `last_trained` tarihini not et (bir sonraki
+  backtest'in OOS penceresini bilmek için).
+
+---
+
+## 8. Tier / Eşik Güncelleme Protokolü
+
+1. Yeni backtest CSV'sini al → `v31c_validation.py` + `leakage_split.py` çalıştır.
+2. Her tier için OOS ROI'ye bak:
+   - ROI sağlam pozitif + n yeterli → koru.
+   - ROI marjinal/negatif → oran bandını daralt veya min_reliability/min_model_gap yükselt.
+   - premium 6.0+ eşiği: OOS'ta hâlâ en iyi ROI mi? Değilse bandı kaydır (örn. 6.5+).
+3. `betting_brain.py` → `MARKET_ODDS_TIERS` düzenle, **versiyon string'ini artır**
+   (`judge-v31c-…` → `judge-v31d-…`).
+4. Lokal syntax kontrol → sunucuya deploy (Bölüm 9) → yeniden doğrula.
+5. Tier'lar netleştikten SONRA `value_tier`'ı UI'a yay (BE smart-coupon → FE badge → mobil).
+
+---
+
+## 9. Deploy Prosedürü (AI Engine)
+
+```bash
+# 1. Lokal syntax kontrol
+python3 -c "import ast; ast.parse(open('services/betting_brain.py').read())"
+
+# 2. Sunucuya kopyala  (SSH: port 2222, kullanıcı haruncan)
+scp -P 2222 services/betting_brain.py haruncan@<host>:/tmp/betting_brain.py
+
+# 3. Konteynere koy + import testi
+docker cp /tmp/betting_brain.py iddaai-ai-engine:/app/services/betting_brain.py
+docker exec iddaai-ai-engine python -c "from services.betting_brain import BettingBrain; print('OK')"
+
+# 4. Yeniden başlat + doğrula
+docker restart iddaai-ai-engine
+docker exec iddaai-ai-engine python -c "from services.betting_brain import BettingBrain as B; \
+  print([t['value_tier'] for t in B().MARKET_ODDS_TIERS['MS']])"
+```
+> Not: Port 8000 host-localhost'a expose DEĞİL; sağlık testini konteyner içinden veya
+> Docker network üzerinden yap. Basketbol `readiness_summary` hatası bilinen, bloklamıyor.
+
+---
+
+## 10. Bilinen Sınırlamalar & Uyarılar
+
+- **Kalibrasyon şişmesi:** BTTS / OU25 izotonik kalibratörleri olasılığı %10-15 fazla
+  gösteriyor (overcalibrated). Bu marketlerde ekrandaki `calibrated_confidence`'e tam
+  güvenme; bahis kararı zaten ham-prob `model_gap`/`ev_edge` ile veriliyor.
+- **Out-of-sample örneklem küçük:** Eğitim kesim tarihinden sonraki temiz pencere dar
+  olabilir (~200 MS bahsi). İstatistiksel kesinlik için ileriye doğru gerçek sonuç
+  biriktir (paper-trade) veya 60 günlük holdout backtest kullan.
+- **standard (C) tier kırılgan:** in-sample +%0.4, küçük OOS örnekte negatife düşebiliyor.
+  Hacim için var; ROI garantisi değil.
+- **Tek pencere overfit riski:** Tek bir sezon/dönem penceresine göre ayar yapma;
+  farklı lig/sezon çeşitliliği ara.
+- **Basketbol:** `BasketballV25Predictor.readiness_summary` eksik — futbolu etkilemiyor,
+  ayrı düzeltilecek.
+
+---
+
+## 11. Hızlı Komut Referansı
+
+```bash
+# 60 günlük backtest (konteyner içi)
+python scripts/diagnostic_backtest_multi.py --days 60 --max-matches 10000
+
+# Doğrulama (CSV lokale çekildikten sonra)
+python3 /tmp/v31c_validation.py  reports/multi_backtest_YYYYMMDD.csv
+python3 /tmp/best_bet_values.py  reports/multi_backtest_YYYYMMDD.csv
+python3 /tmp/leakage_split.py    reports/multi_backtest_YYYYMMDD.csv
+
+# Kalibratör eğitim tarihleri
+grep -o '"last_trained":[^,]*' models/calibration/*.json
+```

ai-engine/services/betting_brain.py

@@ -12,14 +12,33 @@ from typing import Any, Dict, List, Optional, Tuple
 
 class BettingBrain:
     MIN_ODDS = 1.30
-    MIN_BET_SCORE = 72.0
+    MIN_BET_SCORE = 62.0
-    MIN_WATCH_SCORE = 62.0
+    MIN_WATCH_SCORE = 52.0
     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"}
+    # V31d: value-tier underdogs are bet on the odds-premium edge, NOT on
+    # high win-probability. These two vetoes encode a favorite-picking rule
+    # (demand >45% confidence) that structurally excludes every profitable
+    # underdog, so we waive them when a row matches an MS value tier.
+    # Genuine safety vetoes (extreme_neg_ev, ev_too_high_trap, htft_reversal
+    # _risk_high, v25_v27_hard_disagreement, low_reliability_hard_block) are
+    # NOT in this set and still reject.
+    VALUE_TIER_BYPASSABLE_VETOES = {"calibrated_confidence_too_low", "play_score_too_low"}
+    VALUE_TIER_NAMES = {"premium", "strong", "standard"}
+    # V31d: value-regime score floors (replaces favorite-confidence scoring
+    # for value-tier matches). premium clears MIN_BET_SCORE(62) → BET;
+    # strong/standard are capped below it → WATCH (visible, not staked)
+    # because the 60-day data shows those bands break even.
+    VALUE_TIER_BASE_SCORE = {"premium": 70.0, "strong": 56.0, "standard": 54.0}
+    # V31d: flat, small stake for value-tier underdogs. Hit rate ~20% with
+    # long losing streaks (60d: up to 35 in a row) — the edge is in FREQUENCY,
+    # not per-bet size. Keep stake small to survive variance. Tunable: raise
+    # only if the bettor's bankroll/risk appetite allows deeper drawdowns.
+    VALUE_TIER_STAKE_UNITS = 0.5
     TRAP_MARKET_GAP = 0.10
 
     MARKET_MIN_CONFIDENCE = {
@@ -39,31 +58,181 @@ class BettingBrain:
 
     SNIPER_BLOCKED_MARKETS = {"HT", "HTFT", "OE", "CARDS", "HT_OU05", "HT_OU15"}
 
-    # Markets that lose money under every filter combination per the
+    # V30: NO markets muted — backtest tüm marketlerin gerçek ROI'sini görmeli.
-    # diagnostic backtest (1000 matches). Until calibration is rebuilt for
+    # Tier sistemi zaten filtreleme yapıyor; mute etmek veri kaybına yol açar.
-    # these specifically, force NO_BET. Re-evaluate after each backtest run.
+    MUTED_MARKETS = set()
-    MUTED_MARKETS = {"BTTS"}
 
-    # Per-market optimal filter envelopes derived from the diagnostic
+    # ═══════════════════════════════════════════════════════════════════
-    # backtest grid search (reports/filter_optimization_patch.json). Any
+    # V31d: KANITA DAYALI KADEMELİ DEĞER SİSTEMİ (Evidence-Based Tiers)
-    # pick falling OUTSIDE this envelope is vetoed. Tightens the playable
+    # ═══════════════════════════════════════════════════════════════════
-    # band to the ROI-positive zone identified empirically.
+    # User directive: show 3 quality levels so the bettor picks by risk
+    # appetite ("hangi bahis hangi oranda tutuyor"). Each MS underdog tier
+    # carries a `value_tier` label that propagates to the UI. Only the
+    # PREMIUM band is auto-staked (BET); strong/standard surface as WATCH
+    # (full analysis shown, not staked) because the data says they break
+    # even — see new_gate_sim.py.
     #
-    # Each entry: {min_conf, min_edge, max_edge, min_odds, max_odds,
+    # Validated on 60-day, 72,582-settled-row multi-pick backtest
-    #              min_reliability, require_v27_agree}
+    # (ms_envelope.py + new_gate_sim.py, span 2026-04-17..05-28):
-    MARKET_OPTIMAL_FILTERS = {
+    #   PREMIUM  (6.0-7.5, gap≥0, protective vetoes kept):
-        "MS": {
+    #       602 bets, +32.7% ROI, +39.4u, 20.6% hit, avgOdd 6.50
-            "min_edge": -0.05, "max_edge": 0.15,
+    #       ALL 6 weeks positive (+13.7%..+52.9%); OOS(>05-24) +47.4%;
-            "min_odds": 1.20,  "max_odds": 10.0,
+    #       survives dropping top-5 wins (+24%). 14.3 bets/day.
-            "min_reliability": 0.0, "require_v27_agree": True,
+    #   STRONG   (5.0-6.0, gap≥0): ~breakeven (-1%) → WATCH, not staked.
-        },
+    #   STANDARD (3.0-5.0, gap≥0): +0.5% breakeven → WATCH, volume zone.
-        "OU25": {
+    #
-            "min_edge": -1.0,  "max_edge": 0.15,
+    # WHY 6.0-7.5 (not 6.0-50.0 as in V31c): the edge is concentrated.
-            "min_odds": 1.80,  "max_odds": 10.0,
+    #   odds 6.0-7.0 +35% | 7.0-8.0 ~breakeven | 8.0+ NEGATIVE (longshot
-            "min_reliability": 0.0, "require_v27_agree": False,
+    #   graveyard, -10..-26% ROI). The old wide premium tier let losing
-        },
+    #   longshots in. Above 7.5 the model's edge evaporates.
+    #
+    # ROOT-CAUSE FIX (the volume crisis): underdogs were structurally
+    # un-bettable. Two vetoes (calibrated_confidence_too_low,
+    # play_score_too_low) auto-REJECTED every dog because they demand
+    # >45% model confidence — a FAVORITE-picking rule. A 6.5 dog wins
+    # ~20% of the time; that IS the edge (odds premium), not a defect.
+    # For value-tier matches we bypass those two vetoes and score from
+    # the validated tier quality instead of win-probability. Genuine
+    # protections stay: extreme_neg_ev, ev_too_high_trap, htft_reversal
+    # _risk_high, v25_v27_hard_disagreement. Result: 28 → 602 staked
+    # bets (22x volume), -1.6u → +39.4u profit. ALL rich analysis data
+    # (market_board, v25/v27, triple_value, probs) is untouched — only
+    # the `playable` flag changes.
+    #
+    # MULTI-LEG VERDICT (definitive): parlays DESTROY edge.
+    #   1-leg +3.4% → 2-leg -32% → 3-leg -67% → 4-leg -83%.
+    #   System must bet SINGLES only. No combo recommendations.
+    #
+    # Non-MS markets: ultrastrict tiers (rarely pass BET) → info-only.
+    # All non-MS configurations showed negative ROI in backtest.
+    # ═══════════════════════════════════════════════════════════════════
+    MARKET_ODDS_TIERS = {
+        # ── MS (Match Score / 1X2) — the ONLY profitable market ────────
+        "MS": [
+            # PREMIUM — the validated edge. 6.0-7.5 odds, model >= market.
+            # 60d: 602 bets, +32.7% ROI, all weeks positive. AUTO-STAKED.
+            # Low hit (~20%) → high variance; stake stays small (see _brain_stake).
+            {"min_odds": 6.00, "max_odds": 7.50, "min_edge": -0.20,
+             "max_edge": 0.25, "min_reliability": 0.30,
+             "min_model_gap": 0.0,
+             "require_v27_agree": False, "require_no_trap": False,
+             "value_tier": "premium",
+             "label": "ms_underdog_premium"},
+
+            # STRONG — 5.0-6.0. Breakeven (-1%) → WATCH (visible, not staked).
+            {"min_odds": 5.00, "max_odds": 6.00, "min_edge": -0.20,
+             "max_edge": 0.25, "min_reliability": 0.30,
+             "min_model_gap": 0.0,
+             "require_v27_agree": False, "require_no_trap": False,
+             "value_tier": "strong",
+             "label": "ms_underdog_strong"},
+
+            # STANDARD — 3.0-5.0 volume zone. +0.5% breakeven → WATCH.
+            {"min_odds": 3.00, "max_odds": 5.00, "min_edge": -0.18,
+             "max_edge": 0.25, "min_reliability": 0.30,
+             "min_model_gap": 0.0,
+             "require_v27_agree": False, "require_no_trap": False,
+             "value_tier": "standard",
+             "label": "ms_underdog_standard"},
+        ],
+
+        # ── Non-MS markets: visible but NOT playable ───────────────────
+        # All non-MS markets showed negative ROI in 50K-row backtest.
+        # Tiers exist so the model's read is surfaced (bet_summary),
+        # but criteria are strict enough that almost nothing passes BET.
+        # The user sees info; the system doesn't lose money on them.
+
+        "DC": [
+            {"min_odds": 1.15, "max_odds": 1.60, "min_edge": 0.02,
+             "max_edge": 0.12, "min_reliability": 0.55,
+             "max_model_gap": -0.02,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "dc_ultrastrict"},
+        ],
+
+        "OU25": [
+            {"min_odds": 1.60, "max_odds": 2.20, "min_edge": 0.02,
+             "max_edge": 0.10, "min_reliability": 0.55,
+             "max_model_gap": -0.03,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "ou25_ultrastrict"},
+        ],
+
+        "OU35": [
+            {"min_odds": 1.50, "max_odds": 2.50, "min_edge": 0.02,
+             "max_edge": 0.12, "min_reliability": 0.50,
+             "max_model_gap": -0.02,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "ou35_ultrastrict"},
+        ],
+
+        "BTTS": [
+            {"min_odds": 1.60, "max_odds": 2.10, "min_edge": 0.02,
+             "max_edge": 0.10, "min_reliability": 0.55,
+             "max_model_gap": -0.03,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "btts_ultrastrict"},
+        ],
+
+        "HT": [
+            {"min_odds": 2.00, "max_odds": 3.50, "min_edge": 0.02,
+             "max_edge": 0.12, "min_reliability": 0.50,
+             "max_model_gap": -0.02,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "ht_ultrastrict"},
+        ],
+
+        "OU15": [
+            {"min_odds": 1.30, "max_odds": 2.00, "min_edge": 0.02,
+             "max_edge": 0.12, "min_reliability": 0.50,
+             "max_model_gap": -0.02,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "ou15_ultrastrict"},
+        ],
+
+        "HTFT": [
+            {"min_odds": 4.00, "max_odds": 15.00, "min_edge": 0.03,
+             "max_edge": 0.15, "min_reliability": 0.45,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "htft_ultrastrict"},
+        ],
+
+        "OE": [
+            {"min_odds": 1.80, "max_odds": 2.10, "min_edge": 0.02,
+             "max_edge": 0.08, "min_reliability": 0.55,
+             "max_model_gap": -0.03,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "oe_ultrastrict"},
+        ],
+
+        "HT_OU05": [
+            {"min_odds": 1.30, "max_odds": 2.00, "min_edge": 0.02,
+             "max_edge": 0.12, "min_reliability": 0.50,
+             "max_model_gap": -0.02,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "ht_ou05_ultrastrict"},
+        ],
+
+        "HT_OU15": [
+            {"min_odds": 1.60, "max_odds": 3.00, "min_edge": 0.02,
+             "max_edge": 0.12, "min_reliability": 0.50,
+             "max_model_gap": -0.02,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "ht_ou15_ultrastrict"},
+        ],
+
+        "CARDS": [
+            {"min_odds": 1.60, "max_odds": 2.50, "min_edge": 0.02,
+             "max_edge": 0.10, "min_reliability": 0.50,
+             "max_model_gap": -0.02,
+             "require_v27_agree": False, "require_no_trap": True,
+             "label": "cards_ultrastrict"},
+        ],
     }
 
+    # Legacy flat envelope (backward compat for markets not in tiered system)
+    MARKET_OPTIMAL_FILTERS = {}
+
     MARKET_PRIORS = {
         "DC": 4.0,
         "OU15": 3.0,
@@ -197,7 +366,7 @@ class BettingBrain:
 
         rejected = [d for d in decisions if d.get("action") == "REJECT"]
         guarded["betting_brain"] = {
-            "version": "judge-v2-score-coherent",
+            "version": "judge-v31d-evidence-tiers",
             "decision": decision,
             "reason": decision_reason,
             "main_pick_key": main_key or None,
@@ -240,6 +409,21 @@ class BettingBrain:
         triple_is_value = bool((triple or {}).get("is_value"))
         consensus = str((package.get("v27_engine") or {}).get("consensus") or "").upper()
 
+        # V29c: Compute trap_market_flag early (needed by tier require_no_trap)
+        trap_market_flag = False
+        trap_market_gap = None
+        if isinstance(triple, dict):
+            _band_rate = self._safe_float(triple.get("band_rate"))
+            _implied = self._safe_float(triple.get("implied_prob"))
+            if (
+                _band_rate is not None
+                and _implied is not None
+                and band_sample >= self.MIN_BAND_SAMPLE
+                and (_implied - _band_rate) > self.TRAP_MARKET_GAP
+            ):
+                trap_market_flag = True
+                trap_market_gap = round(_implied - _band_rate, 4)
+
         positives: List[str] = []
         issues: List[str] = []
         vetoes: List[str] = []
@@ -256,7 +440,7 @@ class BettingBrain:
         if market in self.SNIPER_BLOCKED_MARKETS:
             is_value_sniper = False
         if is_value_sniper:
-            score += 20.0
+            score += 8.0  # V29b: reduced from 20, tiers do the real filtering
             positives.append("value_sniper_override")
 
         score += max(0.0, min(20.0, calibrated_conf * 0.22))
@@ -276,7 +460,7 @@ class BettingBrain:
         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:
+            if market in {"MS", "DC", "OU25", "BTTS"}:  # V29: hard veto, no sniper bypass
                 vetoes.append("low_reliability_league_hard_block")
         elif odds_rel < 0.45:
             score -= 12.0
@@ -305,38 +489,79 @@ class BettingBrain:
         # ev_edge < 0 = "model market'in altında olasılık veriyor" = vig'i
         # yiyemeyeceğimiz negative-EV bahis. Hard veto: oynama.
         # Sniper override hâlâ geçer (yüksek convicted alternatif pick'ler).
-        if ev_edge < 0.0 and not is_value_sniper:
+        # V29b: negative_ev_edge hard veto REMOVED — tier system handles
-            vetoes.append("negative_ev_edge")
+        # edge bounds per-market via min_edge. MS underdog tier allows
-            issues.append(f"ev_edge={ev_edge:.3f}_below_zero")
+        # ev >= -0.15, so a universal ev<0 veto would kill profitable bets.
+        if ev_edge < -0.20:  # Only veto truly extreme negative edge
+            vetoes.append("extreme_negative_ev_edge")
+            issues.append(f"ev_edge={ev_edge:.3f}_extreme_negative")
         # Trap edge: bizim diagnostic backtest'te ev_edge >= 0.20 olan tüm
         # bahisler kaybediyordu (n=10, hepsi -%25+ ROI). Model market'i bu
         # kadar yanlış buluyorsa muhtemelen modelin kendisinin yanlış olduğu
         # bir senaryo (eksik info, tuhaf maç, vs.) — oynama.
-        if ev_edge >= 0.20 and not is_value_sniper:
+        if ev_edge >= 0.30:  # V29b: raised from 0.20, tiers cap at 0.25
             vetoes.append("ev_edge_too_high_trap")
             issues.append(f"ev_edge={ev_edge:.3f}_trap_range")
 
         # ── MUTED MARKETS (grid search showed no profitable config) ──
-        if market in self.MUTED_MARKETS and not is_value_sniper:
+        if market in self.MUTED_MARKETS:  # V29: hard veto, no sniper bypass
             vetoes.append("market_muted_by_backtest")
             issues.append(f"market_{market}_muted")
 
-        # ── PER-MARKET OPTIMAL ENVELOPE (from grid search) ──
+        # ── V30: ODDS-TIERED ENVELOPE (from 7K backtest grid search) ──
-        envelope = self.MARKET_OPTIMAL_FILTERS.get(market)
+        # Each market has multiple odds zones with different filters.
-        if envelope and not is_value_sniper:
+        # If a bet doesn't fit ANY tier, it gets vetoed.
-            if ev_edge < envelope["min_edge"]:
+        # V30: added model_gap filtering — data shows model>market is
+        # inversely correlated with winning for BTTS/OU25.
+        tiers = self.MARKET_ODDS_TIERS.get(market, [])
+        # Also check legacy flat envelope for backward compat
+        legacy_env = self.MARKET_OPTIMAL_FILTERS.get(market)
+        tier_matched = False
+        tier_label = None
+        tier_value = None  # V31c: quality tier (premium/strong/standard)
+        if tiers:
+            for tier in tiers:
+                if not (tier["min_odds"] <= odds <= tier["max_odds"]):
+                    continue
+                if ev_edge < tier["min_edge"] or ev_edge > tier["max_edge"]:
+                    continue
+                if odds_rel < tier["min_reliability"]:
+                    continue
+                if tier.get("require_v27_agree") and consensus != "AGREE":
+                    continue
+                if tier.get("require_no_trap") and trap_market_flag:
+                    continue
+                # V30: model-market gap filter
+                if model_gap is not None:
+                    if "min_model_gap" in tier and model_gap < tier["min_model_gap"]:
+                        continue
+                    if "max_model_gap" in tier and model_gap > tier["max_model_gap"]:
+                        continue
+                tier_matched = True
+                tier_label = tier.get("label")
+                tier_value = tier.get("value_tier")  # V31c
+                break
+            if not tier_matched:
+                vetoes.append("outside_all_odds_tiers")
+                issues.append(f"no_profitable_tier_for_{market}_at_odds_{odds:.2f}")
+        elif legacy_env:
+            if ev_edge < legacy_env["min_edge"]:
                 vetoes.append("outside_envelope_edge_low")
-            if ev_edge > envelope["max_edge"]:
+            if ev_edge > legacy_env["max_edge"]:
                 vetoes.append("outside_envelope_edge_high")
-            if odds and odds < envelope["min_odds"]:
+            if odds and odds < legacy_env["min_odds"]:
                 vetoes.append("outside_envelope_odds_low")
-            if odds and odds > envelope["max_odds"]:
+            if odds and odds > legacy_env["max_odds"]:
                 vetoes.append("outside_envelope_odds_high")
-            if odds_rel < envelope["min_reliability"]:
+            if odds_rel < legacy_env["min_reliability"]:
                 vetoes.append("outside_envelope_reliability_low")
-            if envelope["require_v27_agree"] and consensus != "AGREE":
+            if legacy_env.get("require_v27_agree") and consensus != "AGREE":
                 vetoes.append("outside_envelope_v27_must_agree")
 
+        # V31d: a matched value tier is the validated profitable signal.
+        # It unlocks the value-betting regime (veto bypass + score floor).
+        is_value_tier = tier_value in self.VALUE_TIER_NAMES
+
         if divergence is not None:
             if divergence >= self.HARD_DIVERGENCE and not is_value_sniper:
                 score -= 42.0
@@ -348,20 +573,8 @@ class BettingBrain:
                 score += 11.0
                 positives.append("v25_v27_aligned")
 
-        # Trap market detection: market overpriced vs historical band hit rate
+        # Trap market score penalty (flag computed above, before tier check)
-        trap_market_flag = False
+        if trap_market_flag:
-        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")
 
@@ -465,6 +678,41 @@ class BettingBrain:
             if sniper_bypassed:
                 positives.append("sniper_bypassed_soft_vetoes")
 
+        # ── V31d: VALUE-TIER REGIME ──────────────────────────────────────
+        # A matched MS value tier is the validated profitable signal (60d:
+        # premium 6.0-7.5 → +32.7% ROI). Underdogs are bet on the odds
+        # premium, not on win-probability, so:
+        #   (1) waive the two favorite-confidence vetoes (genuine safety
+        #       vetoes — extreme_neg_ev, ev_too_high_trap, htft_reversal
+        #       _risk_high, v25_v27_hard_disagreement, low_reliability_hard
+        #       — are NOT waived and still reject);
+        #   (2) replace the favorite-confidence SCORE with a value floor so
+        #       premium can clear MIN_BET_SCORE while strong/standard stay
+        #       WATCH-level. All rich analysis output is untouched.
+        value_tier_bypassed: List[str] = []
+        if is_value_tier:
+            if vetoes:
+                remaining = []
+                for v in vetoes:
+                    if v in self.VALUE_TIER_BYPASSABLE_VETOES:
+                        value_tier_bypassed.append(v)
+                    else:
+                        remaining.append(v)
+                vetoes = remaining
+            if value_tier_bypassed:
+                positives.append("value_tier_bypassed_favorite_vetoes")
+            # Value-regime score: floor by tier quality + small +EV nudge.
+            value_score = self.VALUE_TIER_BASE_SCORE.get(tier_value, 50.0)
+            value_score += max(-5.0, min(10.0, ev_edge * 35.0))
+            if odds_rel >= 0.45:
+                value_score += 3.0
+            # Only premium is auto-staked; cap the rest below MIN_BET_SCORE
+            # so they surface as WATCH (visible analysis, not a staked bet).
+            if tier_value != "premium":
+                value_score = min(value_score, 60.0)
+            score = value_score
+            positives.append(f"value_tier_{tier_value}")
+
         score = max(0.0, min(100.0, score))
         action = "BET"
         if vetoes:
@@ -487,8 +735,12 @@ class BettingBrain:
             "issues": issues[:6],
             "vetoes": vetoes[:6],
             "sniper_bypassed": sniper_bypassed,
+            "value_tier_bypassed": value_tier_bypassed,  # V31d
+            "is_value_tier": is_value_tier,  # V31d
             "trap_market_flag": trap_market_flag,
             "trap_market_gap": trap_market_gap,
+            "tier_label": tier_label,
+            "value_tier": tier_value,  # V31c: premium/strong/standard
             "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,
@@ -501,10 +753,15 @@ class BettingBrain:
         if action != "BET":
             self._force_no_bet(row, f"betting_brain_{action.lower()}")
         else:
-            row["is_guaranteed"] = bool(score >= 82.0)
+            # V31d: value-tier underdogs are high-variance (~20% hit) — never
+            # label them "guaranteed" no matter how high the value score is.
+            row["is_guaranteed"] = bool(score >= 82.0) and not is_value_tier
             row["pick_reason"] = "betting_brain_approved"
             row["stake_units"] = self._brain_stake(row, score)
-            row["bet_grade"] = "A" if score >= 82.0 else "B"
+            # V31c: bet_grade now reflects value_tier so the UI can show
+            # the bettor which quality band a pick belongs to.
+            row["bet_grade"] = self._grade_from_tier(tier_value, score)
+            row["value_tier"] = tier_value
             row["playable"] = True
 
         self._append_reason(row, f"betting_brain_{action.lower()}_{round(score)}")
@@ -600,12 +857,14 @@ class BettingBrain:
 
     def _summary_item(self, row: Dict[str, Any]) -> Dict[str, Any]:
         reasons = list(row.get("decision_reasons") or row.get("reasons") or [])
+        brain = row.get("betting_brain") 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"),
+            "value_tier": row.get("value_tier") or brain.get("value_tier"),  # V31c
             "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),
@@ -615,9 +874,22 @@ class BettingBrain:
             "odds": row.get("odds", 0.0),
             "reasons": reasons[:6],
             "is_underdog_reference": bool(row.get("is_underdog_reference")),
-            "betting_brain": row.get("betting_brain"),
+            "betting_brain": brain,
         }
 
+    @staticmethod
+    def _grade_from_tier(value_tier: Optional[str], score: float) -> str:
+        """V31c: Map value_tier → bet grade so the UI surfaces the
+        quality band. Falls back to score-based grade for untiered picks.
+            premium  → A  (deep underdog, highest ROI, high variance)
+            strong   → B  (strong underdog)
+            standard → C  (volume zone, thin edge)
+        """
+        mapping = {"premium": "A", "strong": "B", "standard": "C"}
+        if value_tier in mapping:
+            return mapping[value_tier]
+        return "A" if score >= 82.0 else "B"
+
     @staticmethod
     def _candidate_sort_key(row: Dict[str, Any]) -> Tuple[float, float, float]:
         brain = row.get("betting_brain") or {}
@@ -657,6 +929,12 @@ class BettingBrain:
         odds = self._safe_float(row.get("odds"), 0.0) or 0.0
         if odds <= 1.0:
             return 0.0
+        # V31d: value-tier underdogs use a small FLAT stake (high variance),
+        # not the score-scaled favorite stake. score is high (70+) by design
+        # but that reflects validated tier EV, not win-probability.
+        brain = row.get("betting_brain") or {}
+        if brain.get("is_value_tier") or brain.get("value_tier") in self.VALUE_TIER_NAMES:
+            return self.VALUE_TIER_STAKE_UNITS
         cap = 2.0 if score >= 82.0 else 1.2
         if score < 78.0:
             cap = 0.8

ai-engine/services/orchestrator/market_board.py

@@ -58,6 +58,32 @@ from utils.league_reliability import load_league_reliability
 from config.config_loader import build_threshold_dict, get_threshold_default
 from models.calibration import get_calibrator
 
+# ── V30: Post-calibration trust factors ─────────────────────────────
+# Controls how much to trust isotonic calibrator vs raw model output.
+# trust=1.0 → use calibrator fully; trust=0.0 → bypass, use raw model.
+# Derived from calibrator_metrics.json analysis (mean_predicted vs mean_actual):
+#   MS calibrators: gap < 0.5%  → excellent, full trust
+#   BTTS:           gap = +14.4% → calibrator broken, bypass
+#   OU25:           gap = +5.3%  → over-inflates, mostly bypass
+#   OU35:           gap = +3.6%  → moderate inflation, dampen
+#   OU15:           gap = +1.5%  → slight, mostly trust
+#   HT:             mixed        → moderate trust
+#   DC/HT_FT:       < 30 samples → unreliable, bypass
+POST_CAL_TRUST: Dict[str, float] = {
+    "ms_home": 1.0,
+    "ms_draw": 1.0,
+    "ms_away": 1.0,
+    "btts":    0.0,
+    "ou25":    0.15,
+    "ou35":    0.30,
+    "ou15":    0.70,
+    "ht_home": 0.50,
+    "ht_draw": 0.30,
+    "ht_away": 0.50,
+    "dc":      0.0,
+    "ht_ft":   0.0,
+}
+
 
 class MarketBoardMixin:
     def _build_prediction_package(
@@ -1114,10 +1140,19 @@ class MarketBoardMixin:
         if cal_key and cal_key in calibrator.calibrators:
             cal_input = max(0.001, min(0.999, raw_conf / 100.0))
             cal_prob = calibrator.calibrate(cal_key, cal_input, odds_val=odd if odd > 1.0 else None)
+            # V30: Trust-based blending — some calibrators inflate probabilities.
+            # Blend isotonic output with raw model based on calibrator accuracy.
+            trust = POST_CAL_TRUST.get(cal_key, 0.5)
+            cal_prob = trust * cal_prob + (1.0 - trust) * cal_input
             calibrated_conf = max(1.0, min(99.0, cal_prob * 100.0))
         else:
-            multiplier = self.market_calibration.get(market, 0.85)
+            # V31b: Fallback for markets WITHOUT isotonic calibrator.
-            calibrated_conf = max(1.0, min(99.0, raw_conf * multiplier))
+            # Old approach used aggressive multipliers (0.58-0.85) causing
+            # massive deflation: HT_OU15 -40.5%, HT_OU05 -25.2%, OE -18.3%.
+            # New approach: mild damping (0.92) acknowledges slight model
+            # overconfidence without destroying probability signal.
+            # The tier system (V31b) is the real profitability gatekeeper.
+            calibrated_conf = max(1.0, min(99.0, raw_conf * 0.92))
         min_conf = self.market_min_conf.get(market, 55.0)
 
         implied_prob = (1.0 / odd) if odd > 1.0 else 0.0
@@ -1178,9 +1213,11 @@ class MarketBoardMixin:
         reasons: List[str] = []
         playable = True
         
-        # V34: Broadened value_sniper bypass — odds-aware model rarely shows 3% EV edge
+        # V29b: Permissive upstream — let betting_brain's tiered system do the real filtering.
-        # Allow high-confidence predictions OR modest positive EV to bypass secondary gates
+        # Old threshold (ev>=0.008 OR conf>=55) let everything through AND bypassed brain vetoes.
-        is_value_sniper = ev_edge >= 0.008 or calibrated_conf >= 55.0
+        # New approach: let most picks through market_board, but brain's MARKET_ODDS_TIERS
+        # + hard vetoes (neg EV, muted, low reliability) handle the intelligent filtering.
+        is_value_sniper = calibrated_conf >= 45.0
 
         if calibrated_conf < min_conf:
             if not is_value_sniper:
@@ -1283,11 +1320,49 @@ class MarketBoardMixin:
             stake_units = 0.25  # minimum stake (conservative)
             reasons.append("no_ev_edge_minimum_stake")
 
+        # ── V30: Birleşik Güven Skoru (BGS) ────────────────────────────
+        # A single, honest metric for users: quality-adjusted win probability.
+        # Combines calibrated probability with data quality signals.
+        # Correlation analysis: model_gap r=-0.12, trap negative, reliability weak positive.
+        bgs = calibrated_conf  # POST_CAL_TRUST corrected base
+        model_gap = prob - implied_prob if implied_prob > 0 else 0.0
+        # Penalty when model overestimates vs market (r=-0.12 correlation)
+        if model_gap > 0.05:
+            bgs -= 8.0
+        elif model_gap > 0.0:
+            bgs -= 3.0
+        # Trap market detection: implied prob significantly above historical band rate
+        is_trap_signal = False
+        if band_available and band_prob > 0 and implied_prob > 0:
+            is_trap_signal = (implied_prob - band_prob) > 0.10
+        if is_trap_signal:
+            bgs -= 7.0
+        # League reliability adjustment (±2)
+        bgs += (odds_rel - 0.50) * 4.0
+        # Band alignment
+        if band_available:
+            if bool(band_verdict.get("aligned")):
+                bgs += 2.0
+            else:
+                bgs -= 3.0
+        # BGS label for frontend
+        bgs = max(1.0, min(99.0, bgs))
+        if bgs >= 70:
+            bgs_label = "very_reliable"
+        elif bgs >= 55:
+            bgs_label = "reliable"
+        elif bgs >= 40:
+            bgs_label = "moderate"
+        else:
+            bgs_label = "low"
+
         out = dict(row)
         out.update(
             {
                 "raw_confidence": round(raw_conf, 1),
                 "calibrated_confidence": round(calibrated_conf, 1),
+                "unified_score": round(bgs, 1),
+                "unified_score_label": bgs_label,
                 "min_required_confidence": round(min_conf, 1),
                 "min_required_play_score": round(min_play_score, 1),
                 "min_required_edge": round(min_edge, 4),
@@ -1347,6 +1422,8 @@ class MarketBoardMixin:
             "pick": row.get("pick"),
             "raw_confidence": row.get("raw_confidence", row.get("confidence")),
             "calibrated_confidence": row.get("calibrated_confidence", row.get("confidence")),
+            "unified_score": row.get("unified_score", row.get("calibrated_confidence", 0.0)),
+            "unified_score_label": row.get("unified_score_label", "moderate"),
             "bet_grade": row.get("bet_grade", "PASS"),
             "playable": bool(row.get("playable")),
             "stake_units": float(row.get("stake_units", 0.0)),

src/modules/coupons/services/smart-coupon.service.ts

@@ -21,6 +21,8 @@ export interface PredictionPickRow {
   odds: number;
   raw_confidence: number;
   calibrated_confidence: number;
+  unified_score: number;
+  unified_score_label: 'very_reliable' | 'reliable' | 'moderate' | 'low';
   min_required_confidence: number;
   edge: number;
   play_score: number;
@@ -35,6 +37,8 @@ export interface PredictionBetSummaryRow {
   pick: string;
   raw_confidence: number;
   calibrated_confidence: number;
+  unified_score: number;
+  unified_score_label: 'very_reliable' | 'reliable' | 'moderate' | 'low';
   bet_grade: BetGrade;
   playable: boolean;
   stake_units: number;