spec: Feature 4.1 — Model Learning Loop (Phase 4 backlog)

Closed-loop intelligence: settled bets feed back into grading weights.
- Grade accuracy tracking per stat type (A/B/C/D hit rates)
- Signal accuracy tracking (which deltas predict outcomes)
- Kill condition effectiveness (hit_rate_with vs without)
- Conservative weight adjustment (20% cap, 50-pick minimum)
- 4 new DB tables: grade_accuracy, signal_accuracy,
  kill_condition_accuracy, weight_history
- Desk-tier endpoints: /api/model/accuracy, /api/model/insights

Spec complete, ready to build when Phase 3 deployment is stable.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

ROADMAP.md

@@ -72,6 +72,19 @@ Depends on: Phase 2 complete
 - Founder code locks rate
 - Status: NOT STARTED
 
+## PHASE 4 — INTELLIGENCE (Backlog)
+
+### Feature 4.1 — Model Learning Loop (depends: 1.3 + 1.5)
+- Settled bets feed back into grading weight analysis
+- Track grade accuracy (A/B/C/D hit rates) per stat type
+- Track signal accuracy (which deltas actually predict outcomes)
+- Track kill condition effectiveness (do they prevent bad bets?)
+- Auto-adjust grading weights with conservative learning rate
+- Weight changes capped at 20% per cycle, min 50 picks per signal
+- GET /api/model/accuracy (Desk tier) — current model stats
+- GET /api/model/insights (Desk tier) — human-readable learnings
+- Status: SPEC COMPLETE — ready to build
+
 ## DEPENDENCY MAP
 ```
 1.1 (Odds API) ──┐