main
7 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 builtbykev
|
13ca070096 |
Spec: metrics-engine north star + sourcing scope report (no code)
NORTH STAR (design philosophy, not built): VYNDR measures players by MODERN FUNCTION, not legacy label — the principle already under the archetype system, from Rashad Phillips' Basketball Position Metric. The rule: every proprietary metric is baselined against the player's functional ARCHETYPE's CURRENT-SEASON behavior, never the position's inherited standard. The edge is that the market often prices today's players against yesterday's baselines, so archetype-vs-position baseline disagreement is a repeatable mispricing. Generalizes across sports. Moat = proprietary metrics x current-game calibration x our private outcome data. Metrics ship as VALIDATED FAMILIES: hypothesis, flagged build, backtest, ship-or-delete with the negative result written down. Nothing is real until the harness proves it predicts better. SOURCING SCOPE (report, no code): MLB opponent strength IS derivable from statsapi, verified live — one free call returns all 30 teams' pitching splits (era/whip/avg/slg/ops/homeRuns/strikeOuts/HR9), which beats the ESPN field we were reaching for because it is STAT-SPECIFIC, exactly what opp_rank_stat wants. NBA/WNBA cannot use ESPN (its team endpoint carries only a team's own stats, no defensive rating or pace); options are stats.nba.com dashboards, deriving allowed-points from scoreboard finals we already fetch, or API-Sports. API-Sports is a fallback tier at best — 100/day will not survive per-team-per-day. ESPN stays last, always behind an adapter. Proposed the SOURCE-ADAPTER pattern: one interface per feed, config-driven primary+fallback per (sport x capability), normalized output so vendor quirks stay in adapters, fallback announced rather than silent, sources with zero callers deleted rather than left as corpses, and a health check that PAGES when a source returns empty or broken — where EMPTY IS A FAILURE. Tonight's crash (captured 0 / errored 15) and the months-null opp_rank_stat are both exactly what that check exists to catch. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
||
|
builtbykev
|
b742230d94 |
Phase 1: ship the backup cron as CODE + a manual regrade trigger
FOUNDATION-FIRST re-order, phase 1 (tooling + safety). BACKUP (highest-severity open item) — INSTALLED, not re-proven. src/backupScheduler.js runs scripts/backup-db.sh nightly from inside the API container, armed at boot in server.js. The container already has SUPABASE_DB_URL, pg_dump and the Supabase route, so deploy == installed: no host crontab, no Coolify click. Arming is deliberately opt-OUT (armed whenever SUPABASE_DB_URL exists; BACKUP_CRON=0 kills it) because the S62 design was opt-in and nobody ever opted in — the DB went unbacked every night for weeks. A failed run pages high-priority ntfy; silence is the danger with backups. Durability is the one part still needing a human: the container FS is ephemeral, so a dump dies on redeploy unless BACKUP_REMOTE (off-box rsync) or BACKUP_DIR (persistent volume) is set. The scheduler detects that and pages a WARNING at boot rather than letting an undurable backup read as "backed up". Runbook rewritten to lead with the code path. MANUAL REGRADE TRIGGER — scripts/run-snapshot.js, runnable via docker exec with no VYNDR_INTERNAL_KEY and no new HTTP surface. Runs the SAME snapshotService.runSnapshot the cron runs (including the team-stats refresh that powers opp_rank_stat), supports `all` and `--settle`, and prints the grade/confidence distribution plus p_win/ev_pct presence — which is the thing you actually want when verifying a grading change. ACCESS BLOCKER, logged honestly in specs/model-train.md: there is no VYNDR_INTERNAL_KEY in the local .env and SSH to the box times out from WSL2, so I can neither curl the internal endpoints (which already exist from S45) nor docker exec. The trigger is built and correct but only Kev can run it until a key or SSH access exists. This is the highest-leverage unblock for phases 2 and 3, which both need on-demand regrade+settle to verify anything. Also logged the standing cautions: CLV ledger stays private until backtest-proven; "self-improving model" is unsupported marketing until the loop closes; the engine is MLB/WNBA-calibrated and NFL/NBA/soccer need their own calibration before the hub grades them (scaling gate). Suite 277/3300 green. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
||
|
builtbykev
|
bf8ecb45ad |
Track U-deg pt2 (edge_pct scale) + the dispersion classifier as open items
Not new work — logging so neither gets lost. U-deg part 2: edge_pct is on a broken scale and it is the number users actually see. Live: edge_pct 100 on a single-digit-edge prop; ledger-wide 311/604 rows (51.5%) exceed the frontend's sane cap of 40, 39 exceed 100, worst 620. Mapped the consumers, and the split is the whole problem: 13 frontend files + deskShowcase/contentTemplate/parlayScan/tierGating read the BROKEN edge_pct, and ledgerService:199 persists it to the column of an append-only table right now. NOTHING on the frontend reads ev_pct; only heroPropService does. Noted that S-b (rank board on EV) is the real remedy and should be done as one piece with the scale fix, and that EDGE_BOARD_SANE_MAX is damage control that nulls half the board. Dispersion classifier: MIN_MEAN=4 is the honest stopgap; it leaves a +/-1.0 dead for MLB low-count stats. The scale-free fix is variance/mean vs the Poisson baseline of 1.0. Logged with its explicit validation bar — backtest harness first (still does not exist), replay settled outcomes, show no tier degradation, report before flipping, env-gate it. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
||
|
builtbykev
|
1a94ef5fcf |
Revive the dead probability layer + restore grade range ON MERIT
Folds re-sequenced steps 1+2 into one change (Kev's call): same bug
family — features wired to sources that return null.
THE PROBABILITY LAYER WAS DEAD IN PRODUCTION. p_win/ev_pct/kelly/
model_odds/value were absent on 0/8 live grades because
gameLogService.getGameLogs returns null for MLB by construction and
depends on the offline Python service for NBA/WNBA, so meta.gameLogs was
[] for every sport. This was the S46 bug in a second location — that fix
gave featureCache an MLB branch (why grades still worked) but never the
estimator. featureCache.getStatRows now supplies normalized rows
([{date,[statType]:v}], most-recent-first) for every sport, feeding the
estimator AND consistency AND game_count_in_7d from one fetch.
VERIFIED on real props: p_win 25/25 WNBA, 8/8 MLB (was 0).
GRADE RANGE, ON MERIT — never by rescaling (permanent founder ruling:
minting A's without new information is a relabelled B sold as an A and
corrupts an append-only ledger).
- refreshTeamStats wired into runSnapshot — it had ZERO production
callers, so opp_rank_stat was permanently null and a +/-1.0 factor
could never fire. Test-env no-op (opsNotify precedent).
- L20 made SYMMETRIC: both branches were delta +1.0, so the season
baseline could only ever ADD. No negative path was a structural reason
D was unreachable. New l20_contradicts_* carries -1.0.
- game_count_in_7d derived from real logged dates (heavy_workload_7d).
- NOT wired, deliberately, with reasons inline: teamId (no team_id
column; getFeatures reads it top-level; factor also needs a starter-id
list) and season_type (ESPN 2 = REGULAR season; threading it raw would
fire veteran_in_playoffs in July). Dead code dressed as a fix is the
thing we are removing, not adding.
CALIBRATION GUARD (found by verifying, not assuming): consistency CV is
NBA-tuned; for a Poisson-ish stat cv ~ 1/sqrt(mean), so any stat with
mean < 4 auto-classifies boom_bust. First verification run showed 8/8 MLB
props boom_bust — a blanket -1.0 that dropped the board to all-C. Floored
at CONSISTENCY_MIN_MEAN=4 -> 'unknown' below. Absent beats wrong. MLB
low-count stats therefore still get no consistency factor: honest, not
fixed. Scale-free index-of-dispersion classifier is the open follow-up.
CONFIDENCE IS NOT A PROBABILITY: payloads carry confidence_basis:
'grade_band'. Corrected mlb-grade-degradation.md — its "25/25
grade<->confidence agreement" is a TAUTOLOGY (confidence is derived FROM
the letter, so it would report 25/25 even if every grade were wrong), not
a validation. Removed dead mlbGrader.js (referenced only by its own test)
and the stale computeFeatures comment claiming a penalty that never ran.
VERIFICATION (scripts/verify-grade-range.js, real props/logs/engine):
WNBA 25 props B 68%->32%, C 32%->64%, D 0->1 (4%); 11-step spread went
from 2 steps to 5 (C/C+/B-/D). The D is earned: Angel Reese assists o2.5,
p_win 0.365. Nothing flooded — grades got HARDER. A did not emit locally
because opp_rank_stat needs the Redis cache only prod populates (local
ceiling +3.0 vs the +4.5 A needs); reachability is proven arithmetically
and locked in tests. Prod A-emission is the outstanding fingerprint.
MARKETING HOLD: "A-RATED" (AccuracyBadge, TopSignals) is unsupported
until that fingerprint. Confirmed honest fallbacks render today —
/api/ledger/accuracy has B and C buckets only, so the badge shows
"MODEL · 63% HIT" and TopSignals self-hides. Nothing fabricated ships.
Suite 276/3286 green, web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA
|
||
|
builtbykev
|
3fe840ab83 |
Diagnose grade collapse + find the DEAD probability layer (report only)
Kev's call: investigate the B/C grade collapse before building. Report only — no grade logic, thresholds, or engine code touched. FINDING 1 — the collapse is real, live and structural. Across 604 ledger rows and both sports the engine has emitted exactly TWO grades (B, C) and NINE confidence values (63/57/55/52/47/45/35/25/20), ceiling 63. Still true today on both sports. Confidence does NOT determine the letter: conf 45 -> B while 47 and 52 -> C (non-monotonic), so the surfaced confidence is not the quantity the letter came from. Edge scale still broken: 311/604 rows exceed the frontend's sane cap of 40, 39 exceed 100, worst 620. FINDING 2 (bigger) — the entire probability layer is DEAD in production. Live /api/snapshot/mlb: p_win, kelly, ev_pct, model_odds and value are absent on 0/8 grades, while alt_lines (Desk-gated) IS present 8/8 — proving nothing is tier-stripped, they are simply never computed. Root cause: gameLogService.pythonPath returns null for MLB by construction and the Python service is offline for NBA/WNBA, so meta.gameLogs is [] for every sport; estimateProbability returns p_over null; every field guarded by `if (pWin != null)` is skipped. This is the S46 bug in a second location — that fix added an MLB branch to featureCache.gameLogFeatures (which is why grades/projections still work) but never to the estimator path. Consequences: EV — the Model Train's whole ranking signal — has never been computed on a live prop. Hero v2 matches nothing and always falls through to the recent-read fallback (live /api/hero-prop returns is_recent:true). Quarter-Kelly, sold on the pricing page and listed BUILT in PROMISE-AUDIT.md, never runs. The value triplet is a duet live. Recommend re-sequencing: revive the probability layer BEFORE G-a and C-led (C-led would persist a column of nulls; G-a's EV_FLEX_THRESHOLD would gate on a permanently-null value — Kev's EV_FLEX_ENFORCE=0 ruling accidentally prevented an outage). featureCache:206-226 already has both adapter branches and is the template. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
||
|
builtbykev
|
669479097c |
Model Train G-b/C-cal: gate simulation + calibration report (docs only)
REPORT-FIRST per the arc order. G-a is HELD — the data changes the recommended dials. No engine code touched. Replayed against live ledger_entries (576 rows, 6 game days, 470 settled) because the "30 days of stored snapshots" does not exist: snapshot Redis keys are latest/previous only at 24h TTL, and no backtest harness exists anywhere in the repo. Findings that change the plan: - The -400 floor shipped this morning was the whole win: past -400 hit 80.3% against an 86.9% breakeven = -13.29u / -7.7% ROI on 173 settled. - Arc 2's incremental cut over the live gate is ~11 props in 6 days. The only material change is gating the flex band behind 2x EV. - The flex band (-161..-250) is our BEST band (+2.2% ROI, n=70) and the takeable band is flat (-0.3%, n=209) — the opposite of the assumption behind EDGE_FLEX_WALL. Recommend shipping the knob with enforcement OFF until EV is persisted and measured. - ev_pct/p_win are on NO ledger row, so the EV half of the gate cannot be replayed at all. C-led (persist EV) is now the highest-leverage item. - Confidence is monotonic but understates hit rate by ~20-25 points, and the entire public ledger contains only B and C grades — zero A/A+. That breaks hero v2 (isAB) and undermines "A-RATED" copy. Escalated. - L-a answered: alt_lines carry NO odds and the feed has no alternate markets. L-b is blocked on a data source, not engine work. - C-led needs no odds backfill (locked_odds 99.1% populated). - U-deg: the projection==0 leak is already closed (0 since 07-18). - C4 confirmed in data (359/376 MLB closes == the lock). Stays suppressed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
||
|
builtbykev
|
18bf3ecb51 |
Reconcile the record: STATE.md header + Model Train arc 1 spec (docs only)
Arc 1 ( |