Files
vyndr/specs/vyndr-roadmap.md
builtbykev 4e49ee0990 S9 (a1): slip reader — zero-API OCR
tesseract.js (self-hosted WASM, Apache-2.0) + pure per-book layout
parsers (DK/FD/MGM/Caesars) with per-field confidence and needs_review
honesty — the reader never guesses. POST /api/slips/parse (auth, free
1/day paid 10/day, 4MB cap) + Next proxy. Gated /slip page: upload or
paste, manual-correct UI, per-leg grades through the normal engine
(refusals render honestly), add-all to Parlay Lab, share card. Vision
model upgrade logged post-revenue. 2574 -> 2608 tests.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-11 19:32:17 -04:00

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VYNDR Master Roadmap — Generated 2026-07-10 (Session 56)

The foundation document. Derived from the Session-56 data audit (specs/propline-audit.md, verified against live MLB Stats / ESPN / Odds APIs). Every session after this references it. Update it as sessions ship.


Current coverage (verified 2026-07-10)

Sport Odds props Grading Features (l5/l20) Outcomes settled Status
MLB PropLine + odds-api real (statsapi) (S55, expanded S56) LIVE
WNBA odds-api/PropLine ⚠️ Python offline → thin PARTIAL
Soccer odds-api only (no PropLine) ⚠️ extractor only ⚠️ PARTIAL
NBA ⚠️ summer league only ⚠️ offline OFF-SEASON (Oct)
NFL ⚠️ preseason ⚠️ mapped, not e2e PRE-SEASON (Sep)
NHL off-season ⚠️ mapped DARK (Oct)

The core product is MLB. It is the only sport that is live end-to-end (odds → grade → real features → settled accuracy). Everything else is a build-out target.


Gap analysis (from the audit)

  1. Outcome settlement is MLB-only — WNBA/NBA/soccer grades never settle, so the accuracy record (the S55 trust engine) only reflects MLB. Highest-value gap. Needs the ESPN box-score settle path (espnStatsAdapter already parses the shape for season avgs).
  2. NBA/WNBA features depend on an offline Python nba_api service — l5/l20 are often empty; only season avgs survive (ESPN fallback). Needs an ESPN game-log feature path so intel populates without the Python service.
  3. Soccer has no PropLine source — odds-api backup only; no SPORT_KEYS entry. Soccer grading isn't wired into the snapshot pipeline end-to-end.
  4. NFL mapped but not graded end-to-end — markets normalize, but no feature/ outcome path. Wire before September.
  5. RBI silent-failure (rbis/rbi)FIXED S56.
  6. Under-requesting MLB marketsFIXED S56 (6 → 12 markets).
  7. No pipeline alerting / retry / missed-cronFIXED S56.
  8. No live calibration — the accuracy record exists but doesn't yet feed back into grade confidence (spec 2.3 from S55).
  9. batter_strikeouts (batter Ks) mapped but not feature/settle-wired.

Session plan (priority-ordered)

Session Focus Ships Scope
57 WNBA/NBA outcome settlement ESPN box-score settle path in outcomeService → accuracy for basketball; WNBA goes fully live M
58 ESPN game-log features l5/l10/l20 for NBA/WNBA without the Python service → real intel on basketball grade cards M
59 Soccer end-to-end soccer into the snapshot pipeline (odds-api source), feature extractor wired, ESPN settle L
60 Live calibration accuracy record feeds grade-confidence adjustment (under/over-confident tiers nudge); "Model health: Calibrated/Learning" indicator M
61 NFL pre-season prep NFL feature + settle path (ESPN NFL boxscore) so Week 1 (Sep) is live L
62 Prop breadth batter_strikeouts, triples, pitcher walks; NBA pra/turnovers requested; WNBA extra markets S
63 Accuracy depth per-archetype hit rates, per-stat hit rates, player-level record ("VYNDR on Judge: 12-5"); ledger UI M
64 NBA regular-season readiness verify NBA pipeline for the Oct tip-off; depth-chart/cascade live M

Scope key: S ≈ ½ session, M ≈ 1 session, L ≈ 12 sessions.

NOTE (Session 58): the numbered plan above predates the overhaul work order. The work order's phases (0 = kill the lies S57, 1 = truth infrastructure S58, 2 = slate UX, 3 = mobile, 2.5 below, 4 = scan/ parlay, 5 = records, 6 = landing/content) take sequencing priority; the table's items slot in where they don't conflict.

Phase 2.5 — Intraday line refresh + directional movement (LOGGED Session 58)

Authoritative scope from the Phase 1 GO prompt. Build after Phase 3 (mobile), before Phase 4. During slate hours (~noonmidnight ET):

  • Lightweight ODDS-ONLY refresh every 1530 min (no full re-grade run). Recompute the signed delta per graded prop RELATIVE TO THE GRADED SIDE — direction is the signal, not magnitude alone.
  • Moved WITH the grade (market chasing our number): PropRow badge STEAM ▲ +N. Good for the record; entry edge compressed. No re-grade.
  • Moved AGAINST the grade ≥ 1.0 (or odds-equivalent): auto re-grade THAT PROP ONLY.
    • Grade holds → badge VALUE ▲ better entry (better number, same read).
    • Grade drops → update the grade with revised_from_grade set (column already exists in ledger_entries) + a visible revision marker (original grade struck through — the ledger UI already renders it). Revisions are ALWAYS public — never silently regrade, per Ledger ethos.
  • All displayed lines remain real book values from the refresh — the refresh CAPTURES market numbers, never computes them.
  • After ship: drop SNAPSHOT_EXPECTED_INTERVAL to the refresh cadence — the HeartbeatBar SYNC badge (normal <1.5x · amber ≥1.5x · STALE ≥3x) goes genuinely live with zero further UI changes.

Phase 4.5 — WNBA settlement via ESPN box scores (DUE DATE, not "roadmap")

Logged Session 59. Pending-forever WNBA ledger rows are honest but become a credibility hole past ~2 weeks of accumulation. Due: within 2 weeks of day one of the public record (by ~Jul 24 2026). Scope: ESPN box-score settle path in outcomeService + ledgerService defaultGetPlayerStats (WNBA branch) so WNBA rows settle nightly like MLB. Build right after Phase 4 (scan/parlay polish).

Phase 5 addition — calibration by grade tier (logged Session 59)

Once settles mature: the MODEL tab shows A-tier hit% vs B-tier hit% SEPARATELY (same n≥20 rule PER TIER — a tier below threshold shows "building", never a %). The separation between tiers is the proof the grades mean something. ledger_entries.grade already carries the tier; this is an aggregate + UI change only.


Stat-type coverage target (fully built)

  • MLB (batters): hits, total_bases, home_runs, rbi, runs, doubles, triples, walks, stolen_bases, batter_strikeouts. (pitchers): strikeouts, earned_runs, hits_allowed, innings_pitched, outs, walks. (Bold-new this session: doubles, triples, outs, runs, walks requested + settleable.)
  • NBA/WNBA: points, rebounds, assists, threes, steals, blocks, turnovers, pra.
  • Soccer: goals, shots, shots_on_target, tackles, cards, corners, saves, passes, clean_sheet.
  • NFL: passing/rushing/receiving yards, receptions, pass/rush/rec TDs, anytime_td, interceptions, kicking_points.

Sport coverage target

  • MLB live now (settled accuracy).
  • WNBA → live after S57 (settlement) + S58 (features).
  • Soccer → live after S59.
  • NFL → ready for September (S61).
  • NBA → ready for October (S64).
  • NHL → ready for October (fast-follow once NBA path exists; shares ESPN pattern).

Slip Reader — vision-model upgrade (POST-REVENUE)

Session 9 (A1 board) shipped the zero-API Slip Reader: tesseract.js OCR (self-hosted WASM, free) + rigid per-book layout parsers (DK/FD/MGM/Caesars) with per-field confidence and needs_review honesty. That architecture is deliberately conservative — it reads clean screenshots of the four big books and refuses everything else.

The upgrade, when revenue funds it (zero-out-of-pocket rule): a vision model (Claude-class multimodal) replaces the OCR+layout-parser pair — one call reads ANY book, any theme, any crop, and returns structured legs with real confidence. Costs per-call money, so it is gated on paid tiers paying for themselves. The route contract (POST /api/slips/parse{ legs, needs_review, source: 'user_slip' }) is the stable interface; only the extraction engine behind it swaps. Keep the never-guess rule: model output below confidence threshold still nulls the field.

Operating invariants (do not regress)

  • Three stat_type whitelists stay in sync: routes/analyze.js, routes/scan.js, python/utils/validation.py.
  • A requested market MUST have a MARKET_MAP entry (else silent zero) AND, if MLB, a MLB_LOG_FIELD entry in BOTH featureCache and outcomeService (else no features / no settlement).
  • The accuracy pill stays HONEST — "LEARNING" below MIN_SAMPLE, never a faked %.
  • Pipeline alerts (ntfy) fire on success/failure/stale/overdue; the status probe (GET /api/internal/snapshot/status) exposes overdue.