report: model architecture recovery map — the live grade uses 0 of 3 specced layers
Archaeology only; nothing built, reconnected, or promoted. The champion is two DISCONNECTED estimates: the letter is engine1's additive factor index (zero references to p_win or any probability in engine1.js), and p_win is probabilityEstimator's frequencyOver + 5 heuristic layers, computed after and merely attached. The live grade path never calls the Python service. The Python three-layer engine is NOT DEPLOYED — no python/pip in the Dockerfile; app.js only health-checks it. So Layers 1-2 never shipped. Layer 3 is wired BACKWARDS: grade_thresholds.json maps PROBABILITY->GRADE and the live JS reads it in reverse to manufacture confidence from an already-chosen letter. Per-sport market-efficiency scaling is specced-absent. Consequence stated plainly: every metric audited to date is on the shadow model, not the specced engine, which has never been measured. Sport boundary TESTED not asserted: a new sport on the live path is a ~10-file core edit with four documented silent-failure modes. Per-sport records DO exist (sports.mlb n=526/62% vs pooled overall n=937/58%, each n>=20 gated), but /api/accuracy ignores ?sport= and the pooled overall would absorb a new sport. Park x weather confirmed challenger-only; xwOBA and leash absent. Recovery map is dependency-ordered with MLB as the reference module. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc
This commit is contained in:
@@ -397,3 +397,32 @@ deploy boundary; the anonymous live order is *1. Brionna Jones (edge 29.4) · 2.
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**PAID FIELDS: NONE**. A bogus bearer token also yields no paid fields (fail-closed proven in prod).
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**PAID FIELDS: NONE**. A bogus bearer token also yields no paid fields (fail-closed proven in prod).
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The board's proxy path now returns 10 props, so TOP GRADES **renders** instead of falling back to empty.
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The board's proxy path now returns 10 props, so TOP GRADES **renders** instead of falling back to empty.
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The rendered board is client-side → tagged for the Chrome audit, not faked.
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The rendered board is client-side → tagged for the Chrome audit, not faked.
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---
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# MODEL ARCHITECTURE RECOVERY MAP — 2026-07-30 (report-only) → `specs/model-architecture-recovery-map.md`
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**The live grade uses 0 of the 3 specced layers.** Every audited metric (calibration, CLV, skew audit,
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takeable floor, p_win→CLV r=0.375) is measured on the SHADOW model. Those findings stand — the shadow
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model served every real grade — but none are evidence about the specced engine, which has never been
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measured.
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| Specced component | State |
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|---|---|
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| Layer 1 Similarity (`python/utils/similarity.py`, 101 ln) | BUILT · NOT WIRED · **NOT DEPLOYED** |
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| Layer 2 Bayesian (`python/utils/bayesian.py`, 320 ln) | BUILT · NOT WIRED · **NOT DEPLOYED** — and its "sport-agnostic math / per-sport parameters" claim is TRUE of the built code |
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| Layer 3 grade scale (`grade_thresholds.json`) | BUILT · **WIRED BACKWARDS** — the table maps PROBABILITY→GRADE; the live JS reads it in reverse to manufacture `confidence` from an already-chosen letter |
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| Per-sport market-efficiency scaling | **SPECCED-BUT-ABSENT** |
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| Live champion (`engine1` + `probabilityEstimator`) | BUILT · WIRED — but the letter is a factor-index with **zero probability input**, and `p_win` is computed separately and never feeds it |
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**The Python engine is not in the deploy image at all** (no python/pip in `Dockerfile`; `app.js` only
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health-checks it). **The sport boundary is NOT clean on the live path** — adding a sport is a ~10-file
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core edit with four documented silent-failure modes, so "make a sport a module" is itself a prerequisite
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build. **Per-sport records DO exist** (`sports.mlb` n=526/62% vs pooled `overall` n=937/58%, each with
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its own n≥20 gate) — the rule to enforce is that a new sport renders `sports.{sport}`, never `overall`.
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**Park×weather is a CHALLENGER, not the champion** ("measured, never served"); xwOBA and bullpen-leash
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are absent entirely.
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Recovery is dependency-ordered in the map: decide the grading basis → pick a runtime (recommend porting
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Bayesian to Node) → wire Layer 2 → reconnect Layer 3 forward → Layer 1 → per-sport efficiency →
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challenger promotion → sport-as-module → NFL/CFB. MLB is specced as the reference module.
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@@ -399,6 +399,70 @@
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> (renders the band IN USER-VISIBLE COPY — "Takeable band −160 to +200", so a floor change is a copy
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> (renders the band IN USER-VISIBLE COPY — "Takeable band −160 to +200", so a floor change is a copy
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> change too).**
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> change too).**
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> ## 🗺️ MODEL ARCHITECTURE RECOVERY MAP 2026-07-30 (report-only) → **`specs/model-architecture-recovery-map.md`**
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> Archaeology of the specced three-layer engine vs the live grade path. Nothing built,
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> reconnected, promoted, or changed. **HEADLINE: the live grade uses 0 of the 3 specced layers —
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> every metric this project has audited (calibration, CLV, the skew audit, the takeable floor, the
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> champion's p_win→CLV r=0.375) is measured on the SHADOW model, not the specced engine.** Those
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> findings stand (the shadow model served every real grade) but none of them are evidence about the
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> specced architecture, which has never been measured.
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> **0.1 THE CHAMPION = two DISCONNECTED estimates.** The letter is `engine1.gradeProp`:
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> `idx = NEUTRAL_INDEX(3); for (f of factors) idx += f.delta` — and **`engine1.js` has ZERO
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> references to `p_win` or any probability** (grep count 0). The probability is
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> `probabilityEstimator` (`frequencyOver` + 5 heuristic layers, clamp [.10,.95]) computed AFTER and
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> merely attached. **p_win never feeds the letter; the letter never feeds p_win.** The live grade path
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> makes **no call to the Python service at all**.
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> **🔴 THE PYTHON ENGINE IS NOT DEPLOYED.** `Dockerfile` has no python/pip/requirements — the image is
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> Node-only; `app.js:92-95` only health-CHECKS it and reports `down`. Layers 1-2 are not "bypassed at
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> runtime", they have **never shipped**. `grade_thresholds.json` carries `"shadow_mode": true`.
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> **LAYER STATE:** L1 Similarity `python/utils/similarity.py` (101 ln, MIN_SIMILARITY .7, NBA+MLB
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> factor weights) **BUILT·NOT WIRED·NOT DEPLOYED** — the live grade skips straight to season/recent
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> averages. L2 Bayesian `python/utils/bayesian.py` (320 ln) **BUILT·NOT WIRED·NOT DEPLOYED** — and the
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> spec's "sport-agnostic math, per-sport parameters" claim is **TRUE of the built code**
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> (`BAYESIAN_WEIGHTS` prior/recent/context for 11 stat types across both sports, skewness,
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> data-sufficiency degradation, ±0.15 calibration clamp, a calibration disclaimer). L3
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> **BUILT·WIRED BACKWARDS** — `grade_thresholds.json` maps **PROBABILITY→GRADE** (A+ = p .85-1.00);
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> the live JS reads it in REVERSE, picking a letter from the factor index then looking up that
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> letter's band MIDPOINT to manufacture `confidence`. **That one artifact IS the drift, provable in a
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> single file.** Per-sport **market-efficiency scaling = SPECCED-BUT-ABSENT** (one global scale; the
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> spec's MLB .55 / NBA-stars .80 exists nowhere; the only `efficiency` in code is *usage*-efficiency).
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> **⚠️ The spec's "min 15 instances" is NOT in the code — CANNOT DETERMINE its origin** (code's
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> abstention is `similar_games_below: 3`; the only 15 is NBA `min_minutes_per_game`; the founding
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> three-layer spec text is not in the repo). **NOTE: STATE.md's own "Layer 1/2/3" references are the
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> Session 73-77 ARCHETYPE build train — a different numbering. Do not conflate them.**
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> **🔴 SPORT BOUNDARY — TESTED, NOT ASSERTED: it is NOT clean on the live path.** Adding a sport to the
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> Python spine costs nothing (the spine is dead); adding one to the LIVE path is a **CORE EDIT across
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> ~10+ files** — proplineAdapter.MARKETS · oddsNormalizer.MARKET_MAP (miss → silent ZERO) ·
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> oddsService.SPORT_KEYS/SPORT_MARKETS · **three stat_type whitelists** (analyze.js, scan.js,
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> validation.py — miss one → silent 400s) · featureCache log-field · outcomeService MLB_LOG_FIELD +
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> SPORTS · liveTrackingService LIVE_BOX_FIELD · a projection-emitting extractor (**without it every
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> prop REFUSES**) · emptyState/statFilters/teams · and a settled feed or the sport never settles.
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> Making "a sport" a real module is itself a prerequisite build.
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> **PER-SPORT RECORDS EXIST (2.7 satisfied):** `getModelAggregate` filters on `opts.sport`
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> (`ledgerService:809,819`) and `/api/accuracy` carries a real breakdown — live **`sports.mlb` n=526
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> 62%** alongside **`overall` n=937 58%**, each with its own `min_sample:20`. Two caveats: the pooled
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> `overall` would absorb a new sport's rows, and **`/api/accuracy` IGNORES the `?sport=` query param**
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> (identical payload for mlb vs wnba — consumers must read `sports.{sport}`).
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> **PARK×WEATHER CONFIRMED CHALLENGER, NOT CHAMPION** — attached in `snapshotService` (S77) onto
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> `env_*`/`challenger_*` columns; `ledgerService.js:253` says it outright ("measured, never served"),
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> and proj-v1.1, which consumes park/weather/platoon/arsenal, came back **NOT PROVEN** on 2026-07-29.
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> MLB champion inputs are ONLY l5/l20 + opp_rank + rest + usage. **xwOBA = 0 files (absent); leash = 0
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> (absent);** barrel/hard-hit/launch-angle/exit-velo/statcast/platoon all present but challenger-only.
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> **RECOVERY ORDER (dependency-sequenced, full detail in the map):** 0 decide the grading BASIS
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> (probability-vs-factor-index — a founder call; note that moving to probability grading legitimately
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> changes the letter distribution because it is NEW INFORMATION, categorically unlike rescaling
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> thresholds to mint A's) → 1 pick a runtime (**recommend PORTING `bayesian.py` to Node** rather than
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> resurrecting a service every live path already routes around) → 2 wire Layer 2 FIRST (its
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> prior/recent/context inputs are already available from `featureCache.getStatRows`, no new I/O) →
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> 3 reconnect Layer 3 FORWARD (grade FROM probability; kills the `confidence` tautology) → 4 Layer 1
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> Similarity (now feasible — S73-77 produce the context store it needs) → 5 per-sport efficiency
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> scaling → 6 challenger promotion on ledger evidence only → 7 make "a sport" a real module →
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> 8 NFL/CFB. **MLB = the REFERENCE MODULE** (deepest stack + a working settled feed + its own 526-row
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> record); the map lists the exact contract a new sport must declare.
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> **HONEST PER-SPORT LABELING: mechanism EXISTS, rule must be enforced** — every future sport renders
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> `sports.{sport}` and reads ACCRUING until its own n≥20 clears; never `overall`, which would silently
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> borrow MLB/WNBA credibility.
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- **Redirect EXISTS + WIRED:** `closingCapture.buildCaptureRows`→`closing_captures` (append-only,
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- **Redirect EXISTS + WIRED:** `closingCapture.buildCaptureRows`→`closing_captures` (append-only,
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provenance: captured_at/book/line_type/both-prices/missed_reason) via `intradayRefreshService:221`
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provenance: captured_at/book/line_type/both-prices/missed_reason) via `intradayRefreshService:221`
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+ internal endpoint; `ledgerService.attachClosingProb`→`closing_prob` (de-vigs both raw sides,
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+ internal endpoint; `ledgerService.attachClosingProb`→`closing_prob` (de-vigs both raw sides,
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@@ -0,0 +1,227 @@
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# VYNDR — MODEL ARCHITECTURE RECOVERY MAP
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Report-only archaeology, 2026-07-30. **Nothing was built, reconnected, promoted, or changed.**
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This is the map every subsequent reconnection order and every new-sport module builds against.
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---
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## HEADLINE — the live grade uses approximately NONE of the specced three-layer engine
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**Everything this project has audited — calibration, CLV, the over-side skew audit, the
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takeable floor, the champion's p_win→CLV r=0.375 — is measured on the SHADOW model, not on
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|
the specced engine.** That does not invalidate those findings (the shadow model is what
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actually served every grade), but it does mean none of them are evidence about the specced
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architecture, and the specced engine has never been measured at all.
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---
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## REVIEW ZERO
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### 0.1 What the live champion actually is — VERIFIED
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Two disconnected estimates, neither Bayesian nor similarity-based:
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**The GRADE LETTER** — `engine1.gradeProp` (`src/services/intelligence/engine1.js:170-174`):
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let idx = NEUTRAL_INDEX; // 3 === 'C'
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for (const f of factors) idx += f.delta;
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idx = clampIndex(Math.round(idx));
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return { grade: GRADE_SCALE[idx], confidence: GRADE_TO_CONFIDENCE[GRADE_SCALE[idx]] ?? 0.25 };
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An additive integer index over flat ±1.0/±0.5 factor deltas. **`engine1.js` contains ZERO
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references to `p_win` or any probability** (verified: grep count 0). The letter is computed
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with no probability input of any kind.
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**The PROBABILITY** — `probabilityEstimator.estimateProbability`: empirical
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`frequencyOver(values, line)` plus five heuristic adjustment layers (last-5 weighted 2x,
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opponent bump/fade, home/away ±1.5%, volatility pull toward 0.50), clamped [0.10, 0.95].
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Computed AFTER the grade and merely attached to the payload.
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**So: `p_win` never feeds the letter, and the letter never feeds `p_win`.** They are two
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independent estimates of the same event that are free to disagree — which is exactly why
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`confidence` (a band-midpoint lookup of the already-chosen letter) carries zero information.
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**It bypasses the three-layer engine completely.** Verified: the live grade path
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(`gradeSlateService` → `analyzeViaEngine1` → `engine1` / `featureCache`) contains **no call
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to the Python service at all**.
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### 0.2 Component inventory — every specced piece, tagged
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| Component | File | State |
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|---|---|---|
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| Layer 1 Similarity | `python/utils/similarity.py` (101 ln) | **BUILT · NOT WIRED · NOT DEPLOYED** |
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| Layer 2 Bayesian | `python/utils/bayesian.py` (320 ln) | **BUILT · NOT WIRED · NOT DEPLOYED** |
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| Layer 3 grade scale | `python/data/grade_thresholds.json` | **BUILT · WIRED BACKWARDS** (see §3) |
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| Market-efficiency scaling | — | **SPECCED-BUT-ABSENT** |
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| Shared archetypes | `python/utils/archetypes.py` | BUILT · superseded by JS `archetypeService` |
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| Sport blueprints | `python/blueprints/mlb.py`, `nba_context.py` | **BUILT · NOT WIRED · NOT DEPLOYED** |
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| Live grade engine | `intelligence/engine1.js` | **BUILT · WIRED · IS the champion** |
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| Live probability | `intelligence/probabilityEstimator.js` | **BUILT · WIRED · not fed into the grade** |
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|
|
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|
**🔴 THE PYTHON ENGINE IS NOT DEPLOYED AT ALL.** `Dockerfile` contains no `python`, `pip`, or
|
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|
`requirements` — the deploy image is Node-only. `app.js:92-95` merely health-CHECKS
|
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|
`PYTHON_SERVICE_URL` and reports `python: 'down'`. Layers 1 and 2 are not "bypassed at
|
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|
runtime"; they have never shipped. `grade_thresholds.json` even carries `"shadow_mode": true`.
|
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|
|
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|
---
|
||||||
|
|
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|
## PHASE 1 — THE THREE LAYERS, ACTUAL STATE
|
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|
|
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|
### Layer 1 — Similarity: BUILT, NOT WIRED
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|
`MIN_SIMILARITY = 0.7`; `SIMILARITY_FACTORS` carries NBA factors (opp DRtg .15, pace .12,
|
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|
rest .08, home/away .06, role match .15, teammate context .10) and MLB factors (pitcher
|
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|
handedness .12, park .10, opponent quality .12, weather .05, day/night .04, batting order .06).
|
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|
**The live grade never performs a similarity search — it goes straight to season/recent
|
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|
averages** (`projectionFor`: `l5_avg ?? l20_avg ?? {stat}_per_90 ?? xg_per_90`).
|
||||||
|
|
||||||
|
**⚠️ The spec's "min 15 instances" is NOT in the code — CANNOT DETERMINE its origin.** The
|
||||||
|
only abstention rule is `ABSTENTION_RULES.similar_games_below = 3` (`bayesian.py:46`); the
|
||||||
|
only `15` is `nba_player.min_minutes_per_game`. The founding three-layer spec text is not in
|
||||||
|
the repo, so the 15 could not be reconciled. (STATE.md's "Layer 1/2/3" references are the
|
||||||
|
Session 73-77 **archetype build train** — a DIFFERENT numbering. Do not conflate them.)
|
||||||
|
|
||||||
|
### Layer 2 — Bayesian: BUILT, NOT WIRED, and genuinely as specced
|
||||||
|
`bayesian.py` header: *"Shared by NBA and MLB. Per-stat-type weights. Similar game confidence
|
||||||
|
modifier. Skewness parameter. Data sufficiency smooth degradation curve."* It carries
|
||||||
|
`BAYESIAN_WEIGHTS` = prior/recent/context triples for **11 stat types across both sports**
|
||||||
|
(e.g. `strikeouts .40/.40/.20`, `hits .30/.45/.25`, `points .35/.45/.20`), plus data-sufficiency
|
||||||
|
minimums per player type, a calibration offset clamped to ±0.15, and a `CALIBRATION_DISCLAIMER`
|
||||||
|
("Model in calibration period... Track record begins building now").
|
||||||
|
|
||||||
|
**VERDICT: the sport-agnostic-math / per-sport-parameters claim is TRUE of the built code.**
|
||||||
|
It is simply not connected to anything. **The champion's `p_win` does NOT flow through it.**
|
||||||
|
|
||||||
|
### Layer 3 — Grading: the table exists and is WIRED BACKWARDS
|
||||||
|
`grade_thresholds.json` maps **PROBABILITY → GRADE** (`A+ 0.85-1.00`, `B 0.60-0.65`,
|
||||||
|
`F 0.00-0.29`). In the specced engine the grade IS a function of the probability.
|
||||||
|
|
||||||
|
**The live JS reads that same table in REVERSE**: it picks a letter from the factor index,
|
||||||
|
then looks up that letter's band MIDPOINT to manufacture `confidence`
|
||||||
|
(`engine1.js:31-36`, `confidence_basis: 'grade_band'`). A probability→grade table is being
|
||||||
|
used as a grade→fake-confidence lookup. **This single artifact is the drift, provable in
|
||||||
|
one file.**
|
||||||
|
|
||||||
|
**Market-efficiency scaling per sport: SPECCED-BUT-ABSENT.** One global 11-band scale, no
|
||||||
|
sport dimension anywhere. The spec's MLB 0.55 / NBA-stars 0.80 does not exist in code; the
|
||||||
|
only `efficiency` hits are *usage*-efficiency in `nba_context.py`/`redistribution.py`, an
|
||||||
|
unrelated concept.
|
||||||
|
|
||||||
|
### The gap, plainly
|
||||||
|
**The live grade uses 0 of 3 specced layers.** It uses: recent/season averages, an opponent
|
||||||
|
rank, rest, usage — summed as flat ±deltas onto a neutral 'C'. No similarity search, no
|
||||||
|
Bayesian inference, no probability-based grading, no per-sport calibration.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## PHASE 2 — THE SPORT-PLUGIN BOUNDARY
|
||||||
|
|
||||||
|
**Is the shared spine sport-agnostic?** In the *Python* spine, largely yes: `bayesian.py` is
|
||||||
|
sport-agnostic math parameterised per stat type. One mild leak: `similarity.py` co-mingles
|
||||||
|
NBA and MLB factor keys in a single `SIMILARITY_FACTORS` dict rather than per-sport blocks
|
||||||
|
(harmless — absent keys simply do not contribute — but it is not a clean plug point).
|
||||||
|
`blueprints/` is a general module directory (coaching, synergy, evolution, resolution…), **not
|
||||||
|
a sport-plugin registry**; `mlb.py` and `nba_context.py` happen to live there.
|
||||||
|
|
||||||
|
### 🔴 TESTED, NOT ASSERTED: what breaks if a sport is added today
|
||||||
|
Adding a sport to the *Python* spine costs nothing — because the spine is dead. Adding a
|
||||||
|
sport to the **LIVE** path is a **CORE EDIT across ~10+ files, not a module drop-in**:
|
||||||
|
|
||||||
|
1. `proplineAdapter.MARKETS` — request the markets
|
||||||
|
2. `oddsNormalizer.MARKET_MAP` — miss it and props silently normalize to ZERO
|
||||||
|
3. `oddsService.SPORT_KEYS` + `SPORT_MARKETS`
|
||||||
|
4. **THREE stat_type whitelists that must stay in sync** — `routes/analyze.js`,
|
||||||
|
`routes/scan.js`, `python/utils/validation.py` (miss one → silent 400s)
|
||||||
|
5. `featureCache` log-field map (features)
|
||||||
|
6. `outcomeService.MLB_LOG_FIELD` + `outcomeService.SPORTS` (settlement)
|
||||||
|
7. `liveTrackingService.LIVE_BOX_FIELD` (live tracking)
|
||||||
|
8. A feature extractor emitting a projection field — **without it every prop REFUSES**
|
||||||
|
9. `lib/emptyState.js`, `config/statFilters.js`, `web/src/lib/teams.js`
|
||||||
|
10. A settled-result feed, or the sport never settles (NBA/soccer still do not)
|
||||||
|
|
||||||
|
**VERDICT: the boundary is NOT clean on the live path.** A new sport today is a core edit
|
||||||
|
with at least four documented silent-failure modes. Making a sport a genuine module is
|
||||||
|
itself a prerequisite build, not a given.
|
||||||
|
|
||||||
|
### Per-sport records — EXIST (2.7 satisfied at the data layer)
|
||||||
|
`getModelAggregate` accepts `opts.sport` and filters both the settled and pending queries
|
||||||
|
(`ledgerService.js:809,819`). `/api/accuracy` returns a real per-sport breakdown — live:
|
||||||
|
`sports.mlb` = **n 526, 62%**, alongside `overall` = **n 937, 58%** — each carrying its own
|
||||||
|
`min_sample: 20` gate. **So a new sport CAN accrue an independent record with its own
|
||||||
|
accruing→proven state.**
|
||||||
|
|
||||||
|
**⚠️ Two caveats.** (a) A pooled `overall` exists alongside, and a new sport's rows WOULD
|
||||||
|
enter it — so any surface rendering `overall` would silently blend a new sport's record with
|
||||||
|
MLB/WNBA's. (b) `/api/accuracy` **ignores the `?sport=` query param** (identical payload for
|
||||||
|
`?sport=mlb` and `?sport=wnba`); consumers must read `sports.{sport}` from the full object.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## PHASE 3 — THE MODERN-DATA STACK
|
||||||
|
|
||||||
|
### MLB — what is wired, and to WHAT
|
||||||
|
| Input | Present | Feeds the CHAMPION? |
|
||||||
|
|---|---|---|
|
||||||
|
| l5 / l20 averages, opp_rank_stat, rest, usage | yes | **YES — this is the whole champion** |
|
||||||
|
| park factor (19 files), weather (18) | yes | **NO — challenger only** (`env_park_base`, `env_weather_mod`) |
|
||||||
|
| platoon / handedness (9 / 7 files) | yes | **NO — challenger only** |
|
||||||
|
| statcast (11 files): barrel%, hard-hit%, launch angle (4 each), exit velo (2) | yes | **NO — challenger only** |
|
||||||
|
| pitcher arsenal | yes | **NO — proj-v1.1 challenger only** |
|
||||||
|
| **xwOBA** | **0 files — ABSENT** | — |
|
||||||
|
| bullpen (1 file) / **leash (0 — ABSENT)** | partial | **NO** |
|
||||||
|
|
||||||
|
**Park×weather status CONFIRMED: CHALLENGER, NOT CHAMPION.** Attached in `snapshotService`
|
||||||
|
(Session 77) onto `env_*`/`challenger_*` ledger columns — `ledgerService.js:253` states it
|
||||||
|
outright: *"is what served the user; this is measured, never served."* And per the completion
|
||||||
|
matrix, proj-v1.1 (which consumes park/weather/platoon/arsenal) was tested 2026-07-29 and
|
||||||
|
came back **NOT PROVEN** — it lost to the champion. So the deep-data stack is accruing
|
||||||
|
measurement, not serving grades, and has not yet earned promotion.
|
||||||
|
|
||||||
|
### Incoming sports — input stacks to scope (NOT built)
|
||||||
|
- **NFL**: snap share, target share / route participation, team pace (plays per game),
|
||||||
|
scheme matchup (coverage/front vs role), role change on injury, red-zone usage,
|
||||||
|
weather (outdoor), Vegas team total as context.
|
||||||
|
- **CFB**: tempo, **soft-market efficiency (the actual edge thesis — books price CFB props
|
||||||
|
thinner)**, role concentration (few-star offenses), talent gradient / mismatch, pace,
|
||||||
|
garbage-time risk, limited-history handling (the Bayesian prior matters MOST here).
|
||||||
|
|
||||||
|
Both need a settled-result feed wired BEFORE launch, or they will grade and never settle —
|
||||||
|
the NBA/soccer failure mode already live in this repo.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## PHASE 4 — THE RECOVERY MAP, IN DEPENDENCY ORDER
|
||||||
|
|
||||||
|
| # | Step | Depends on | What it takes |
|
||||||
|
|---|---|---|---|
|
||||||
|
| **0** | **DECIDE THE GRADING BASIS** — probability-based (spec) vs factor-index (live). Everything below hangs on this. | — | A founder decision, not a build. Note: moving to probability-based grading legitimately CHANGES the letter distribution because it is NEW INFORMATION — that is categorically different from rescaling thresholds to mint A's (permanently forbidden). |
|
||||||
|
| **1** | **Choose a runtime for Layers 1-2: deploy Python, or PORT to Node.** | 0 | Python is absent from the Dockerfile and has been offline for the project's life; the live path is 100% Node. **Recommend PORTING** `bayesian.py` (320 ln) to Node over resurrecting a service every live path has learned to route around. |
|
||||||
|
| **2** | **Wire Layer 2 (Bayesian) first.** | 1 | Its inputs (prior / recent / context) are ALL already available from `featureCache.getStatRows` — no new I/O. Reachable soonest; delivers a probability the grade can actually be a function of. |
|
||||||
|
| **3** | **Reconnect Layer 3 forward** — grade FROM the probability using the existing table. | 2 | Deletes the backwards `confidence` lookup and makes `confidence` a real probability. Kills the tautology in one move. |
|
||||||
|
| **4** | **Wire Layer 1 (Similarity).** | 2, 3 | Needs a historical instance store with context — S73-77 park/weather/platoon now PRODUCE that, so it is newly feasible. Set the min-instance threshold explicitly (the spec's 15 is unreconciled; code says 3). |
|
||||||
|
| **5** | **Per-sport market-efficiency scaling.** | 3, 4 | Needs per-sport calibration accrual — the per-sport ledger record already exists to supply it. |
|
||||||
|
| **6** | **Promote park×weather / challengers** — only on ledger evidence. | 5 | Currently NOT PROVEN (proj-v1.1 lost to champion). Gated, not scheduled. |
|
||||||
|
| **7** | **Make "a sport" a real module** (collapse the ~10-file core edit into a registry). | 3 | Prerequisite for NFL/CFB shipping as modules rather than core surgery. |
|
||||||
|
| **8** | **NFL / CFB modules.** | 7 | Each needs its input stack + a settled-result feed before launch. |
|
||||||
|
|
||||||
|
### MLB as the REFERENCE MODULE
|
||||||
|
MLB is the right reference and is already the deepest: free unlimited `statsapi` (game logs,
|
||||||
|
season stats, probables, box scores), statcast row per batter, park base, weather modifier,
|
||||||
|
platoon split, pitcher arsenal, a settled-result feed that works, and 526 settled rows of its
|
||||||
|
own record. **A new sport module should be required to declare the same contract:** market
|
||||||
|
list → normalizer mapping → stat whitelist (all three) → feature extractor emitting a
|
||||||
|
projection → log-field map for features → settlement field map + a settled feed → live box
|
||||||
|
map → archetype set → thresholds/efficiency score → its own ledger record.
|
||||||
|
|
||||||
|
### Honest per-sport labeling — MECHANISM EXISTS, RULE MUST BE ENFORCED
|
||||||
|
The n≥20 per-sport gate and independent `sports.{sport}` records already exist. **The rule for
|
||||||
|
every future sport: launch with the engine fully built but the record rendered from
|
||||||
|
`sports.{sport}` and labeled ACCURING until that sport's own games clear its own gate — never
|
||||||
|
`overall`, which would silently borrow MLB/WNBA credibility.** The pooled `overall` is the
|
||||||
|
specific hazard to avoid on any new-sport surface.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## TAGS
|
||||||
|
VERIFIED: 0.1, 0.2, all of Phase 1, the sport-boundary break-test, per-sport records,
|
||||||
|
park×weather challenger status, MLB input inventory, Python-not-deployed.
|
||||||
|
CANNOT DETERMINE: the spec's "min 15 instances" (founding spec text absent from the repo;
|
||||||
|
code says 3). BLOCKED: none.
|
||||||
Reference in New Issue
Block a user