ecf78b911c96618e3ffb10943a7bbc872fa1d6d8
39 Commits
| Author | SHA1 | Message | Date | |
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 builtbykev
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929fd81940 |
Repair the champion: it was reading ten games, not a season
PHASE 0 — the defect is real past the peek. Against a FAIR point-in-time baseline (each player's rate over games strictly before that date, >=10 prior games, box scores back to 05-01), the served champion LOSES on all four stats, three of four CIs excluding zero: hits 0.00251 vs 0.00774 CI [-0.0074,-0.0011] TB 0.00393 vs 0.00619 CI [-0.0055,-0.0003] rbi 0.02481 vs 0.03133 CI [-0.0153,-0.0005] runs 0.00181 vs 0.00683 CI [-0.0114,+0.0008] PHASE 1 — the cause is the WINDOW, not the weights. estimateProbability builds its base rate as the frequency over every row it is handed, and featureCache.getStatRows handed it res.last10. So the "season rate" was a TEN-GAME rate, and 0.4 of the forecast was the last five OF THOSE TEN. The 0.40 recency weight costs resolution on all four stats (-0.00086, -0.00107, -0.00562, -0.00365). Nudges are mixed and small -- harmful on hits and rbi, marginally helpful on TB and runs -- so they are left alone. PHASE 2 — two lines, no new data, no extra API call, because fullLog was already fetched by the same adapter call that produced last10: getStatRows now reads fullLog, and RECENCY_WEIGHT goes 0.40 -> 0.20. hits 0.00251 -> 0.00817 (tripled; now above the fair baseline) TB 0.00393 -> 0.00734 (above baseline; vs old CI [0.0020,0.0067]) rbi 0.02481 -> 0.02727 (still below baseline, CI includes zero) runs 0.00181 -> 0.00436 (still below baseline, CI includes zero) Gate stated exactly: hits and TB now exceed the fair baseline on the point estimate; rbi and runs remain below but EVERY CI now includes zero, so no stat reliably loses to a frequency table. That is a tie on rbi/runs, not a win, and it is reported as one. Only TB's improvement over the old champion is CI-confirmed; the rest are directional. STALE-FIT GATE: CALIBRATION_DEPLOYED is now EMPTY. The low-param maps were fitted on the retired forecast and fromLedger cannot rescue them -- settled ledger rows still carry OLD p_win, so refitting today would refit the retired forecast. Nothing is served calibrated until dates settle under the repaired champion, and the favourite-longshot bias must be re-measured rather than assumed to survive. The shadow duel is void. PHASE 3 — the hits factor lift is NOT re-measured, and cannot be yet: it needs settled rows produced BY the repaired champion, which ships in this commit. Replaying would score the factors against a reconstruction rather than the served forecast. Deferred, explicitly. The factors remain wired and transmitting; only their lift is unquantified on the new baseline. PHASE 4 — standing flag, and it is large: EVERY factor verdict in this programme, every null and every THEATER, was measured against a champion worse than a frequency table. Signal added to noise reads as noise. Prior verdicts may deserve re-audit. Logged, not re-run. Re-queued not built: rbi lineup-slot / RISP opportunity through the two-part gate, now landing on a repaired champion. Serving-path change by design; the byte-identical invariant inverted and all four stats move. Nine frozen model modules verified unchanged. No Bonferroni slot -- resolution accounting on the champion's own knobs. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
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43f65d30cb |
Wire the three proven hits factors pre-grade: transmission proven, gain
inconclusive THE BUG THIS NEARLY SHIPPED AS A FINDING. The first audit reported 0 factors fired on all 1,140 rows. Not a result -- my paging helper ordered by `id`, and batter_spray, team_defense, platoon_splits and statcast_aggregates have composite primary keys with NO id column. The query errored, the loop broke on error, and four fully-populated tables read as empty. hitsFactorContext.js -- the PRODUCTION loader -- had the identical defect, so live wiring would have loaded nothing and served unadjusted while logging success. Third occurrence of this class in one session. Both loaders now order by a real column and THROW rather than degrade. The Phase 2 gate is what caught it: no resolution number was quoted until transmission was proved. PHASE 1 — pipeline is now base -> FACTORS -> CALIBRATE -> GRADE. Context built in snapshotService BEFORE gradeAndCacheSlate (was line 640+, grade at 454), threaded per prop, applied to p_over before p_win is set with p_win_prefactor and a full trace retained. Hits only. Coverage 859/1140 rows (75%): 474 with all three factors, 256 two, 129 one, 281 none. PHASE 2 — TRANSMISSION PROVEN, 12/12 sign-correct, 4/4 per factor, each applied IN ISOLATION. My first table compared each factor's expected sign against the COMPOSITE change and showed 3 false failures -- with three factors firing the net can oppose any single member; that was a flaw in the test, not the wiring. Two under-side rows confirm the flip is handled: a factor raising p(over) correctly lowers p_win. Switch hitters (Bailey, Bell, Rocchio) took no spray adjustment while their other factors fired normally -- the refusal is selective, not a blanket skip. PHASE 3/4 — both maps refit on the factor-adjusted forecast; the shadow-duel baseline is VOID and restarts, since it accumulated against a different forecast. Point-in-time, 765 held-out rows: reliability 0.00795 -> 0.00828 RESOLUTION 0.00229 -> 0.00345 (variance explained 0.93% -> 1.39%) Brier 0.25398 -> 0.25305 delta -0.00093 CI [-0.00225,+0.00002] Resolution rose 51% relative. The CI TOUCHES ZERO on 4 eval dates, so the composition does NOT earn a proven keep -- three isolated passes did not grant a composed pass. INCONCLUSIVE, reported as such. The gain is far below the sum of the isolated effects, which is expected: all three run through the same pitcher-batter confrontation and share signal. PHASE 5 — 1.39% of variance is still far below what band separation needs. The pivot was correct and incomplete: the plumbing defect was real and is fixed, three proven factors reach the served number for the first time, and transmission alone did not buy grade separation. Next arc is factor STRENGTH and BREADTH, not more plumbing. PHASE 6 — rbi anomaly logged, not chased: 14.51% variance explained vs hits 1.03%, on the stat we do not serve corrected and which has no proven factors. Either the biggest lever on the board or a mirage; it deserves its own order. The byte-identical invariant INVERTED for hits by design. All 13 frozen non-hits modules verified unchanged, probabilityEstimator included -- the factors ride outside it. No new Bonferroni slot; the composed OOS claim is reported with its CI and not claimed as a pass. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
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e872eff4ce |
Instrument the calibration duel forward; diagnose the resolution ceiling
— the proven factors were never wired in
PHASE 0 — two truths recorded. The swap is a BET, not an OOS win:
isotonic beat low-param on identical held-out rows (hits +0.0028, rbi
+0.0042, TB tied) and we serve low-param anyway on an untestable prior
about shared daily structure. At 19 dates nothing here can test it. And
the MIN_SLOPE catch is preserved as standing rationale: a near-zero or
negative slope collapses toward base-rate-for-everything, which LOWERS
Brier while destroying all resolution -- a metric win that guts the
product.
PHASE 1 — the duel is now falsifiable. Both corrections computed on every
hits/TB prop; p_win_lowparam served, p_win_isotonic_shadow logged in its
own try so it can never break serving. calibrationDuel.adjudicate encodes
the rule IN CODE before any forward date exists: >=10 forward dates and
isotonic winning with a date-block CI excluding zero => REFUTED, revert;
otherwise UPHELD; under 10 dates PENDING regardless of the numbers. A
date counts as forward only if NEITHER map was fitted on it -- otherwise
we would be scoring which map memorised better. Nothing swaps now.
PHASE 2 — the ceiling, quantified via Murphy decomposition:
stat reliability RESOLUTION uncertainty variance explained
hits 0.01353 0.00252 0.24532 1.03%
TB 0.01419 0.00442 0.24329 1.82%
rbi 0.00654 0.03268 0.22531 14.51%
runs 0.00788 0.00130 0.23182 0.56%
Calibration did exactly what theory says and nothing more: hits
reliability 0.01353 -> 0.00233 (-0.0112, 83% of the error removed) while
resolution moved -0.0002. Unexpected: rbi has 13x the resolution of hits
and is the one stat we do NOT serve corrected -- it needs calibration
least and discriminates most.
PHASE 2 DIAGNOSIS — NOT-TRANSMITTED, and not weak, ABSENT. Traced in code:
sprayDefense.js and platoonSeverity.js are required by NOTHING in src/,
only by analysis scripts and their own tests. The served p_win
(intelligence/probabilityEstimator.js:54) reads exactly four inputs --
game-log frequency, opp_rank_stat +/-0.03, home_away +/-0.015, and a cv
pull -- with zero occurrences of spray, platoon, hard-hit or
contact-profile. And snapshotService grades at line 454 while computing
challenger/context at 640+, so everything proven is computed DOWNSTREAM of
the grade it would inform. The three proven hits factors have never once
moved a served number.
That reframes the recent nulls: "calibrated p_win does not separate within
archetype" was never a statement about factors. The factors were not in
the forecast.
PHASE 3 — bands rebuilt on SERVED values (hits/TB low-param, rbi/runs
raw): 28 archetype slots across four stats, ZERO show lift. No longer an
open shrug -- it is the arithmetic of resolution 0.0013-0.0327 against
uncertainty ~0.23. A forecast explaining 1% of variance cannot produce
separating bands, and no correction to its numbers will change that.
HEADLINE: calibration is complete, delivered honest numbers on two stats
and zero grade separation, because the counter has no resolution -- and
the proven factors are not wired into the forecast at all. The second is
the reason for the first, and it is plumbing rather than a modelling wall.
Per-archetype grades need proven factors that actually reach p_win. Last
calibration order.
Serving unchanged from
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builtbykev
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74cf1ce974 |
Robust bias established; low-parameter correction replaces isotonic
PHASE 0 — sample-limit truth on record: on 19 dates BOTH stability
instruments are underpowered. LODO power 0.014-0.093 (best 0.337 across
every k tried); deploy CIs rest on 2-4 date clusters, where a
cluster-robust interval has ~1 df. This is the SAMPLE, not a fixable
instrument, and the gate-refinement loop stops here. Runs corrected: its
DATE-DRIVEN label was an artefact of the coin-flip ruler (2 reversals in
3 drops never cleared cutoff 2) -- it is an ordinary no-fittable-map
refusal.
PHASE 1 — the bias is ROBUST, tested model-free and map-free with a
date-block bootstrap. Pooled over-prediction rises monotonically -0.0076
/ +0.0428 / +0.0963 / +0.1589 / +0.2451 across deciles from 0.5 to 1.0,
sign stability 0.9946 over 17 date blocks, and 4 of 4 stats replicate
(bar was 3). Also visible: realized rate PLATEAUS at 0.65-0.68 from p=0.7
upward -- the 0.9+ bucket (0.6624) does no better than the 0.8-0.9 bucket
(0.6841). The model has no high-confidence reads, only high-confidence
numbers.
PHASE 3 — Platt, two parameters over the whole curve, shrunk toward
identity by fit-date count. Validated as a NEW estimator vs RAW with
date-block CIs:
hits a=0.406 shrink 0.565 0.2626 -> 0.2540 CI [-0.0112,-0.0069] DEPLOY
total_bases a=0.472 shrink 0.333 0.2490 -> 0.2429 CI [-0.0062,-0.0059] DEPLOY
rbi a=0.775 shrink 0.231 0.2011 -> 0.2007 CI [-0.0007, 0] REFUSE
runs a=-0.032 REFUSE
A GUARD THE FIRST RUN NEEDED: runs fitted a = -0.032. A non-positive
slope inverts the forecast rather than flattening it, and near zero the
curve collapses to a constant predicting the base rate for everything --
which LOWERS Brier while destroying all resolution. It would have scored
as a win while making the product worthless. MIN_SLOPE now refuses it by
name, with a test.
STATED PLAINLY: on the identical held-out rows isotonic BEAT the
low-param on hits (+0.0028) and rbi (+0.0042) and tied on TB. The swap is
a CAPACITY JUDGEMENT, not a measurement -- the window spans 2-4 date
blocks and that is exactly what a flexible map produces when it captures
structure shared by fit and eval. Labelled as a judgement.
PHASE 4 — hits and total_bases serve the correction, basis
direction_robust_magnitude_provisional (direction bootstrap-robust,
magnitude thin-sample and shrunk). rbi is WITHDRAWN to raw -- it was
deployed on isotonic at
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builtbykev
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ced40421ed |
Audit the LODO instrument: it cannot evaluate any stat, and both prior
FAILs were false
PHASE 0 — the gate at
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builtbykev
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1f40014256 |
Power-derive the LODO threshold: hits restored through the gate, rbi/runs
routed as date-driven PHASE 0 — threshold derived BLIND, before any stat was re-read. A reversal is informative only if that date's Brier delta is distinguishable from zero at its row count. Per-row Brier difference d_i = (pc-y)^2 - (p-y)^2, so SE(n) = SD(d)/sqrt(n) and n* = (SD(d)/|effect|)^2. Pooled across all four stats so no single stat's verdict could shape the threshold deciding it: pooled rows 3,417 | SD(d) 0.09816 | |effect| 0.01175 n* = (0.09816/0.01175)^2 = 69.8 -> 70 The hand-chosen 20 sat at 0.54 SE -- a coin flip. That is the defect this removes, and why the previous verdict moved with the number. Committed as calibrationRegistry.LODO_MIN_HELD_ROWS = 70 with LODO_THRESHOLD_BASIS; a test recomputes (SD/effect)^2 and asserts it equals the constant, so it cannot drift from its own justification. The derivation script prints no stat verdict, no date and no reversal. PHASE 1 — LODO at n*, applied cold: hits 5 informative drops, 0 reversals PASS total_bases 4 informative drops, 0 reversals PASS rbi reverses 2026-08-01 (n=99) FAIL runs reverses 08-01 (n=86), 08-05 (244) FAIL hits held-out deltas -0.0041/-0.0080/-0.0192/-0.0140/-0.0139 across 123-272 row dates, favourite sign holding on every testable drop. THIS IS THE INSTRUMENT FINALLY POWERED, NOT VINDICATION OF A PREDICTION -- the withdrawal at |
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builtbykev
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6ae11f1193 |
LODO-gated provisional calibration: total_bases deploys, hits withdrawn
PHASE 0 — I applied factorGate's >=40 date-cluster floor to a calibration layer without challenging the binding. That floor is a cluster-robust interval bar for a CAUSAL claim. Calibration makes no causal claim, has a bounded failure mode (it can only over- or under-shrink) and consumes no Bonferroni slot. Its real risk is that the correction is DATE-DRIVEN, and leave-one-date-out tests that directly -- a STRICTER bar, since a cluster count cannot detect a single day carrying the effect. The >=40 floor is retained, correctly scoped as the PROMOTION bar. PHASE 1 — both guards codified, 11 tests, green before Phase 2. Demonstrated on live data: raw population violated=true, mean_p 0.4962, both_sides_share 0.9763; after dedup violated=false, mean_p 0.6694. The null guard's test demonstrates the trap explicitly, since (null-1)**2 is 1 and (null-0)**2 is 0 so a Brier over nulls equals the win rate. PHASE 2 — LODO: hits n=1140 dates=17 2 reversals (07-22 n=20, 07-26 n=25) FAIL total_bases n=1050 dates=7 0 reversals, 0 sign flips PASS rbi n= 630 dates=5 1 reversal (08-01 n=99) FAIL runs n= 597 dates=5 2 reversals (08-01 n=86, 08-05 n=244) FAIL Threshold sensitivity reported because the verdict moves: total_bases passes at every held-size threshold, runs fails at every one, and hits fails ONLY when 20/25-row dates are admitted. I fixed MIN_HELD_ROWS=20 before seeing which stats passed and did not move it afterwards to preserve a deploy. Honest caveat: a per-date Brier delta on 20 rows has a standard error several times the effect, so the instrument is underpowered per-drop -- an argument for pre-registering a higher threshold, which is a Roundtable call, not one to make while holding the results. PHASE 3 — total_bases DEPLOY-PROVISIONAL, band [0.6-0.8]. hits, rbi and runs REFUSE. HITS WAS BEING SERVED CALIBRATED AND IS NOT ANY MORE. snapshotService hardcoded it since S91; it fails LODO, so it is out. A stat that cannot survive dropping one day was never calibrated, it was fitted to that day. The consequence is real -- hits props become unstackable for chain.chainAcross -- and it errs toward withdrawing a claim rather than preserving one on a fragile verdict. Deployment is now driven by a frozen, tested CALIBRATION_DEPLOYED set, not a hardcoded stat name. PHASE 4 — calibrationRegistry, 14 tests. Deploy needs BOTH gates, neither waivable. reverify auto-demotes on the first breach (CI stops excluding zero, or the favourite bias flips sign) and logs the breaking date. Promotion needs the original >=40 bar. A provisional deploy that cannot be taken away is just a deploy. PHASE 5 — TB bands rebuilt on calibrated values, 625 eval rows. The two-bar rule still bites: calibrated YES, proven NO, so they stay a base-rate read, now honestly numbered. Every archetype still collapses to one band -- calibrated p_win separates within archetype no better than raw. PHASE 6 logged only: the dead gradient is buried (hits~TB > runs > RBI, and RBI has the SMALLEST bias, so the skill-driven-gradient mechanism did not survive); the refused set is a map of missing inputs; a low-parameter calibrator is queued unbuilt. p_win never mutated; calibration rides as p_win_calibrated with calibration_status provisional. No Bonferroni slot consumed. Counter and frozen clusters byte-identical. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
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4d1803f6d7 |
Calibrate hits point-in-time: partial pass, and an honest ceiling of 0.667
Fitted the isotonic map on game_date < 2026-08-02 (n=589) and evaluated it on everything from that date forward (n=383). The map never saw the evaluation rows, which is the only thing that makes the result mean anything -- fitting and evaluating on the same rows always looks perfectly calibrated, because the map is reciting the answers it was built from. It works, on most of the distribution. Held-out after correction: 0.477 comes back 0.506, 0.587 comes back 0.580, 0.667 comes back 0.603 -- against raw errors of +0.191, +0.279 and +0.246 in the same bins. Ordering survived, and that was verified pairwise rather than assumed, because a broken map would silently destroy the one thing this model does well. Two findings matter more than the pass. First, the honest ceiling is 0.667. Once the numbers are truthful this model has no 80%-plus hit reads at all -- the top of its range was miscalibration, not confidence. A four-leg ticket at the ceiling is 0.198, where the raw numbers implied 0.686. The high-floor parlay is a two-thirds-per-leg proposition, and that is the number to say out loud. Second, calibration is certified BY BAND rather than by a blanket flag. Held-out error was -0.029 and +0.007 through the middle but -0.167 at the bottom and +0.063 at the top: the model is trustworthy over most of its mass and untrustworthy at both edges. A single true/false would either throw away the 72% that works or ship the edges that do not. Only a probability inside a certified band is marked stackable, and that flag is what chainAcross requires before it will compound anything. The certified band is 0.40 to 0.60, n=276. A methodological catch on the way: my first pass condition demanded honest bins at 0.70 and above -- but honest calibration REMOVES those bins, since the ceiling drops to 0.667. The gate would have failed the repair for succeeding. It now tests the highest remaining band instead of a fixed threshold. Wired forward with the same discipline: calibrationService fits strictly before today, splits by time rather than at random, and returns null on thin history so that "no calibrator" means nothing is stackable rather than "trust the raw numbers". p_win is never mutated -- the calibrated value rides beside it as p_win_calibrated, because a calibration map is a correction to a forecast, not a different forecast, and the counter stays byte-identical. 4,275 tests green (339 suites); web build exit 0. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
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08276c0880 |
Ingest lineup + baserunner context: the input RBI and runs always needed
RBI is power TIMES opportunity. The same swing drives in one run or three depending on who is on base, and a hitter batting with the bases empty cannot drive anyone in however hard he hits it. Every context-free model of RBI here has failed, and the failure kept being read as 'skill inputs don't work for RBI' when the truth was that we were modelling half the stat. Both halves are free from statsapi.mlb.com, which we already call for game logs, schedules and probable pitchers. No new provider, no key, no quota. RUNG 1, batting order: schedule?hydrate=lineups returns homePlayers and awayPlayers as ORDERED arrays of nine, and the order IS the batting order -- index 0 is the leadoff hitter. That single fact gives CATALYST its identity and supplies lineup-position context for every context-dependent stat. RUNG 2 turned out cheap, which the cheapest-first rule did not expect. It looked like it would need play-by-play reconstruction across a season; statsapi serves situational splits directly, so 'how often does this hitter bat with runners to drive in' is ONE call per player rather than one per game. Measured on a real hitter: 87 plate appearances with runners in scoring position producing 25 RBI, against 302 with the bases empty producing 17. That ratio is the opportunity half of the stat and it is the thing no amount of exit velocity can tell you. Both tables are dated in the primary key. statcast_aggregates was built upsert-in-place and that silently made every backtest leak the games it was predicting; a lineup is worse still, because it is a PRE-GAME fact that changes by the hour, so an in-place table would overwrite what we knew at grade time with what turned out to be true. Absent stays absent throughout: no lineup posted is an empty slate rather than a guessed order, a short lineup records fewer slots rather than padding to nine, and a hitter with no splits is null rather than a zero RISP share -- which would assert he never bats with runners on, a strong claim and usually a false one. Wired into the snapshot best-effort, so a context failure can never break the pipeline it rides in. The three pre-registered theories are now marked input-ready rather than input-blocked: DRIVER's power x runners-on and power x lineup-position, and CATALYST's speed x on-base x power-behind. They are sample-blocked from here, and the proofs run under native cumulative correction as sample accumulates -- ingesting is not proving. Counter and frozen clusters byte-identical. 4,250 tests green (338 suites); web build exit 0. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
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5de464330c |
URGENT: anchor the ledger price/book/takeable to TAKEABLE books
Ships before tonight's settle. Served path, champion, ranking and the
reference ruler are untouched.
TWO leaks, not one. The audit found ledgerService.indexProps; tracing the
lock price found that snapshotService.indexOdds has the SAME defect -- it
also indexed the full props list, so gradedAt.odds (the price a grade is
locked at) could itself be a DFS or exchange price. Fixing only the ledger
would have left the contamination flowing in through the lock.
Both now gate on TAKEABLE_BOOKS -- deliberately NOT MODEL_BOOKS. pinnacle
is model-eligible and correctly not takeable, so a MODEL gate would
re-break this the moment pinnacle's feed recovers. A test asserts pinnacle
cannot anchor a price.
TWO INDEXES, TWO ROLES, because the row needs two different things from a
prop and they have different correctness rules:
PRICE / BOOK / TAKEABLE -- takeable books only.
GAME FACTS (game_time, game_date, team/opponent) -- book-INDEPENDENT.
First pitch is first pitch whichever book listed it, so these still
come from any book. Gating them too would drop otherwise-valid rows
for no gain.
Collapsing those roles into one index is precisely the bug.
No takeable quote leaves the key ABSENT and the price null. An honest
missing price beats a price from a book you cannot bet -- and it keeps the
takeable flag from being computed off a DFS number, which is what made it
wrong on its own terms rather than merely mislabelled.
Gates: 4,111 tests / 330 suites green; next build exit 0.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc
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builtbykev
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8d052131c5 |
Add a bisect hook (?limit=) to the internal snapshot trigger
The cap raise 25 -> 500 made an induced snapshot 502 at 13.4s and the run did not complete in background either, while a 25-prop run had completed in 16.3s. That rules out a simple duration timeout and means the cause has to be measured, not guessed. ?limit= bounds one run so the regression can be bisected without a prod env change; omitted, the real DEFAULT_LIMIT applies. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc |
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builtbykev
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c7067c80c4 |
Persist lock-time multi-book lines to lock_lines (unblocks the staleness audit)
The over-side skew audit's confirming check — was our locked line stale-high vs consensus AT LOCK — was BLOCKED because multi-book lines at lock were never persisted (bookprices is Redis current-only). This persists them. - migration 033: lock_lines table (tracked + applied to prod). One row per (graded prop × book) with both odds + a lock timestamp. RLS enabled, NO policies -> service-role only (fence). UNIQUE key -> idempotent re-runs. - lockLineCapture.js: buildLockRows (pure, graded-props only, honest-absent single-book) + idempotent upsert persist. Built from the in-memory props at the LOCK moment (ts) -> no Redis re-read, no TTL race. - snapshotService: persist right after `enriched` (the lock moment; gradedAt uses the same ts). Best-effort + fenced. FENCE (measurement-only): lock_lines is read by NOTHING on the grade path (gradeSlateService, snapshot dedup/indexOdds, challengers, selector, ledger) — a grep test asserts it, and RLS locks it to the service role. Grade byte- identical proven: runSnapshot grades are identical with persist on/off (test). Volume ~1.5-3k rows/day (graded props x books x 5 snapshots); weeks retained, no pruning needed short-term. Does NOT retroactively fix the existing 62 rows — future accrual only; confirmation still needs weeks of settled rows. Full suite 3842 green, web build exit 0. No grade/locked_odds/outcome/served surface changed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VsztNChZ7vEvSR61AuMhD1 |
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builtbykev
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e81c9b8c51 |
Book Comparison Phase 1-3(backend): fenced per-book store + honest gated crown
Per-book prices existed only transiently (odds cache, ~1h, raw names, grade-path
input); every grade-path persistence point collapses to one book. The
/api/books feature was built+mounted but non-functional (fed FLAT rows to a
GROUPED comparator -> always empty).
Phase 1: bookPriceStore captures per-book prices from `props` BEFORE dedupeProps,
keyed nameKey|stat, into bookprices:{sport} (SNAP_TTL) in snapshotService. Fenced:
reads props, writes its own key, read by nothing on the grade path. Grade proven
byte-identical (test + no-grade-path-reference grep test).
Phase 2: scripts/measure-book-spread.js reports same-line best-vs-worst spread
(cents + implied-prob pts), per sport, never pooled. Pre-registered crown
threshold: median >=8c OR >=2pp. Runs post-deploy on real data.
Phase 3 (backend): compareProp is honest-absent (single-book/flat -> no crown)
and the crown is gated (BOOK_CROWN_ENABLED, default OFF until Phase 2 clears).
/api/books repointed to the snapshot-locked store (fallback odds cache),
nameKey-matched; `source` field is the deploy fingerprint.
HELD unchanged: dedupeProps, snapshot dedup, selector, grade, champion,
challengers, ranking, edge_pct/ev_pct. UI routing of BookComparison + crown
treatment deferred to post-measurement (gated on Phase 2). Full suite 3834 green,
web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VsztNChZ7vEvSR61AuMhD1
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builtbykev
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6386e737b9 |
proj-v1: absolute matchup projection challenger (distribution + full ladder)
A THIRD challenger (after arch-v1, contact-v1), MLB batting v1. Champion is market-relative P(stat>LINE); proj-v1 is ABSOLUTE — what the hitter will DO — emitted as a full distribution from which the WHOLE LADDER (P≥1,P≥2,P≥3) derives. Champion untouched; nothing claimed; the ledger decides per rung, per stat. - projection/distribution.js — Bayesian Gamma-Poisson → negative-binomial predictive. Admits over-dispersion; under-dispersion → Poisson approx (conservative, documented). Uncertainty scales with sample by construction (r=α): thin → WIDE (real mass on P≥1, honestly thin P≥3), thick → tight. NEVER abstains — width carries the honesty. - projection/matchupRead.js — the input the book doesn't use. HONEST FIDELITY: pitcher repertoire is rich (97% pitch-mix) but hitters have NO pitch-type performance, so TRUE repertoire-vs-profile is impossible today. This is the COARSE version (arsenal buckets fastball/sinker/breaking + whiff/hard-hit tendency × hitter whiff/chase/gb-fb/hard-hit) — beats generic L/R, derived + documented + TESTED two-sided. A hitter pitch-type feed unlocks the true form. - projectionChallenger.js — park RELATIVE to the player's own log exposure (isHome→own park, away→opp park; Phase B's raw-multiply bug solved), recency- weighted fit, per-factor breakdown (form/park/weather/platoon/matchup — show your work), full rung set + book-implied per rung. Combined non-form multiplier bounded. - Wired after contact-v1, own try, flag PROJ_V1_ENABLED, reusing arch-v1's already-computed park/weather/platoon (no duplicate env I/O). Own ledger columns (migration 032, applied to prod): distribution, ladder, point, line, our-P, book-implied, factor breakdown — measurable per rung/stat after settle. Phase 0 (prod-verified): venue join via isHome; NB family; uncertainty-as-width; coarse matchup honest fidelity; no lineup-slot (per-game rate, volume implicit). Sanity: thin-hot → wide (credible low rung, thin high rung); .300 hitter ≠ 3.0; matchup two-sided; champion byte-identical. proj-v1 suites 23/23; snapshot/ ledger/siblings 74 green. Forward-only, version-stamped, PROJ_V1_ENABLED kill. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj |
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builtbykev
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b6f12daa98 |
Contact-quality challenger (contact-v1) — nominate, don't swap
Phase A #2: the champion grade (l5/l20 result-based form) is a HYPOTHESIS that contact quality predicts better — unmeasured on our props, with zero settled p_win yet. Swapping l5/l20 (the champion's two heaviest ±1.0 factors) blind could degrade the core grade undetectably for weeks. So this NOMINATES contact quality as a second challenger, records what it WOULD project per prop, and lets the settled ledger decide. Nothing users see changes; the champion is untouched. - src/services/contactChallenger.js — pure, mirrors challengerProjection. Log- odds lean (capped, never a re-forecast) from SEASON contact quality vs league percentiles. Metric→prop mapping is the whole game: barrel_pct→HR, hard_hit_pct→TB/doubles, k_pct-INVERSE→hits (singles resolve on contact frequency, not barrels), k_pct→batter K. rbi/runs/walks ABSTAIN (opportunity/ discipline — no clean contact predictor). Honest-absent: thin (<50 PA)/absent/ unmapped/non-batter → p_win_contact NULL (no projection), never a fallback; "measured but unremarkable" is distinct (equals champion, delta 0). - Wired in snapshotService AFTER arch-v1, reusing the already-loaded statcast rows; its own try so a second challenger can't break the pipeline. Reads g.p_win, never writes it. - Retained SEPARATELY on the ledger (p_win_contact/contact_delta/ contact_adjustments/contact_version='contact-v1') so each challenger's marginal contribution is measured independently; ledger_entries.stat gives per-prop-type segmentation. Migration 031 (applied to prod). Phase 0 (prod-verified): statcast_aggregates is SEASON cumulative (not rolling), 48h stale now but season-scoped so ~8 PA/600 is negligible; 100% of graded hitters covered, 92% at ≥50 PA; no xBA/xwOBA in the feed. Forward-only, version-stamped (contact_version null on pre-nomination rows). Promotion is a LATER decision on settled evidence, per prop type — never asserted here. contactChallenger 14/14; snapshot/ledger/arch-v1 suites 80 green. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj |
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builtbykev
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7b25d97891 |
Wire the four dormant adjusters live — pure input-wiring
Verified state going in: parkBase, weatherMod and platoonSplits were called by nothing, and env_multiplier was non-null on zero rows across four orders. The adjusters were correct in isolation and starved of inputs. This gives them their inputs and changes none of their internal logic — the five adjuster files are byte-identical after this commit. PHASE 0 GATE — all three inputs are available at snapshot build, and the two join keys already existed. Venue: always, on every schedule game object. First-pitch: always, gameTime on the same object. Opposing-pitcher hand: present once the probable is declared, via the pitchers endpoint's pitcherId joined to statsapi handedness — 15 of 15 games declared this afternoon, though morning locks precede declaration and those props honest-absent on platoon, correctly. The batter-handedness join (statcast bats) and the MLBAM id were already on each grade from earlier sessions. environmentContext.js is the wiring, kept separate from the adjusters so they stay pure. It fetches once per snapshot: the schedule (team to venue, gameTime), probable pitchers (team to opposing pitcher id), one batched handedness call, one Open-Meteo forecast per home park, and batter splits per graded hitter. Park coordinates for 30 parks live here as public geometry, the same class as the dome list and centre-field bearings already in weatherMod, rather than inside an adjuster. Everything is best-effort: a missing venue drops park and weather, an undeclared pitcher drops platoon, and any fetch failure degrades that prop to archetype-only rather than breaking the pipeline the adjusters are measured inside. attachChallenger becomes async and takes a per-grade contextFor that returns the environment coefficient (park_base x weather_mod, composed) and the matchup (platoon). Point-in-time holds: the weather is a forecast for first pitch fetched now, and the split is the hitter's line entering the game — neither reads a settle-time value. Attribution is independent. env_multiplier, env_park_base, env_weather_mod and env_weather_state land in their own ledger columns, and challenger_adjustments keeps every axis — archetype, environment, matchup — as a separate entry, so when volume accrues each of the four can be measured for its own marginal contribution rather than as one blended delta. The combined move stays bounded, tested on the worst case: a Coors slugger with wind out and a favourable platoon, all at once, still moves under 12 percent, because every layer is capped and the total nudge is clamped. Stacking leans, it does not compound into a re-forecast. Non-MLB honest-absents entirely — park, weather and platoon are MLB-only today, so a WNBA prop gets no environment and no matchup. The champion is untouched throughout: p_win is read, never written, the served snapshot payload is still the enriched object, and a test confirms p_win passes through byte-for-byte while the challenger moves. Tests 3741 passed / 301 suites, web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj |
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builtbykev
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f2da9dd7e8 |
Layer 3 Step 2: archetype-aware CHALLENGER, measured not claimed
The champion (probabilityEstimator -> p_win) keeps serving and grading users, completely unchanged. The challenger is a second probability computed from the same inputs at the same instant, landing on the same ledger row so it joins to the same outcome and the same close. Identical conditions, one difference — the only clean A/B. NOTHING IS CLAIMED. Running a challenger is honest beta; asserting it is better before the settled ledger says so is not. Promotion stays a later decision gated on Brier and calibration over sufficient segmented volume. INTERPRETABLE, NOT A RE-ESTIMATION. The challenger is the champion's probability adjusted by the Layer-2 axes, applied in log-odds space so a nudge cannot push past 0 or 1 and means the same thing at p=0.5 as at p=0.9. Every deviation is attributable to a named axis and a signed nudge, stored as challenger_adjustments, and the total is capped at 0.45 log-odds — a lean on a real signal, never a re-forecast. Only mechanically obvious stat/axis relationships are mapped; a speculative mapping would be the same guessing this layer exists to replace. IDENTICAL WHERE THERE IS NO SIGNAL, by construction. An unremarkable player, a thin sample, an unmapped stat or a missing classification all return the champion's probability byte-for-byte with an empty adjustment list and a stated reason. The experiment therefore differs only where archetype-awareness could possibly help or hurt, with no dilution from rows the treatment never touched. Induced on real players. Judge home runs over: 0.42 -> 0.447, via BOMBER +0.22 and WHIFF RISK -0.11 — two real opposing signals netting positive. The same prop under mirrors it exactly to -0.027. Judge strikeouts: delta exactly 0, because WHIFF RISK and GRINDER cancel — an honest "no lean" with both signals still recorded. Skubal strikeouts over: 0.60 -> 0.702 via WHIFF, TRAPDOOR and CANNON all aligned; his hits-allowed goes the other way, 0.50 -> 0.392, because a strikeout arm makes hits less likely. Josh Bell and a 12-PA sample are untouched. Isolation is structural: adjust() is pure, the champion field is read and never written, the served snapshot payload is still the untouched champion object, and a challenger failure is caught so it can never break the pipeline it is measured inside. Statcast aggregates load once per snapshot run rather than per prop, so grade-time I/O stays at zero. Migration 034. Tests 3634 passed / 295 suites, web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj |
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builtbykev
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1bcdd8b305 |
Fix the ROOT: bind props to their real game, never date by the grade clock
Order 1.5 Phase 1. PropLine emits NO commence_time (grep-verified: zero hits in proplineAdapter), so ledgerService's `dateET(prop.game_time) || dateET(gradedTs)` always fell through to the GRADE timestamp — and a 01:00/03:00 UTC snapshot is 21:00/23:00 ET the PREVIOUS day. Tonight's props were filed under yesterday, settlement correctly found no game there, and Order 1's void logic turned that into 64 destroyed results. gameBinder.attachGameTimes() now matches every prop to a scheduled game by TEAMS across the plausible ET window (grade date, +1, -1) and attaches the GAME'S OWN time/date/id. It runs in snapshotService before grading and before the ledger write, so ledger, retention and settlement all inherit the correct date from one place. HARD CONTRACT: an unbindable prop returns NOTHING. ledgerService no longer has a grade-clock fallback — a row with no real game time is SKIPPED and counted, because a mis-dated row is fabricated data and the ledger holds real values or nothing. A slate that binds nothing pages. DOUBLEHEADERS are reported, never guessed: two games with the same teams on one date mark the binding `ambiguous` so settlement can decline rather than attribute a prop to the wrong game. (Real example already in the data: mlb:2026-07-11:MilwaukeeBrewers@PittsburghPirates(Game1).) Also fixes retention, which had the SAME bug from last night — I had dated model_snapshots rows with the snapshot clock. Rows now take the ET date of the bound game_time. Suite 282/3381 green, build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
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builtbykev
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5a5e37e32e |
Retention: fill enrichment fields + page on a zero-write slot
PHASE 1 — cron capture needed NO wiring. Verified in code: the scheduler tick calls runAll = snapshotService.runAllSnapshots, which loops runSnapshot per sport, which already carries the onGraded -> retention hook. The scheduled path and the manual path are the SAME function. The reason no cron cycle had been captured is simply that no slot has fired since retention deployed (slots are 14/19/22/1/3 UTC; retention landed ~02:55). Induced proof follows the deploy. PHASE 2 — archetype/team/opponent were permanently null because retention persisted at GRADE time, before enrichment attaches them. Retention still COLLECTS at grade time (the only moment the feature vector exists) but now PERSISTS after enrichment, merging those three fields via retentionService.mergeEnrichment. The merge is pure and fills ONLY those three fields — features and every model output are grade-time values and must never be rewritten by enrichment; a test asserts that. Unmatched rows (refusals not in the enriched slate) keep nulls rather than guesses. The empty-slate early return now persists too: a refusal-only slate is still history worth keeping. PHASE 3 — ZERO-WRITE ALARM. opsWatch.retentionZeroWriteAlarm pages at missed-snapshot severity when a slot GRADED props but retention wrote fewer rows than the slate (or nothing). runSnapshot now returns retentionRows so the scheduler can evaluate it. Retention is best-effort by design so it can never break a snapshot — which means a broken write is silent by construction. This is the counterweight. A slot that graded nothing never false-pages; an absent count reads as NOTHING and still pages, distinct from a reported 0. Suite 280/3349 green, build exit 0. Outcome stamping deliberately NOT implemented (depends on the settlement fix). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
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builtbykev
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d3ffa1b8c2 |
Retention: model_snapshots live + base64 SSH key support
RETENTION (Phase 2, priority zero). History starts compounding tonight. migration 025 model_snapshots — APPLIED to prod. Append-only, one row per graded prop PER SIDE PER CYCLE, with a unique index on (snapshot_id, player_key, stat, line, side) so a retried cycle cannot duplicate. RLS on, service-role writes only. What it captures that the ledger never did: - features jsonb — the model's INPUTS. Without these a backtest can only grade our own homework; with them any future model can be replayed against the exact conditions this one faced. - REFUSALS (refused + refusal_reason). The ledger drops them, so a gate refusing props that would have WON is invisible — unmeasurable lost edge. Captured via a new onGraded hook in gradeSlateService that fires with BOTH sides before any filtering. - grade_11, the pre-collapse grade. The 4-letter map throws away the entire live C-/C/C+/B- range. - model_version + code_sha on every row. ledger_entries mixes pre/post-fix grades with no marker and cannot be separated retroactively. - p_win / ev_pct / fair_odds / takeable / value — none of which any permanent store held. Wiring: analyzeViaEngine1 attaches _features/_grade_11 (underscore = internal); gradeSlateService fires onGraded then STRIPS them so they never reach a cache or API payload; snapshotService builds rows and persists best-effort. Retention reuses the LEDGER's dateET/gameIdFor helpers so rows share the ledger's natural key exactly — otherwise the settle pass could never join outcomes onto them. Rows are written BEFORE the empty- slate early return: a slate that refused everything is exactly the case worth recording. CONTRACT HELD: retention is injectable and every path is caught. persist() returns errors, never throws; a missing Supabase client is SKIPPED, not an error. A retention failure can never break a snapshot. BACKUP: backup-db.sh now accepts BACKUP_SSH_KEY as base64 (recommended — survives env-var newline mangling, which is how injected SSH keys usually break silently) OR raw PEM, detected by decoding and looking for the PEM header. Verified both forms detect correctly against a real generated key. Suite 279/3325 green, build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
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builtbykev
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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
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builtbykev
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66d52a9ce0 |
Item 1 — VERB LAW: one verb, READ (never SCAN), + a lint that enforces it
The product argued with itself: FAB/nav said "Scan", Free tier "5 scans", ticker "MLB slate scanned" — while the Ledger says "MY READS". Swept every user-visible surface to READ: - BottomTabBar FAB + Nav link: 'Scan' → 'Read' - Pricing free tier: '5 scans to try the model' → '5 reads …' - StatStrip: 'Awaiting next scan' → 'Awaiting next read' - Ticker badge + snapshotService event: tag 'SCAN' → 'READ', 'slate scanned' → 'slate read' (readSportOf parses BOTH old and new so cached ticker items dedupe cleanly through the rollover) - upgradePitch: 'You've scanned N parlays' / 'unlimited scans' → read/reads Internal untouched (not user-visible): /api/scan routes, scan_count column, scanning state, DemoScan/ScanIcon, scanlines CSS, the transitional SCAN color-map key. tests/unit/verbLaw.test.js is the enforcement: it fails on user-visible scan/scanned/scans copy across web/src + src/services (skips comments). Suite 270/3254 green, web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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4cd933d83e |
Job 1: per-sport snapshot cadence (config, not baseball's rhythm for all)
Every ACTIVE sport was graded at all five MLB slots (14/19/22/1/3 UTC). Sports post lines on different clocks, so that inheritance was wasteful both ways: WNBA props aren't posted at 14:00 UTC (10am ET) → that slot always graded 0 (the audit's "wnba:0"); soccer odds come from the 500/MONTH odds-api key, so five slots/day is a third of the budget for 1-2 matches. New src/config/sportCadence.js is the single source of truth (config-over- constants). Mapped from reality + quota headroom (PropLine 9k/day abundant, odds-api 500/mo scarce): mlb 14/19/22/1/3 intraday (full grid — games+props all day) nba 14/19/22/1/3 intraday (in-season fits; off-season self-skips empty) wnba 19/22/1 intraday (afternoon→evening ET; drops the 14/3 waste) soccer 14/19 NO intraday (WC live; 2 lean odds-api reads, key-protected) The scheduler still fires at HOURS_UTC and the missed-cron watchdog still references MLB (which runs every grid hour) — each slot now grades only sportsForHour(h), and only intradaySports() get the 20-min refresh. Every sport's hours are kept a subset of the firing grid (a boot-time guard + a test warn if that's ever violated). Retune a sport by editing one table row. Adaptive, not constant: near-zero when a sport is quiet, protecting the scarce odds-api quota from being drained by noon. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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7712f0a442 |
Heartbeat honesty: SYNC badge reads refreshed_at, not grade-lock updated_at
The "SIGNAL LIVE vs STALE 8h" contradiction was a field mismatch, not a dead pipeline. updated_at is the grade-LOCK time (advances only on a full snapshot, 5×/day — grades never change in-game, so it is intentionally stable). The SYNC badge measured the 20-min intraday cadence (expected_interval_s=1200) against that 5×/day field → structurally guaranteed STALE between slots even when intraday refreshes lines perfectly. - snapshotService: full snapshot now seeds refreshed_at at lock time - intradayRefresh already bumps refreshed_at every ~20 min (unchanged) - /api/snapshot/summary + GET /:sport now expose refreshed_at (was written to Redis but never serialized → no public liveness signal existed) - LiveLayer SYNC badge measures freshness from refreshed_at (fallback updated_at) Exposing refreshed_at also gives a public heartbeat probe: it advances every intraday slot, so pipeline liveness is verifiable without container logs. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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fceb3707b5 |
Wave 2B: reliable cross-sport headshots via ESPN athlete index
The NBA/WNBA espnId was captured only from espnStatsAdapter (the offline-
Python fallback), unreliable in prod. Add espnAthleteIndex — a pure,
defensive harvester that builds { nameKey -> {espnId, headshotHref} } from
the ESPN schedule->summary/boxscore/leaders/injuries/roster feeds the
pipeline already calls (free, bounded mapLimit, cached, MLB->{}).
snapshotService now fills any player the primary stats-resolve left without
an espnId from this index, and stores a DIRECT headshotHref as headshotUrl
on the enriched grade (the exact URL, never 404s on a constructed path).
Threaded headshotUrl through slateAdapter.buildPlayerStripsFromProps ->
GameCard -> StatStrip -> PlayerAvatar/getHeadshotUrl (direct href wins over
the constructed one). MLB's MLBAM path is untouched. Soccer resolves only
via a direct href; absent -> honest monogram (API_FOOTBALL_KEY remains the
reliable soccer path, unwired).
getGameSummary now also passes through ESPN `rosters` (pre-game lineups
carry id + headshot). Everything graceful: any miss -> absent -> monogram.
Tests: tests/unit/espnHeadshotIndex.test.js (11) — fixture->index, snapshot
merge fallback, direct-href-wins, soccer honest monogram, malformed/cyclic
parse never throws. Full suite 3080 green; web next build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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47ada9013c |
Wave 2A: real player headshots — sport-agnostic id threaded from ingestion
Threads a REAL athlete id from the snapshot's per-player stats resolve (zero
new I/O) → enriched grade → grades:{sport} → slate strip → PlayerAvatar. Real
photo where an id resolves; team-colored monogram (never a gray silhouette,
never a broken image) where it can't. Ids are never fabricated.
Ingestion (Addition 1):
- espnStatsAdapter.getSeasonAverages now RETURNS the resolved ESPN athlete id
(was discarded) as espnId; non-numeric uid degrades to null.
- playerIntelService surfaces MLBAM playerId (MLB) / ESPN espnId (NBA/WNBA).
- snapshotService captures both per player and stores them on the enriched
grade beside archetype/team (null when unresolved → monogram path).
Thread → component:
- slateAdapter.buildPlayerStripsFromProps carries playerId/espnId onto each
strip; StatStrip → PlayerAvatar (accepts both ids; getHeadshotUrl routes by
sport: MLB→mlbstatic, NBA/WNBA→a.espncdn).
- Silhouette surfaces rewired to PlayerAvatar (branded monogram on null):
scan search dropdown (guarded MLBAM p.id) + tonight chips, SearchModal,
HotListPanel, GradeResultCard header. Scan grade card feeds the picked
MLBAM id through gradeAdapter.
- playerHeadshot pure URL logic extracted to CommonJS playerHeadshotUrl.js
(unit-testable; the .ts re-exports it). nfl/nhl added to ESPN_SPORT_PATH.
Tests: tests/unit/headshotThread.test.js (per-league URL + id thread + monogram
null path) + extended snapshotService/espnStatsAdapter suites. Full suite
241 suites / 2915 green; next build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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b6787af191 |
Wave 1: kill three trust bugs (billing renewal + namesake collision + Desk copy)
FIX 1 — Honest billing renewal render. VYNDR tiers are monthly, so a `subscription_end` far in the future (the manually-seeded "RENEWS 6/9/2036" founder row) is a comped/lifetime/seed value, not a renewal. New web/src/lib/billingDisplay.js `classifyRenewal()` → date | none | lapsed | unknown (strict Date.parse guard, MONTHLY_RENEWAL_MAX_DAYS=60). Profile page renders the classified label for both the "Renews" stat and the cancel-scheduled "Access ends" line — no raw far-future date. No DB row mutated. FIX 2 — MLB namesake collision (James Wood → "Chicago Cubs"). searchPlayer now collects ALL exact-nameKey matches instead of first-`.find`; a ≥2 collision resolves ONLY via a confident teamHint (the prop's game participants, matched against the cached /teams list with ESPN↔statsapi abbr reconciliation), else refuses (null) — never guesses. The hint threads getPlayerStats → resolvePlayerStats → snapshotService (built from each prop's home/away team). Join invariant: a single-exact player whose team isn't in the hinted game has its team DROPPED (null), so streaks/rosterlogs never tag a foreign team. Full teamHint recovery shipped (not just the refuse fallback). FIX 3 — DeskShowcase headline "A $1M terminal." → deadpan value-showing copy "Every grade, every alt line, live." Prices ($44.99 / $34.99) unchanged. Tests: billingDisplay.test.js (7), mlbNamesakeResolve.test.js (12, disambiguation + join invariant + pure helpers), ds5PricingStates updated to assert the new headline and no "$1M". Full suite green (237 suites / 2863 tests); web `next build` exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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4d2b27d1d0 |
Session 61 (pre-merge): live-verification fixes from day-one rows
1. [settlement] armed boot line — settlement WAS scheduled (the tick runs settleAllOutcomes + settleAllLedgers FIRST at every snapshot slot, src/snapshotScheduler.js) but was invisible at boot. Now verifiable from logs forever. 2. Null locked_odds root cause (4 day-one rows, all betmgm SB unders): genuinely absent in the DB — the first-seen book row carried no juice for that side. indexOdds/indexProps now PREFER a book row with both sides priced (still a real row, never synthesized). Display was correct: absent beats wrong. 3. Verified retry-safe: 'Odds data temporarily unavailable' is the odds-api quota gate (oddsService:423) — thrown BEFORE any spend or cache write; runSnapshot errors write nothing; the next cron retries fresh with PropLine first. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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builtbykev
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2c9cbca7bd |
Session B (night2): streaks/hot-list engine — producer + form heat + THE LENS
RESURRECT verdict: the Session-23 engine existed, was pure, tested, and
mounted — it starved because every producer was external and unarmed
(tank01-prefetch via n8n, offline Python flow). The snapshot pipeline is
now the producer: each run merges slate players' real game logs (already
fetched for archetypes — zero extra calls) into rosterlogs:{sport}.
- computeFormHeat: hot hitters (7d AVG), hot sluggers (SLG), hot shooters
(FG%) — correct sum/sum rate math, season baseline else prior stretch,
min-sample refusals, never extrapolated.
- streakLens: no raw streak renders alone — built-vs opponents, tonight's
matchup + opposing SP w/ ERA, step-up/step-down difficulty, one-line
read. Absent context = say less, never invent.
- /api/streaks/:sport: heat merged into the feed, lens applied from cached
schedule + pitchers (time-bounded, can never hang the route), snapshot
grade letters joined.
- ACCEPTANCE (live statsapi, real 2026 logs): 32 rows found — Turang
12-gm on-base, Reynolds 8-gm on-base, Pratt 7-gm on-base + 3-gm
multi-hit, Meidroth 5-gm on-base, Cortes 5-gm on-base.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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builtbykev
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4ddfb84232 |
Session A (night2): AUTONOMY.md + SNAP_TTL 6h→24h P0 fix
The overnight cron gap (03→14 UTC) is 11h; a 6h snapshot TTL meant the morning settle pass read an expired snapshot and the accuracy loop silently settled nothing. Ledger (Postgres) settle was immune. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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builtbykev
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d10bb4cce2 |
Session 59: Addendum + work-order 1.6 + Phase 2 + Phase 3 (2352 tests)
Overnight sprint for the Saturday 10 AM ET deploy gate — day one of the
public ledger record locks against freshly posted lines.
Task A — ledger team/opponent (migration 020, applied at 0 rows):
populated in both write paths from the real feed; opponent only when the
player's team matches a game participant (never guessed). Roadmap: Phase
4.5 WNBA ESPN-boxscore settlement (due ~Jul 24) + Phase 5 per-tier
calibration logged.
Task B — work-order 1.6 CLOSED (canonical player keys):
- searchPlayer resolves via nameKey; the old matcher deleted accents
("Sanchez" with acute -> "snchez") and substring-guessed onto the WRONG
player (the mismatched last-10 bug). Ambiguous -> null, never guess.
- Slate JOIN INVARIANT: a graded prop whose player's real team isn't in
the game is dropped (TB player can't render under MIL@PIT) — locked by
tests that fail the suite on regression.
- grades:{sport} TTL 2h -> 6h (expired between 5h cron gaps — the real
cause of /team "No active props" for slate players).
Task C — Phase 2 slate UX: tabs are THE filter (URL ?sport=, deep-linkable,
duplicate legacy tablist removed); cards cap at 6 graded props sorted
A+->F with ALL N READS in-place expander; waiting states show the real
next pipeline run ("Grades post ~6:00 PM ET").
Task D — Phase 3 mobile P0: root cause of vanished 390px nav was HIDE_ON
including '/' (landing had zero navigation) — fixed; html/body overflow-x
contained; GAME LINES collapses to best-line summary + "N BOOKS" expander
below 640px; venue drops before time/pitchers ever truncate.
Live verification: raw ESPN today STILL returns the Jun 13 NYK@SA Finals
game without a date pin; the pinned fetch returns 0 games, 0 off-date.
Backend 2327 -> 2352 tests (202 suites), web build exit 0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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builtbykev
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d296e40cb6 |
Session 58: Phase 1 — Truth Infrastructure (2327 tests)
ledger_entries is live (migration 019 applied to prod, RLS + NULLS NOT
DISTINCT dedupe verified against the real database). Every grade now
persists, settles against the real result, and carries closing-line value.
- ledgerService: pipeline pre-grade upserts (public model record, user_id
null, idempotent), closing capture on every snapshot (last write before
game start = the close), settlement with SIGNED CLV (over = locked -
closing; beat/faded/flat), 30d model aggregate with the hard n>=20 rule.
- Write paths: snapshotService -> ledger (priority path); Next /api/scan ->
ledger for authenticated users only (anon never touches the public
record). Refused reads write nothing and don't burn a scan.
- Honest refusal (work-order 1.5): no projection => insufficient_data,
grade null, "INSUFFICIENT DATA - no read" UI. The web gradeAdapter no
longer displays the line as the model projection (the audit's
model==line / +0% edge degenerate); the card renders absent states.
projectionFor is sport-aware (l5 -> l20 -> {stat}_per_90 -> xG).
- /ledger: MY READS | MODEL tabs; model header shows hit% + beat-close%
only at n>=20, else RECORD BUILDING + live pending count. ModelRecord
deferred-render strip on landing + player hero. CLV + outcome chips,
revised_from_grade strikethrough (Phase 2.5 ready).
- SYNC (Task 5): thresholds vs SNAPSHOT_EXPECTED_INTERVAL (normal <1.5x,
amber >=1.5x, STALE red >=3x) via /api/snapshot/summary.
- Phase 2.5 logged in specs/vyndr-roadmap.md (build after Phase 3).
- Data-semantics hardening: strict null-safe numeric parsing everywhere a
market value is handled (Number(null)===0 would have fabricated lines).
Backend 2309 -> 2327 tests (201 suites), web build exit 0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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builtbykev
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2ae8a5697e |
Session 56: Full audit — PropLine + boxscore + pipeline + sport coverage (2289 tests)
Research (verified against live MLB Stats / ESPN / The Odds APIs): - specs/propline-audit.md — every stat_type mapped against our 4-layer pipeline; real MLB boxscore fields; sport coverage status; pipeline gap analysis. - specs/vyndr-roadmap.md — priority-ordered Sessions 57–64 + coverage targets. - scripts/propline-audit.js + specs/audit-data/ (raw capture). Headline bug: oddsNormalizer mapped batter_rbis → 'rbis' while the whole grade/feature/outcome chain keys on 'rbi' — every PropLine RBI prop silently failed to grade AND settle. Fixed (+ regression test). Phase 4 — wired missing MLB stats end-to-end: - PropLine MLB markets 6 → 12 (+runs, walks, doubles, earned_runs, hits_allowed, outs — same request, no extra quota). - doubles/outs/triples added to featureCache + outcomeService MLB_LOG_FIELD and all three grade whitelists (analyze/scan/validation.py). Phase 6 — pipeline resilience: - opsNotify.js: ntfy alerts (never throws, test-disabled). Snapshot success/ stale/failure alerts; retry-once on hard odds error (not on empty slate). - Missed-cron watchdog (mostRecentExpectedSlot/isSnapshotOverdue); status probe now returns `overdue`. Coverage truth: MLB is the only end-to-end-live sport; outcome settlement is MLB-only (WNBA/NBA/soccer never settle) — documented as the #1 roadmap gap. Backend 2276 → 2289 tests (+13). Web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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8629021774 |
Session 54: Audit cleanup — name edges + polish (2255 tests)
P1 name edge cases (BOTH playerName.js copies, kept identical): - normalizeName strips hyphens (display+key): "Jung-hoo Lee" === "Jung Hoo Lee". - nameKey strips single-letter MIDDLE tokens: "Josh H Smith" === "Josh Smith" (keeps first+last; real middle names + collapsed initials untouched). - richie -> richard added to NICKNAMES. P2 polish: - Team Hub names normalized at the source (teamService.getTeamHub) so "J.C. Escarra" renders as "JC Escarra" like the dashboard. - snapshotService dedup keeps the highest-confidence GRADE but the richest DISPLAY (accented "José" over "Jose") so prop rows match the pitcher line. - correlationWarning names the game: "2 legs from the same game (NYY @ BOS)". Backend 2246 -> 2255 tests (+9), 194 suites. Web build clean (exit 0). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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cdedecf55b |
Session 52: Coming Soon teaser + infrastructure verification (2239 tests)
Phase 1 — Push-to-Book teaser (feature not live; teaser only): - StatStrip: "BOOK IT ⟶" per graded prop (hover: "Push-to-Book coming soon"). - GradeResultCard: "PUSH-TO-BOOK · COMING SOON" footer. Phase 2 — infrastructure verification: - snapshotScheduler logs armed AND disarmed state (incl SNAPSHOT_CRON) so container logs disambiguate off-vs-crashed. - NEW GET /api/internal/snapshot/status (internal-key gated): cron_armed, cron_hours_utc, last_snapshot per sport (gradeCount/deltaCount), redis_keys existence map, ticker_count. The post-deploy pipeline health probe. - Finding: Redis AOF/RDB persistence is a server-side (Coolify) config the app can't set/verify — documented. Phase 3 — delta pipeline (verified sound, no fix needed): - runSnapshot already rotates :latest->:previous and diffs locked lines; added opt-in SNAPSHOT_DEBUG=1 [deltas] log + a trace test asserting :previous is preserved verbatim and the delta math is correct. Backend 2234 -> 2239 tests (+5), 192 suites. Web build clean (exit 0). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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91b03c4044 |
Session 48: Name normalization at every layer + usage field (2156 tests)
Trace-first: the normalizer functions were correct (S47) but raw names still
flowed through paths that skipped them. Fixed each leaking path.
- 2a (source chokepoint): snapshotService.runSnapshot normalizes each grade's
player to the de-dotted display AND dedupes to one grade per nameKey|stat
(highest confidence) before writing grades:{sport} + snapshot:latest. Every
consumer (GameCard, Explore, leaders, profile) now gets clean merged names.
- 2b: buildPlayerStripsFromProps dedupes a player's props by stat (graded >
awaiting) → one row per stat (kills "Ks 5.5 AND Ks 3.5" variant dupes).
- 2c: scan tonightsPlayers grid groups by nameKey, displays normalized name.
- 3: profile VYNDR INTELLIGENCE "+0%"/"—" was buildIntel's defaults (separate
from the grade card's buildIntelFields, which already works). resolvePlayerStats
now attaches real usage (AB/G) + rest (B2B/Xd); buildIntel renders them; REST
default is now "—".
Backend 2149 -> 2156 tests (+7), 181 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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78db55d499 |
Session 47: Name normalization + grade intel + ticker polish (2149 tests)
- Name normalization completed: NICKNAMES table (Matt↔Matthew, Mike↔Michael...)
resolved in nameKey, parenthetical team-tag strip "(STL)", verified accent-fold
(Iván/Ivan, José/Jose). Slate strip now DISPLAYS the normalized de-dotted name
("AJ Ewing" not "A.J. Ewing") via buildPlayerStripsFromProps.
- Complete MLB VYNDR INTELLIGENCE: mlbGameLogFeatures derives rest_days (days off
between latest games; 0=B2B) + ab_per_game (usage). buildIntelFields renders
usage as "X AB/G", rest as B2B/Xd, matchup from bvp_advantage fallback.
- Ticker SCAN dedup: pushTickerItems keeps one SCAN per sport (sport field or
text-prefix parse for legacy); MOVE/GRADE preserved; cap 50.
- BOMBER threshold prorated for mid-season (hr>=15 strong / >=10 mod) so June
sluggers classify BOMBER not FLEX/DRIVER.
Backend 2122 -> 2149 tests (+27), 179 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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c8fc9f577e |
Session 46: Grade card intel + name normalization + pitchers (2122 tests)
Three focused P1 fixes on the Session-45 snapshot model.
- Grade card intel ROOT CAUSE: gameLogService is NBA/WNBA-only (offline Python),
so MLB props never got l5_avg/l20_avg and buildIntelFields returned {}. Wired
MLB game logs into featureCache.gameLogFeatures via mlbStatsAdapter.getPlayerStats
(pure mlbGameLogFeatures + MLB stat_type->field map). buildIntelFields gained
playerStats/projection fallbacks for partial intel.
- Player name normalization: src/utils/playerName.js (+ web/src/lib copy):
normalizeName -> {display,key}. Strips periods, de-dots suffix, accent-folds
the key. Applied in snapshotService grouping, slateAdapter grade index +
player-strip merge (variants collapse, longest name shown), and
playerIntelService. "A.J. Ewing"/"AJ Ewing" + "Jazz Chisholm"/"Jr." now merge.
- MLB starting pitchers: new GET /api/schedule/:sport/pitchers (probablePitchers
service wrapping mlbStatsAdapter.getScheduleWithPitchers + best-effort ERA).
Slate fetches it, builds a team->pitcher map (full name + mascot match),
attaches pitchers to MLB GameCardData. + Next proxy.
Backend 2100 -> 2122 tests (+22), 176 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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f8b120c0aa |
Session 45: Snapshot pipeline + GameCard swap + live ticker (2100 tests)
The on-demand "Read" grade model is RETIRED. A scheduled pipeline pre-grades the
slate, locks grades to the line, tracks movement; the dashboard shows them already
there. Orchestrates existing services — nothing rebuilt.
- snapshotService.runSnapshot(sport): getOdds → gradeAndCacheSlate → classify
archetype per player → lock gradedAt → line deltas vs previous snapshot → write
snapshot:{sport}:latest/previous + grades:{sport} → ticker events. Fully
injectable, zero-network unit tests. runAllSnapshots = cron entrypoint.
- Internal trigger POST /api/internal/snapshot/:sport + /all (requireInternalAuth).
In-process cron (SNAPSHOT_CRON=1, UTC 14,19,22,1,3) in server.js, no new dep.
- Public reads: GET /api/snapshot/:sport (cache-only) + GET /api/ticker (merges
TICKER_MANUAL pins) + Next proxies.
- GameCard swap: live Slate renders vyndr/GameCard (legacy kept for types only),
overlays locked grades onto game props → player name once + archetype badge +
"Graded Xh ago at -115 · Current 2.5 · ▲ TOWARD +1.0". Ungraded → "Awaiting next
scan", NO Read button. On-demand onGrade flow deleted.
- Ticker polls /api/ticker every 30s, graceful fallback to hardcoded items.
- NBA/WNBA: espnStatsAdapter free fallback (defensive parse → found:false on shape
mismatch) wired into resolvePlayerStats after the offline Python service.
Env: PROPLINE_API_KEY_1/2/3, VYNDR_INTERNAL_KEY, SNAPSHOT_CRON=1, TICKER_MANUAL.
Backend 2061 -> 2100 tests (+39), 173 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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