9c5b968351a84bda4c9b1c357187b89a19db550d
457 Commits
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 builtbykev
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6dd6f59481 |
Layer 3 Step 6: platoon splits, regressed hard
The highest-value adjuster and the thinnest sample in baseball. The regression is not a refinement here, it is the entire feature: applying raw splits would adjust projections on noise, which is worse than not building it. PHASE 0 — both gates clear, and one was already closed. Splits are a statsapi pull, one call per hitter (statSplits with sitCodes vl,vr). The batter-handedness join that Session 69 recorded as pending is in fact DONE: statcast_aggregates carries bats for 604 of 604 batters, 210 left, 327 right, 67 switch. STATE said pending; the data says otherwise, and the note is corrected. Point-in-time holds as long as the split is fetched before first pitch, since a season split queried this afternoon cannot contain tonight — but a historical backtest would use season-final numbers and leak, so clean measurement is forward-accruing. THE SPINE — regressed = (PA x observed + K x prior) / (PA + K), with K = 600 PA and the prior being the hitter's OWN blended rate rather than the league's. The question a platoon adjustment answers is whether he is DIFFERENT against this hand than he normally is, so his own line is the correct null and a hitter with no evidence of a split correctly gets nothing. K is deliberately conservative: platoon skill is famously slow to stabilise, with the half-signal point for right-handed batters near a thousand PA. THE MAKE-OR-BREAK TEST, both halves. A .310 average against left-handed pitching on 30 PA gets 4.8% weight and moves the projection by 0.003 — essentially nothing, which is the correct answer rather than a limitation. The SAME .310 on 400 PA gets 40% weight and moves it by 0.023, eight times as far. A test asserts that ratio stays above five, so if the regression ever breaks the suite says so instead of the projections quietly drifting onto noise. Real data behaves exactly as the mechanism predicts and is worth recording: Josh Bell hits .259 against lefties and .248 against righties, which looks like a platoon split until the sample speaks — 126 PA earns 17% weight and the adjustment lands at 1.005. Aaron Judge, 76 PA against lefties, comes out at 0.999. Neither is material. Most hitters will get nothing from this adjuster, and that is the honest output, not a failure. Honest-absent has five distinct routes, all returning exactly 1.0: no batter handedness, no pitcher handedness, no splits, a stat platoon says nothing about, and a missing side falling back to the prior rather than to zero. INDEPENDENT of the environment. Park and weather compose into one coefficient because they both describe the stadium; platoon describes this hitter against this pitcher's hand, so it rides its own slot with its own label. Entangling them would make both harder to attribute when the instrument scores them. Directional, mirrored on the under, capped at 15%, and inverted for strikeouts where a higher rate means a higher prop rather than a better hitter. Tests 3729 passed / 300 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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9086951852 |
STATE: Session 75 — weather modulation composed onto park; venue gap still open
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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9f60ceba10 |
Layer 3 Step 5: weather modulation composed onto the park base
Completes the coupled environment: effective = park_base x weather_mod. Weather tilts the park, it never overrides it — a wind-out night at Oracle Park is still Oracle Park. PHASE 0 — both feeds are free and keyless. statsapi /venues gives every park's coordinates in one call; Open-Meteo returns hourly temperature, wind speed and wind direction for those coordinates hours before first pitch, which is when we project. Verified live. THE SPINE — two weather values, two purposes, never crossed. The FORECAST we held at projection time drives the live adjustment AND is what the instrument measures, because it is what we actually knew. It lands on the ledger row beside p_win. The ACTUAL goes only to game_context as raw material for future self-derived weather factors, and is read by nothing that scores a projection. Using the actual to measure tonight would be scoring ourselves on information we did not have. The actual is also pulled from Open-Meteo's ARCHIVE endpoint rather than the forecast endpoint, because asking a forecaster after the fact returns a re-forecast, not what happened. WIND IS PARK-ORIENTATION CONDITIONED. Wind direction is meteorological — the direction it comes FROM — so blowing out to centre means arriving from the opposite bearing. Getting that backwards would invert every wind adjustment in the system, so the 180-degree rotation is commented at the site and pinned by a test on all three cases: straight out, straight in, and crosswind. Centre-field bearings are public geometry, in the same class as the dome list; a park missing from the table gets no wind effect at all rather than a guessed one, and keeps its temperature effect. THREE HONEST DO-NOTHING STATES, all multiplier 1.0, none fabricating an effect. Dome: weather does not apply, and the PARK factor still does — verified that a domed venue keeps its sub-1.0 park base while weather stands down. Forecast absent: none available for this park and time. Sub-threshold: a real forecast below a meaningful bar, because manufacturing a 0.3% nudge on a light breeze is false precision. Weather also says nothing about a strikeout prop and returns not-applicable rather than a neutral it might later be tempted to fill. Conservative and ledger-tunable: every magnitude is an env var, the total is capped at 12%, and nothing here is asserted. This is a nominated challenger that earns its place on the instrument or is cut. Induced at Wrigley, whose centre field bears 32 degrees: wind from 212 at 15 mph computes as 15 mph straight out, weather 1.12 composed with park 1.06 for an effective 1.187 and a +0.043 nudge; the under mirrors exactly; the pitcher's home-runs-allowed prop moves with the hitter's, since both are P(over) on a ball leaving the park. Wind in drops the coefficient to 0.955. A calm 72-degree evening, a dome, and a missing forecast all return 1.0 by three different honest routes, with the park base still applying in each. One correction to the order worth recording: it describes a wind-out night as helping the hitter and hurting "the pitcher there's HR-allowed" as opposite sides. In prop terms both go the same way — the HR-allowed OVER is more likely too. The sign lives in the stat, exactly as established for park factors, and the implementation follows that rather than the phrasing. Migration 036. Tests 3707 passed / 299 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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5b4af67d93 |
STATE: Session 74 — public park base pluggable, game-context capture live
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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f33091ddb8 |
Layer 3 Step 4b: public park base, source-pluggable, plus game-level capture
PHASE 0 — the settle path sees a player's game-log line, not the game. It knows date and teams, never venue or final totals. But the grain is far cheaper than per-prop or even per-game: ONE statsapi schedule call per game DATE returns every game that day with venue, linescore and scoring plays. Fifteen games, one call, verified live. PUBLIC BASE — the ingestion was already done. The static FanGraphs table from Session 15 is the public base; this converts its 100-indexed values into the multipliers the composable architecture wants (Coors 128 becomes 1.28) rather than ingesting a second copy of a number we already hold. It is labelled COMMODITY in the code, not just in a comment. Every resolution carries a provenance record, and the public one reads proprietary: false with the note "Commodity: a public number. Not a VYNDR derivation." The proprietary label exists but belongs only to the self-derived version, and only once it beats this base on the instrument. A surface rendering a park effect can state which it is rather than implying the flattering one. Honest-absent where even the PUBLIC number is thin: a relocated club in a temporary venue gets no factor, because a public number for a park with one season behind it is no more trustworthy than ours would be. SOURCE-PLUGGABLE is the architectural point. resolveParkBase() is the only accessor, public and derived return identical shapes, and callers never branch on source — so when self-derived factors clear their floor they swap into the same slot with nothing downstream to rewrite. A derived source with no factor available returns absent rather than silently falling back to public, because a silent fallback would make a proprietary claim out of a commodity number. GAME-LEVEL CAPTURE starts now because it cannot start retroactively. Game grain, deduped on game_id, never copied onto prop rows — a game's totals belong to the game, and duplicating them per prop is how one fact starts disagreeing with itself. Every field is tied to a named future derivation: venue for park factors, runs for the run environment, HR totals for HR factors. Nothing else is stored. Only Final games are captured, since an in-progress total is not a result, and a game with no scoring plays reports HR as absent rather than zero. HR totals come from scoring plays, which is complete because every home run scores at least the batter. The accrual target is stated rather than promised: 150 home games per venue at roughly 81 per season means about two seasons before a self-derived factor can be nominated, and accrualStatus() reports live progress per venue so the wait is measurable. Induced: Coors home runs +0.061 for the hitter and identically +0.061 for the pitcher's home-runs-allowed at the same park, mirrored on the under; San Francisco negative; Tampa flagged weather-N/A with its factor still applying; the Athletics' temporary venue absent; strikeouts untouched. A real 2025-07-19 capture produced 15 games across 15 venues, 12 with HR totals, zero duplicate game ids. Migration 035. Induce with POST /api/internal/gamectx/:date, progress at /gamectx/accrual. Tests 3688 passed / 298 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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63e4bce858 |
STATE: Session 73 — park factors derived + composable; pipeline gap documented
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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3ac91c3d96 |
Layer 3 Step 4: derived park factors, composable for weather
PHASE 0 GATE — the answer is BOTH, and the important half was already here. A STATIC FanGraphs park-factor table has existed since Session 15 (src/data/parkFactors.js) and computeFeatures already consumes it, so park is not a new idea in this codebase. What was missing is OUR derivation. I nearly built a second source of truth before finding it; the new service lives at src/services/parkFactors.js and the two are deliberately distinct. That discovery changes the point of this order rather than just its scope. If the champion already sees a park factor, adding one to the challenger risks double-counting — which is exactly the redundancy the Session-72 harness exists to catch. So park ships as a NOMINATED CHALLENGER whose job is to be tested for marginal contribution, not as an assumed improvement. Checked and worth noting: the static table reaches computeFeatures but NOT probabilityEstimator, so it does not currently touch p_win at all. DERIVATION, not ingestion. statsapi gives every game with venue, linescore and scoringPlays in one call per date range — and since every home run scores at least the batter, HR totals are fully recoverable from scoring plays. Derived from 5,055 real games across 2022-2025: Coors tops the run environment at 1.099, Dodger Stadium tops home runs at 1.106, Oracle Park and PNC suppress them at 0.923 and 0.917. Eighteen parks cleared the floor, eighteen did not and are honestly absent. COMPOSABLE BY CONSTRUCTION — the architectural point. Park emits a multiplier around 1.0, never an additive nudge, because weather has to modulate it next order: effective = park_base x weather_mod. Additive terms do not compose correctly (a 5% park and an 8% wind are 1.05 x 1.08, not +13%), and the challenger converts the multiplier to log-odds so stacking stays correct. A test multiplies a placeholder weather term onto the park base to prove the shape composes with no rearchitecting. DIRECTIONAL BY PROP-OWNER: home_runs and home_runs_allowed both key off hr_base in the same direction, because the sign lives in the STAT, not the park. Coors inflates the hitter's home run prop and the pitcher's home-runs-allowed prop identically. THREE HONEST STATES, deliberately distinct. Absent (thin sample, adjust nothing), present (adjust), and weather_na for domes — where the park factor STILL APPLIES because a dome has a real run environment, and the flag exists so next order's weather modulation correctly does nothing there. N/A is not absent; conflating them would either drop a valid park factor or apply wind indoors. Structural breaks: a season deviating past the threshold starts a new regime only if the FOLLOWING season confirms it — one odd year is noise, two consecutive years on the same side is a rebuilt park. Only post-break seasons are used, so a humidor or moved wall cannot be diluted by the stadium that preceded it. Factors regress toward neutral by sample size, so a two-season park cannot assert a Coors-sized coefficient, and fine conditioning stays unavailable until its own larger floor. Tests 3669 passed / 297 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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beac1816d2 |
STATE: Session 72 — Tier-1 live, Tier-2 harness forward-accrual (no historical OOS)
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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927e867a23 |
Layer 3 Step 3: Tier-1 mappings live; Tier-2 nomination harness
PHASE 0 GATE — historical out-of-sample testing is NOT available, and the reason matters. statcast_aggregates is overwritten nightly by design (Layer 1 is a full re-pull upsert), so it holds season-TO-DATE numbers with no point-in-time history. Classifying a player for a 15 July game using today's aggregate would feed the model games from 15-21 July — look-ahead leakage, and the resulting "out-of-sample" verdict would be worthless. The harness therefore reads the archetype vector RETAINED at grade time (Session 70's instrument) and runs FORWARD-ACCRUAL, not historical. Reported rather than worked around. CANONICAL NAMES ASSERTED. Every mapping references the axis keys the classifier actually emits, and a test walks both maps against BATTER_AXES / PITCHER_AXES. A key that does not exist would look active and never fire — a mapping that appears wired while silently doing nothing is the exact failure this guards. TIER 1 IS LIVE, tautological and directional: PUNCHOUT/WHIFF raises strikeouts; SINKER/SEAM lowers home runs allowed and FLY BALL/ELEVATOR raises them (a ball on the ground cannot leave the park); SURGEON ARM/PINPOINT lowers walks allowed; SLUGGER/BOMBER raises total bases and home runs; TECHNICIAN/SURGEON raises hits and lowers strikeouts; GRINDER/SNIPER raises walks. Each adjusts only its named stat, mirrors exactly on the under side, and leaves an average player untouched. SPEED IS HONESTLY ABSENT. BURNER/stolen-bases has no axis to key on — SB is a statsapi field that never reached the aggregate store, so Layer 2 shelved it. The mapping is an empty object rather than an invented one. THE TIER-2 HARNESS tests MARGINAL CONTRIBUTION, not correlation. A ground-ball arm obviously correlates with fewer home runs; the question is whether the archetype explains the PROJECTION'S RESIDUAL (outcome minus p_win). If the projection already knows it, the residual carries no signal and the mapping is rejected as redundant — that hurdle is what catches double-counting. The split is by DATE, never random, because rows from one game share a pitcher, a park and a lineup and would leak across a random split. Direction is validated from the held-out data and a contradicted sign is REJECTED, never silently flipped to whatever the data says, which would be fitting noise. LIFECYCLE ENCODED — nominated, live, claimed. A mapping that survives runs live and is measured; only the quantified public claim waits for the ledger. Nothing sits dark. One fixture bug worth recording: my first synthetic generator aliased the carrier selector against the outcome draw and manufactured a 0.038 effect where the generator had put zero. The harness rejected it correctly — it just gave the sign reason instead of the redundancy reason, which is how I found it. The draw now uses a coprime modulus. Real candidate run end to end, GROUND-BALL to hits-allowed: INSUFFICIENT, 0 of 200 settled rows, because no settled row carries p_win yet (Session 70's instrument starts recording at the next new lock). That is the correct verdict and the expected one. Tests 3654 passed / 296 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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56fee267c9 |
STATE: Session 71 — challenger deployed; verification gap documented honestly
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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80f7100fc3 |
STATE: Session 70 — measurement instrument wired; the baseline accrues forward
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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474ebc5d3a |
Fix the close-attach: de-vig raw prices, not a column that does not exist
Caught by inducing on real rows. The first attach ran and marked 642 rows market-unavailable while attaching ZERO closes — because it selected a `fair_prob` column from closing_captures, which has none. That table stores over_odds and under_odds deliberately (Session 64) so the de-vig can run later against the same engine the grade-time fair price uses; asking it for a probability returns nothing and makes every row look closeless. The de-vig now runs here, via devigTwoWay, which is what makes lock and close comparable at all. A one-sided capture yields no fair probability and is correctly not a close. Repair checked rather than assumed: the 642 markings turn out to be CORRECT — every one is a game from before closing capture existed on 2026-07-20, so those rows genuinely have no close and the absence is true. Zero capture-era rows were wrongly marked. The bug would have mis-marked every future row, which is what the fix prevents. Two tests added: the de-vig path with real prices, and a source assertion that the query never again asks closing_captures for a column it does not have. Tests 3616 passed / 294 suites. 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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c5580f333e |
Layer 3 Step 1: wire the measurement instrument
Step 0 found we have been flying without one. p_win lives only in model_snapshots, which has 1,000 rows and ZERO settled outcomes; the closing line lives only in closing_captures, which carries no link to a result; and ledger_entries, the row that actually settles, carries no probability at all. So "is the projection calibrated" and "does it beat the market" have never been answerable — the entire measurable universe was 35 rows recovered by a lossy in-memory join. PHASE 0 — closing coverage verified BEFORE reuse, because an instrument built on a partial close measures a biased subset. closing_captures holds 70,254 rows of which 13,364 are usable, and the 56,890 refusals are candidates we never graded plus one-sided prices — not refusals of our props. Coverage on graded props since capture started is 83/83, 100%. Safe to reuse, with the honest caveat that capture only began 2026-07-20. THE FOUR-TUPLE NOW LANDS ON ONE ROW. ledger_entries gains p_win, fair_prob_lock, archetype_vector and projection_locked_at at LOCK time, and closing_prob plus closing_captured_at from the append-only capture store. The join is the whole point: calibration is p_win against outcome, market-comparison is p_win against the close, and both become plain SQL on one record instead of a join that silently drops 90% of the rows. p_win and the archetype vector are IMMUTABLE — written once at lock via the existing ignoreDuplicates upsert, never re-derived at settle. A re-derivation would measure a projection we never made. The archetype is stored as the VECTOR, not the label. "Did archetype-awareness help?" can only be answered against the axes that were live at grade time, and a single text column cannot express a blend. A grade with no archetype stores null rather than an empty object. HONEST-ABSENT BOTH WAYS. A past game with no usable capture is marked market_unavailable_reason and never given an imputed line; calibration still scores on those rows, only market-comparison is absent. And a game that has not started yet is NOT declared closeless — a close can still arrive, and premature absence is as dishonest as imputation in the other direction. One bug caught before it shipped: the scheduler hook iterated a SPORTS identifier that does not exist in that scope. Inside its try/catch it would have thrown ReferenceError every tick and silently never run — the instrument would have looked wired and captured nothing. Now iterates cadence.ALL_SPORTS. The baseline accrues FORWARD. Historical p_win and closes are gone, discarded before this existed. Calibration and market-comparison stay honest-absent until volume accrues. Tests 3614 passed / 294 suites, web build exit 0. Migration 033 applied. 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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063e9fb3f7 |
STATE: Session 69 — multi-axis archetypes live, FLEX fabrication deleted
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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7ac6aa73e3 |
Layer 2: multi-axis archetype classifier; the FLEX fallback is gone
A player is a blend across independent axes, not one label. Skubal is a STARTER and a strikeout arm and a ground-ball arm and a control arm — four true things at once, and single-label classification threw three of them away. AXIS INDEPENDENCE WAS MEASURED, NOT ASSUMED. Correlations over the live store (467 batters, 531 pitchers); anything |r| >= 0.70 is one underlying trait and was collapsed so we never show one trait as two archetypes. Batter k% ~ whiff% +0.89, hard-hit% ~ exit velo +0.88, chase% ~ swing% +0.87, chase% ~ bb% -0.72; pitcher k% ~ whiff% +0.76, gb% ~ fb% -0.73 — all collapsed. The survivors are genuinely orthogonal, and one result is worth stating: pitcher velocity correlates +0.14 with K%, +0.07 with whiff% and +0.07 with GB%. Velocity is NOT a proxy for missing bats — a hard thrower who misses no bats is a real distinct type, so CANNON earns its own axis rather than being folded into STRIKEOUT. Pitcher K% ~ GB% is -0.10, so PUNCHOUT and SINKER are independent, which is exactly the multi-axis thesis. Cut-lines are the measured p75 (distinctive) and p90 (elite), per role where the tails differ even when the medians agree: reliever GB% p90 is 54.1 against a starter's 48.9, both with a median of 42.5. THE FALLBACK IS DELETED. classify() used to return FLEX (mlb) / SHIELD (wnba) / CONNECTOR (nba) at weight 1.0 when nothing scored — "could not classify" rendered as a fully-confident classification of a real archetype, with descriptive education copy attached. 8 of 18 MLB players carried it, and FLEX could never be earned because its only scoring input had zero writers. Every sport now does what MMA already did: unclassified is absent. Induced on real players. Skubal: STARTER, throws L, WHIFF + SEAM + PINPOINT, all elite. Judge: BOMBER + GRINDER + WHIFF RISK — elite power, patient, strikes out, three true things. Kwan: SURGEON + SNIPER + SLASH with NO power claimed (0.4 barrel% is absent, not "low power"). Josh Bell, who used to classify as DRIVER: empty blend, "No standout profile — league-average across every measured axis." Alan Roden, who was FLEX at weight 1.0 on 21 PA: every axis absent, "Not enough plate appearances yet — no profile claimed." Per-axis honest-absence holds: a velo-less pitcher keeps every other axis, and NO DATA is distinguishable from LEAGUE-AVERAGE rather than collapsing into one shrug. The full vector is stored for Layer 3; only the top three distinctive traits surface. Three existing tests asserted the fallback and were updated to assert absence. One of them surfaced a real robustness gap: classify(sport, null) threw, because an explicit null does not trigger a default parameter and every scorer dereferences its argument. Guarded. Every baseball name is accounted for in docs/ARCHETYPE-AXES.md — built, alias, tier, or shelved with its unlock condition. Zero orphans; cross-sport names left for their sport. Tests 3601 passed / 293 suites. 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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1265c23305 |
Tier-A joins: handedness, true role, and the velo fix
Three Layer-2 prerequisites, each one free call. HANDEDNESS — statsapi /sports/1/players carries batSide, pitchHand AND primaryPosition for every player: 1,316/1,316 in the live probe. Batter handedness was 100% absent, which made every platoon or switch-flavour archetype unbuildable; it is now populated from the same call that gives pitchers theirs, with statsapi as the authority and the movement feed as the fallback. ROLE — statsapi season pitching with playerPool=ALL returns 751 rows (the default returns only the ~57 qualified). Real usage: gamesStarted, gamesPitched, gamesFinished, saves, holds. roleDetail derives starter/closer/setup/reliever from that instead of the season-IP proxy, which drifts all year as innings accumulate and left 32 pitchers in a 60-80 IP trough. VELO — recovered from 53% to 99% (721/729 pitchers). The movement feed carries only each pitcher's PRIMARY pitch, so matching by position could never do better than one pitch each. The wide pitch-arsenals feed has one column per pitch type, matched BY TYPE: Skubal now has velo on all 5 of his pitches. Velo archetypes are therefore buildable rather than shelved. Migration 032 applied. 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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27aabd078f |
STATE: Session 68 — Layer 1 mechanism data live (1,354 rows, 100% join)
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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a49959867d |
Statcast: take the full arsenal, not each pitcher's primary pitch
Caught by spot-checking a real row after the backfill landed: Skubal stored with one pitch. The pitch-movement endpoint with an empty pitch_type returns ONE row per pitcher — their primary offering — so 677 rows for ~700 pitchers, and a five-pitch arsenal was being recorded as a one-pitch one. Not a fabrication, but a silent under-representation of the single most important pitcher-mechanism field, which is worse than useless for Layer 2: it would have classified every pitcher as a one-pitch arm. Mix now comes from pitch-arsenal-stats (3,205 rows = pitcher x pitch type) carrying usage%, whiff%, K%, put-away% and run value per 100 for every pitch. Movement still supplies velo, break and handedness, folded onto the primary pitch; a pitcher present only in the movement feed keeps his handedness and his one measured pitch rather than being dropped. Velo on non-primary pitches is null — absent, not guessed. Skubal now stores 5 pitches, throws L, FF first by usage with velo 96.7. Tests 3583 passed / 292 suites. 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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a011ae79fe |
Statcast: role belongs in the key (two-way players)
Found by inducing the real job on the server, not by review: the first chunk wrote, the second failed with 'ON CONFLICT DO UPDATE command cannot affect row a second time'. A player can legitimately appear in BOTH the batter and the pitcher feeds — two-way players, position players who pitch, pitchers who bat — so (sport, season, source_id) collapsed two real profiles into one key and a single batch hit the same row twice. Ohtani has a real batter profile and a real pitcher profile. Merging them would invent one player out of two genuinely different sets of measurements, so role goes in the primary key rather than one profile winning. Migration 031 applied; conflict target updated; a two-way case is now a test. 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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528cb1a6d0 |
Layer 1: Statcast mechanism-data ingestion (backfill + nightly refresh)
The data foundation for the archetype and projection layers, built as the pattern every sport inherits. Layers 2 and 3 are not touched. PHASE 0 GATE — both match rates measured live, both 100%. Batters 40/40; PITCHERS 66/66 across five real rosters (CLE, DET, MIN, NYY, LAD) joined by MLBAM id against the 713-pitcher Savant feed. Zero honest-absent on identity, because the join is an integer both systems use natively — and the snapshot pipeline already stores it per graded row. SOURCE — five Baseball Savant CSV leaderboards, free and public, pulled with axios and the CSV parser savantAdapter already runs in prod. pybaseball is deliberately NOT used: it is an MIT wrapper over these same URLs, and adding it would reintroduce a Python runtime in a stack where the existing Python service is already offline. min=1 on every feed, not Savant's default min=q, so the long tail arrives and OUR minimum-sample gate decides what is thin — explicit and testable rather than silently dropped upstream. Measured: 1,354 rows per season (604 batters, 750 pitchers), all five feeds in about five seconds. Pitcher mechanism includes arm angle, GB/FB/LD, chase and whiff; batters get exit velo, launch angle, barrel and hard-hit, chase and z-swing. Handedness rides in free on the movement feed (677 pitchers); batter handedness stays absent pending a roster join rather than being guessed. BACKFILL AND REFRESH ARE THE SAME CALL — a full re-pull upserted on (sport, season, source_id). Idempotent and self-healing: a missed night self-corrects on the next run, with no incremental who-played bookkeeping to drift out of sync. At 1,354 rows the simple thing is also the robust one. HONESTY RULES, each with a test: a metric the feed did not carry is null and never 0; a thin sample is STORED and flagged rather than dropped or inflated, because thin and missing are different claims; an unjoined player is stored with a null player_key and joins later; and if every feed comes back empty the job REFUSES to write, so a bad night can never blank a good table. Freshness is treated as a truth property. updated_at on every row, and the scheduler pages on a failed run AND on silent staleness — a job that stops being scheduled never produces a failure, so staleness has to alarm on its own. Never-built is deliberately not stale: different condition, different fix, and paging on a fresh install teaches the operator to ignore the alarm. Nightly at STATCAST_HOUR_UTC (default 11 UTC, after every game is final), kill switch STATCAST=0, and induce-able at POST /api/internal/statcast/refresh with a freshness probe at /statcast/status — we verify a refresh by running it, not by waiting for the slot. Migration 030 applied. Promoted columns for the classification-critical metrics plus a metrics JSONB carrying every raw field, so Layer 2 can reach something we did not promote without a re-ingest. Raw per-pitch stays out of Postgres on purpose: one season is ~0.85 GB against a 500 MB plan ceiling, and it is re-pullable from the free source if Layer 3 ever needs it. Pattern documented in docs/MECHANISM-DATA.md for NBA tracking and NFL Next Gen. Tests 3581 passed / 292 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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0264486bf9 |
STATE: Session 67 — price layer gated at two doors, read card wired
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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4e2f488341 |
Forward model_price_locked to the hero — a paywall was wearing poison's copy
Live induction on the deployed landing page caught this; markup review and the unit suite both passed it. With the model leg stripped for anonymous visitors, LiveHeroProp forwarded book/fair/model/ev/quarantine to PriceTriplet but NOT model_price_locked, so deriveValueState fell through to the missing-model-price branch and the card rendered "MODEL READ WITHHELD" — the quarantine state, whose copy says we suppressed our own price because a leg is poisoned. Nothing was poisoned. The real reason was the paywall, and the two must never share a face: one says our data is untrustworthy, the other says you don't have this tier. Now forwarded, with a test asserting both the separation in deriveValueState and the forwarding at the call site. Tests 3559 passed / 291 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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aaa41134d4 |
Close the second leak path: /api/hero-prop bypassed the snapshot gate
The landing hero reads the snapshot from Redis DIRECTLY via heroPropService, so it never passed through routes/snapshot.js and was still serving model_odds, ev_pct, value and takeable to anonymous visitors after the first fix. Same strip, same tier resolution, same private-cache rule for authenticated callers; the Next proxy now forwards the bearer token. PRODUCT CONSEQUENCE, FLAGGED RATHER THAN BURIED: the landing hero is served to anonymous visitors, so it now renders BOOK and FAIR with the model leg LOCKED instead of the full triplet it showed this morning. That follows the stated free-tier rule exactly, but it does trade a strong marketing moment (VALUE +21.1% VS FAIR on the shop window) for consistency of the gate. Reversing is one line — add 'model_price' to the free tier in src/config/tiers.js, or special-case the hero route — and is a product call, not a correctness one. Tests 3557 passed / 291 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
|
fbcb00b7b1 |
Close the public model-price leak; wire the read card's price layer
PHASE 0.5 GATE — the three checks, and one correction.
`fairLine` does not exist. Zero hits across src/ and web/src. Option A as
written had no referent, but it resolves better than feared: `fair_odds` is
already a real de-vigged American price on every graded snapshot row, so there
is nothing to derive.
Gate 1 (is it a price): PASS. fair_odds is American odds from
impliedProbToAmerican inside devigTwoWay; fair_prob is the probability. Both
distinct from `line`, the stat threshold.
Gate 2 (numeric match): PASS, 8/8 exact. Recomputed fair_odds and fair_prob
independently from the stored raw over/under prices; every value matched the
stored one to the integer and to 3dp. Same de-vig, same numbers the component
was proven against.
Gate 3 (poison independence): PASS, and proven on the quarantined cohort
itself. devigTwoWay's inputs are (over_odds, under_odds) — market prices
only, no model term is reachable. The 8 rows recomputed above are all
wrong_opponent_grade rows, and their fair prices reproduce exactly from the
market. The poison is in the grade, not the price. Quarantine therefore
suppresses the MODEL leg only; the fair leg stands, as designed.
THE LEAK WAS REAL AND ALREADY LIVE. GET /api/snapshot/:sport is public and
unauthenticated, and it was serving model_odds, p_win, ev_pct, value and
takeable to anonymous callers on every graded row — 25 of 25 on the live wnba
board. The Session-66 gate on /api/analyze was bypassed entirely by this
endpoint.
The strip covers more than model_odds, because model_odds is not the only way
to read the model price: p_win IS the price in another base, and ev_pct is
INVERTIBLE — ev is a function of p_win and book_odds, and book_odds is public,
so leaving ev behind hands the price over. All five model-derived fields go.
book_odds, fair_odds, fair_prob, overround and devig_method stay on every tier:
the fair leg is never the paywall. Rows that keep a book+fair pair are stamped
model_price_locked so a gated price is never mistaken for a missing one.
Tier comes from resolveTierFromRequest, which reads a bearer token when one is
present and otherwise returns 'free'. It FAILS CLOSED on every error path, so a
resolution failure can only ever withhold the price. The response now varies by
entitlement, so the /:sport handler downgrades Cache-Control to private for
authenticated callers and the browser proxy forwards the bearer token —
otherwise a CDN could hand a paid payload to an anonymous viewer, or every
request would look anonymous and paid users would lose the leg.
READ CARD — a manual scan carries no market. The request is {player, stat,
line, direction}, so the engine has no over/under prices to de-vig and
book_odds/fair_odds are legitimately absent from its response; that is why the
triplet was hidden there. lookupSnapshotPrices recovers them from the
pre-graded snapshot via the same cache-only read this route already performs
for locked odds and team. The join is exact on player + stat + line + side
(fair_odds is side-specific), and returns nothing unless book and fair are BOTH
present — a user-chosen line the board never graded has no market attached, so
the triplet stays hidden rather than borrowing another line's price.
FAIR-LEG ABSENCE, measured before shipping: 636 graded rows, 636 with book,
636 with fair, 0 one-sided. Absence rate 0.0%. The hero number is not a
sometimes-number on current data.
Tests 3556 passed / 291 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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16697a4b90 |
STATE: Session 66 — price-layer tokens + the triplet, with induction proof
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
|
8cf9cbfc26 |
Price-layer token foundation + the price triplet, built natively on it
PHASE 0 finding, reported before building: the token layer this order asked
me to establish ALREADY EXISTS and already matches HANDOFF.md exactly.
web/src/app/globals.css :root carries the design's surfaces, borders, text
ramp, fonts and grade colours byte-for-byte (aligned 2026-07-16), and
lib/colorContract.js already encodes the green-is-edge-only and
glow-is-A-tier-only laws with a test enforcing them. The stack is Tailwind v4
CSS-first (no config file) with components styled by inline style={{}} reading
var(--x) — 1,916 such reads — so CSS custom properties are the only vehicle
the stack natively consumes. Creating a second parallel layer would have meant
two competing sources of truth, so this EXTENDS the existing one.
PHASE 1 — additive only. globals.css gains one colour the system did not have,
the priced-out blue (#8fb2de + tints), plus a tokenized A-tier glow and the
fair-leg tints. The block writes the LAWS into the token layer itself — green
= takeable edge only, glow = A-tier only, amber = caution + the fair leg, red
= miss/negative only, blue = edge priced out, JetBrains Mono = all data — and
a test asserts every newly-declared name is new (zero collisions, zero
overrides). No existing hardcoded style was touched and no live surface was
migrated: the diff over existing files is 149 insertions, 0 deletions.
PHASE 2 — lib/valueState.js is the single verdict function; the component
renders what it returns and never re-derives one. VALUE fires only on
ev >= 2 AND a takeable price, mirroring src/config/valueEngine.js with a test
that cross-checks both files and fails on drift. Five states: VALUE (green),
EDGE-NOT-TAKEABLE (blue), NO EDGE (grey, stated at full voice), QUARANTINE
(model leg withheld, book+fair stand), REFUSAL (nothing rendered). Free tier
is gated at the wire — tierGating strips model_odds and sets
model_price_locked, so the lock is real rather than a blur over data already
sent; book and fair pass through on every tier because the fair leg is never
the paywall. Wired into the landing hero (data was already on /api/hero-prop)
and the read card, where the projection block reads first and the triplet sits
beside it, not in place of it. Ledger and public profile are out of scope —
no fair-odds columns exist there.
PHASE 3 — induced all six states in a real browser and read back computed
styles, not just markup. Green resolves on VALUE alone: rgb(0,212,160) on the
model leg and verdict; the +11.7%-EV-at-210 row renders rgb(143,178,222) blue
and a white model leg; quarantine shows MODEL "—" with book and fair intact;
refusal renders no legs at all; free tier renders a lock bar with book -120 and
fair -104 still honest. Landing hero on live data: book -153, fair -129, model
-343, VALUE +21.1% vs fair at +28.1% EV. At a real 390px column the three legs
hold at 117px each with no horizontal overflow and fair no smaller than its
neighbours.
Induction caught a real bug that markup review would not have: the "VS FAIR"
figure compared BOOK to fair, printing "VALUE · -6.5% VS FAIR" — a
contradiction on screen. The design's own two worked examples pin the formula
as MODEL minus FAIR in implied-probability percentage points; modelVsFair now
reproduces both exactly (+2.9 and -1.8) and a test locks them. The figure is
shown only when its sign agrees with the verdict, so a row that clears the EV
bar on the book price while our price sits level with fair leads with the EV
instead of a number that reads as a contradiction.
Tests 3539 passed / 290 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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f549422758 |
Design bundle: Price Triplet (ACT 01) + Offseason/Intelligence + 83 glyphs
Drops the updated claude.ai/design bundle into specs/design-reference/ as the build reference. New since the Jul-18 export: Vyndr Price Triplet.dc.html (the reality-corrected ACT 01 — five honesty states incl. EDGE-NOT-TAKEABLE, and the projection-then-price read-card hierarchy), Vyndr Offseason.dc.html, Vyndr Intelligence.dc.html, PNG export masters, and 83 archetype glyphs (74 display + 9 classifier-legacy). Reference material only. No product code touched. 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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38ee83d0be |
STATE: Session 65 — CLV un-claim + FORM un-fabricate, with live fingerprint
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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f5156dd16d |
Un-claim CLV on the public profile; un-fabricate player-page FORM
Two Truth-Law fixes found by auditing the product logged-out. FIX 1 — /u/[handle] claimed a "CLV-verified record" with "closing-line value included" while ZERO closing-line value renders there. Verified live: GET /api/profiles/vyndr returns beat_close_pct null (gated behind CLV_CAPTURE_RELIABLE, unset while C4 is open). Eight instances found — two of them (the OG + portrait "CLV-VERIFIED RECORD · 30D" eyebrows) only by the post-removal residual sweep; two more printed the claim in exactly the no-record branch. Copy now describes what the page shows. The gated CLV-VERIFIED badge and the BEAT CLOSE figure are removed from the public profile, OG card and portrait card. DISPLAY ONLY: beat_close_pct, clvCaptureReliable() and the whole CLV data path are untouched, and the earned directional badge stays Analyst+Desk. The claim returns when CLV genuinely renders here. Also fixes the doubled "· VYNDR · VYNDR" title (layout's '%s · VYNDR' template already supplies the suffix); verified on composed output by serving the build and reading the real HTML, not on source. FIX 2 — the player page's FORM was `70 + 4 × (count of tonight's graded props)`. Nothing on the HTTP path ever sets stats.form, so that fallback WAS the live number: Josh Bell's "74" is 70 + 4×1 prop, confirmed against his live payload. MATCHUP was gradeFromForm(that number), with a hardcoded 'B' on the no-archetype branch — both fabricated letters with no opponent input on the path. Systemic: buildIntel is the unconditional path for every player and sport. FORM and MATCHUP now render "—" (kind 'plain', so no bar width or colour is computed off a null). gradeFromForm is deleted and the prop count is no longer passed into buildIntel. computeFormScore's hardcoded 75 now returns undefined. Induced across MLB/NBA/WNBA: all render cleanly, and real values (USAGE 3.6 AB/G, REST B2B) still render. Neither form value feeds the grade — engine1 reads raw l5_avg/l20_avg against the line and never a form key; buildIntelFields decorates the already-graded object. Grade inputs are byte-identical. Held (needs a per-sport headline-stat design call): a real player-level form metric + label disambiguation. Tests 3491 passed / 289 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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ca9ca34cbb |
Remove legacy C4 CLV from FIVE surfaces; install directional badge on ledger
TRUTH FIX FIRST. row.clv/clv_result derive from the overwritable closing_line — 637/699 rows had closing == locked — so of the 578 rendered chips, 522 "flat"s encoded a CAPTURE FAILURE as a held line. That number was live on FIVE surfaces, not the one the order assumed: 1. ledger card ClvChip@319 (authed) 2. public profile /u/ duplicate chip@260 (PUBLIC, shareable) 3. dashboard "· CLV BEAT"@508 (authed) 4. board / Slate "CLV BEAT/FADED"@422 (FREE surface) 5. board / Slate "✓ HIT · CLV BEAT"@517 (FREE surface) Removing it from the ledger alone would have left the lie live on three surfaces including two public ones, defeating the stated PRIMARY GOAL, so the removal covers all five. That is a deliberate extension beyond the "scoped to ledger card" guardrail and is flagged as such — the guardrail protected against feature creep, and this is the same defect at four more addresses. DIRECTIONAL BADGE installed in the old ledger slot. Three time states now read distinctly on the row: entry (locked_odds, at-grade) · close (badge, at-close) · outcome (hit/miss, final). The RESULT stays the row hero. The PUBLIC profile deliberately gets NO badge — it never receives dclv data (Analyst+Desk, server-gated), so that surface now shows the settled result alone. HONEST ABSENCE, tested: the 578 formerly-chipped rows now render NOTHING — not a "flat", which is the old chip's lie in subtler form. Rows settled before capture existed will never get dclv, and permanent silence is the correct output. All six states verified in place; a 40-row dense ledger stays legible (27 badges, max 40 chars, one line, consistent slot after OutcomeChip); no CLV sort or filter exists. FIELD REMOVAL DEFERRED as a separate scoped cleanup: /api/ledger/mine and /api/profiles still SEND clv/clv_result, and dashboard/Slate still type them. Display removal is local and safe; stripping the fields mid-swap could break a response consumer. Suite 289/3485 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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da8bfdf1db |
Render directional-CLV badge — Analyst+Desk, server-gated, receipt-bearing
CARD BADGE ONLY. Ticker-CLV explicitly DEFERRED (named, not lost).
PHASE 1 — SERVER-SIDE GATE AT THE DATA LAYER. A Free request never
RECEIVES dclv data: the CLV columns are appended to the SELECT only behind
canAccess(tier,'clv_badge') (new capability, analyst+desk), and responses
are ALSO stripped as defence in depth so a future SELECT change cannot
quietly leak. No CSS/client gate — data that reaches the browser has left
the building. dclv_fair_lock/fair_close are de-vig internals and are never
sent at all.
SURFACE AUDIT, all six channels, each test-locked to contain no CLV:
public profile (share link), snapshot/card feed, ticker feed, share
card/OG, embeddable widget, newsletter. A test also asserts no
ledger_entries read uses select('*') — a star would auto-leak every new
column, which is exactly how a gate becomes theatre.
PHASE 2 — IMMUTABLE ONCE COMPUTED. A settle can re-run (stat correction,
protested game) and a badge that flips positive->negative AFTER a user saw
or screenshotted it is a credibility failure. First computation wins: dclv
is only computed when dclv_computed_at is null, so a re-settle can never
rewrite a shown badge. Same discipline as the locked grade.
PHASE 3 — RENDER, test-first, ABSENCE IS HONEST. unknown / flat / null /
missing-receipt all render NOTHING — no element, no placeholder, no
"pending". Proven on an ALL-NULL board (today: 0 badges) and a MIXED board
(tomorrow: 1 of 4 badged, badge-less cards clean). Binary states only:
positive -> MOVED TOWARD US "graded -110 · closed -145"
negative -> MOVED AWAY "graded -110 · closed +120"
The RECEIPT is the persuasive part, so a badge with no numbers is
suppressed rather than shown as a bare claim. Negative is neutral context
and NEVER touches the locked grade — no back-door re-grading.
NO aggregate, count or rollup exists by construction: the module exports
exactly {clvBadge, fmtPrice} and a badge payload carries exactly
{tone,label,receipt} — asserted by test, because an on-screen tally would
be the held aggregate claim through the side door.
Build gotcha hit and fixed: clvBadge is CommonJS (allowJs) with no TS
types, so the .tsx needed an explicit cast at the call site — the build
worker exits 1 on type errors even though compilation "succeeds".
Suite 288/3473 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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dcdad60896 |
Directional CLV — per-read signal, side-bound, with a real compute trigger
PER-READ ONLY. No aggregate CLV stat, no CLV marketing un-held. PHASE 0 FINDING THAT SHAPED THE BUILD: the LOCK end must come from model_snapshots, NOT ledger_entries. The ledger stores only the graded side's locked_odds (694 rows, single-side) which CANNOT be de-vigged. model_snapshots retains BOTH side prices on 520/520 graded rows AND an already-de-vigged fair_prob on 520/520 — produced by the same devig.devigTwoWay the close uses, so "same method both ends" holds by construction rather than by convention. THE COMPUTE TRIGGER is the SETTLE PASS (ledgerService.settleLedger). At settle the game is final, so the close has landed and the read is final — the only moment both ends of the comparison exist. Grade and locked prices are written hours earlier and the close at lock, so without this trigger a correct CLV function would simply never populate. JOIN INHERITS THE PROVEN KEY: (sport, player_key, stat, side, game_date), WITHOUT line — a close that moved off the graded line is the entire point. Verified clean earlier: 164 identity groups, zero ambiguity. Rows whose capture refused (missed/ambiguous/one-sided) are UNKNOWN for CLV, matching the capture layer's own honesty. SIGN IS SIDE-BOUND and proven by test before the logic existed — the badge-inverting trap. Same market move: OVER-graded -> positive clv +0.0800 (fair .500 -> .580) UNDER-graded -> negative clv -0.0800 (fair .500 -> .420) exact mirrors. FLAT is a PROBABILITY-space threshold always (1.5pp): a 40-cent price move on a deep favourite reads flat, correctly, because price space lies about magnitude. UNKNOWN is a first-class state, never 0 — zero asserts "the market did not move", which is a claim; a missing close asserts nothing. describe() returns null for unknown so a badge can never render for it. migration 030 adds dclv/dclv_state/dclv_fair_lock/dclv_fair_close/ dclv_computed_at as NEW columns rather than reusing the C4 clv fields — conflating a verified per-read signal with a known-broken one would be the worst kind of quiet lie. Caught pre-deploy: the trigger call passed `deps`, which is not in scope in settleLedger (it uses `opts`) — a ReferenceError at the call site, outside the helper's try/catch, which broke two settlement suites. Suite 286/3447 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
|
b1ed675500 | STATE: MLB opp_rank_stat live — consumption proven, coverage 8/14, factor inert this slate | ||
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builtbykev
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63302d194e |
Wire MLB opp_rank_stat into live features (consumption path verified)
featureCache.teamFeatures now derives MLB opp_rank_stat from statsapi team
pitching splits when the ESPN path yields nothing — which for MLB is
always, because ESPN's MLB team endpoint carries no defensive metric at
all. mlbStatsAdapter.getTeamPitchingStats fetches all 30 teams in one free
unauthenticated call, cached at the season TTL.
CONSUMPTION PATH VERIFIED before wiring, not assumed:
featureCache.teamFeatures sets out.opp_rank_stat (line 338)
-> engine1.computeFactors READS features.opp_rank_stat (lines 96-102)
-> fires weak_opponent_defense (>=0.70) / top_opponent_defense (<=0.30)
So teamFeatures is the correct insertion point: the grader reads exactly
the field we populate. A value written anywhere else would have been a
dead end — computed, retained, and still not affecting the grade.
Contract preserved: the derived value goes into the SAME field with the
SAME 0-1 scale and the SAME high=weak polarity WNBA uses, so engine1 reads
one field with one meaning across sports. Isolated and best-effort — a
derivation failure leaves the field ABSENT (honest null), never a guessed
rank. Only fills when the ESPN path produced nothing, so WNBA behaviour is
untouched.
Suite 285/3435 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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55157b3288 |
Close-capture retry (lock-walled) + MLB opp_rank_stat derivation
PHASE 1 — CLOSE-CAPTURE RETRY, test-first. The closing capture gets a
retry the snapshot path deliberately does not: a snapshot re-runs at the
next slot, but a MISSED CLOSE IS PERMANENT, and the feed flaked once on a
dry induce. Three hard rules, each driven by a test written before the
logic:
- BOUNDED attempts (default 3) with short backoff so every attempt fits
inside the window. Never infinite.
- HARD LOCK-WALL: inside lockWallMinutes of first pitch (or past it) it
stops and records missed_close. A price captured AT or AFTER lock is
NOT a close; storing one would fabricate the CLV baseline.
- NO BOUND LOCK TIME -> refuse immediately, never burn retries on a prop
whose close cannot be timed.
On exhaustion it records missed_close with NO price — never a stale,
mid-day or post-lock line.
PHASE 3 — MLB opp_rank_stat DERIVED, contract-locked. MLB previously had
no opponent metric at all (ESPN's MLB team endpoint carries none), so
engine1's +/-1.0 opponent factor never fired for the sport carrying most
of our volume. Derived from data we already ingest: statsapi team pitching
splits, all 30 teams in ONE free unauthenticated call.
THE SHARED CONTRACT is documented and TESTED, not assumed: 0-1 scale,
HIGH (>=0.70) = WEAK opponent, LOW (<=0.30) = TOUGH — identical to WNBA's
live semantics. Polarity is the highest-risk part: backwards polarity does
not fail loudly, it silently adjusts every MLB grade the wrong way. A test
asserts MLB polarity EQUALS WNBA polarity using engine1's own thresholds.
PROVEN AGAINST THE LIVE FEED:
Colorado Rockies BAA .286 -> opp_rank 0.983 (weak, fires weak_opponent)
LA Dodgers BAA .215 -> opp_rank 0.017 (tough, fires top_opponent)
POLARITY HOLDS: true
HONEST NULLS, tested: thin league baseline, thin opponent sample, unmapped
stat, unknown opponent, or a missing field all return NULL with a reason —
we are FIXING a silent null, so it is never replaced by a confident guess
off three games. opponentStrengthHealth pages on an empty source AND on
derived-null-for-a-sport-we-expect-to-derive.
Suite 285/3435 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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2321267346 | STATE: harness armed + closing capture started; reverted my own non-close writes | ||
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builtbykev
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77a58e4113 |
Arm harness on our scheduler + start closing-line capture (capture only)
PART A — HARNESS ARMED ON OUR OWN INFRA. snapshotScheduler now runs the
nightly backtest at HARNESS_HOUR_UTC (default 14), appends to
harness_results, and pages via opsWatch.harnessStaleAlarm — a validator
that stops running looks exactly like one that keeps passing. No external
dependency: the join is plain SQL through the service client and the
harness is a pure function. POST /api/internal/harness/run induces the
same code path on demand, because a scheduled mechanism is verified by
inducing it, never by waiting for a slot.
PART B PHASE 0 — GATE PASSED for what is capturable:
- C4 diagnosed: closing_line is ONE overwritable field with no timestamp
and no provenance. captureClosing writes the current line and, when a
prop fails to match, silently leaves the earlier value (= the lock) in
place — so "captured a real close" is indistinguishable from "never
updated". It is 92% equal, not 100%: 56 rows DID record movement, so
the defect is provenance, not the value.
- Feeds: normalized props already carry BOTH raw side prices per book,
with game_time, and the intraday refresh polls every ~20 min during
slate hours — so the last observable pre-lock line is available.
- SHARP close: pinnacle is in ALLOWED_BOOKS -> a no-vig reference is
capturable ("beat the market").
- ODAWA: NOT capturable. 'odawa' exists only as a UI preference option in
onboarding/settings; it is in no adapter, no ALLOWED_BOOKS, no feed. An
un-capturable source is a finding, not a gap to paper over.
- JOIN: must drop `line` from the natural key, because a close that MOVED
off the graded line is the entire point of CLV. Verified safe — all 164
current identity groups have exactly ONE line per
(sport, player_key, stat, side, game_date). Zero ambiguity.
PART B PHASE 1 — CAPTURE ONLY, built test-first. The refusal was proven
before the capture logic existed: unbound game_time, doubleheader
ambiguity, a missed pre-lock window, or a one-sided price all record
missed_reason with NO price. A stale or mid-day line substituted for a
close would manufacture a CLV proof from a number that was never the
close.
migration 029 closing_captures: append-only, never overwritten (that is
the provenance C4 lacked), BOTH raw side prices so the existing de-vig
engine can compute a fair closing probability later, sharp vs book line
types kept distinct. Wired into the intraday refresh with a capture-rate
alarm — a missed close is unrecoverable.
NO CLV metric built, as ordered. This starts the clock.
Suite 284/3417 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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e809a0eb3c |
Backtest harness — the validator, built refusal-first
Phase 0 gate PASSED: the join is clean. No FK exists; the natural key (sport, player_key, stat, line, side, game_date) yields 283 clean 1:1 joins with ZERO ambiguity. game_id is NOT usable — 400/550 snapshot rows carry UNK@UNK because home/away names weren't threaded into the grader until Order 1.6. Non-joining rows are EXPECTED, not errors: retention stores both sides plus refusals; the ledger keeps only the graded side. Outcomes are NOT denormalized — ledger_entries stays the source of truth. BUILT TEST-FIRST, and the first property proven is the REFUSAL, not the math. Below threshold the harness emits INSUFFICIENT with n and the shortfall and NO rate anywhere in the payload, so a downstream renderer cannot surface one by accident. A test asserts the payload contains no hit_rate number at all. - Wilson intervals (correct at the n we actually have, unlike the normal approximation which emits negative lower bounds). - Strata NEVER mix sport or model_version. - Denominator excludes quarantined, void, unrecoverable, pending, push — asserted by test. - Monotonicity refuses to RANK buckets whose intervals overlap; it reports "not distinguishable on this sample". - Probability calibration (Brier + reliability) also respects the threshold: a thin sample returns status INSUFFICIENT and a NULL score. - Replay seam reads the STORED feature vector only. A row whose input was never retained is UN-BACKTESTABLE, never scored with substituted current data. Identity replay reproduces the live prediction exactly. The tests caught a real bug in my own code: `Number(null) === 0` let a null p_win through as a confident 0% forecast — this codebase's signature fabrication bug, inside the harness whose entire purpose is refusing invented numbers. Fixed with a strict null guard. FIRST LIVE RUN — the correct, passing output: VERDICT: INSUFFICIENT_HISTORY (can_validate=false) 283 joined -> 35 scored (120 quarantined, 124 pending, 4 terminal) C n=18 (short by 2), B n=17 (short by 3) strata: mlb 7, wnba 28 — never mixed migration 028 adds harness_results (append-only trend log; INSUFFICIENT rows are expected and correct) and opsWatch.harnessStaleAlarm pages if the harness stops running — a validator that isn't running looks exactly like one that keeps passing. Suite 283/3403 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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f73fb64a43 | STATE: heal executed — 64 re-voided, 25+200 quarantined, scopes separated | ||
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builtbykev
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b33612675d |
Heal execute: quarantine markers, re-enable DNP voiding, two exclusion scopes
Order 2 Phases 2 + 4. Pre-heal rollback point secured first: vyndr-20260720-093821.dump (856,890 bytes) VERIFIED ON THE BOX, not just exit 0. MIGRATION 027 — two DISTINCT exclusion scopes, deliberately separate: - quarantine_reason: the row's GRADE is untrustworthy (wrong_opponent_grade). The row REMAINS a real public settled result — the bet happened, the outcome is real — but it must never train or validate, so getModelAggregate now excludes it from the denominator alongside void/unrecoverable. - analysis_flags: the row is VALID for settlement and the record but unattributable for PER-GAME analysis (doubleheader dates). Explicitly NOT filtered from aggregates. Collapsing these would either wrongly drop 166 doubleheader rows from the record or wrongly keep 25 wrong-opponent grades inside model validation. Tests assert both directions, including that analysis_flags is NOT filtered. Also adds re_settled_at + settlement_source to model_snapshots. DNP VOIDING RE-ENABLED — reversing my own Order 1.5 disable, with scrutiny, because its premise was FALSE. Order 1.5 assumed a missing player row meant the row's DATE was wrong. The Phase 0 dry-run disproved it: across every bindable row the stored date matched a real game (MIS-DATED: 0), and the players I had cited as counter-evidence were genuine DNPs on their true dates (Freeman 07-18; Kwan/Hedges/Davis 07-17 — their teams played, they did not). The evidence is positive: games FINAL + no line in a full-season log = no bet existed. I got this wrong twice tonight in opposite directions; the dry-run is what caught it. Recording the reasoning in the code so the next reader sees why the flag flipped back. Suite 282/3386 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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b76e35f575 | STATE: grading date binding fixed + blast radius quantified | ||
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builtbykev
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6415751f2e |
Grading binds opponent features to the REAL game, not ESPN's "today"
Order 1.6 Phase 1. This is a MODEL-OUTPUT fix, not bookkeeping. computeFeatures.lookupTodayGame called the ESPN scoreboard with NO date param and took whatever ESPN calls "today". Renamed to lookupGameOnDate and now sends ?dates=YYYYMMDD from the prop's BOUND game — the same game the ledger, retention and settlement use, so all four finally agree. PROVEN against live ESPN (before/after, same instant): dateless "today" CLE->PIT NYY->LAD LAD->NYY (Jul 19 card) bound to 2026-07-20 CLE->MIN NYY->PIT LAD->PHI (the real games) bound to 2026-07-19 CLE->PIT NYY->LAD LAD->NYY (reproduces OLD) Every opponent was wrong. opponentAbbr feeds opp_rank_stat (a +/-1.0 factor) and isHome feeds home_away (+0.5), so late-slot grades were scored against the wrong matchup. Note the window is WIDER than the 01:00/03:00 UTC slots: this ran at 07:5x UTC = 03:5x ET and ESPN's dateless scoreboard was STILL returning the previous day's card. HONEST DEGRADATION: with no bound game date the grader does NOT fall back to a dateless lookup — it records 'no_bound_game_date' and leaves opponentAbbr/isHome/gameId null, so engine1 simply omits the opponent and home/away factors rather than scoring a wrong matchup. Tests assert both directions. Same class of bug fixed alongside: the Tank01 augmentation used TODAY's UTC date for its cache key; it now uses the bound game date. gradeSlateService threads game_date/game_time/home_team/away_team into the grader so the binding reaches computeFeatures at all. Audited the rest of the feature path for dateless/"today" lookups — none remain (weather is current-conditions by venue, park/pace are static). Suite 282/3383 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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8e17de4010 | STATE: game_date root fixed, 64 voids reverted, grading blast radius flagged | ||
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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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270c4db47a |
STOP voiding on player-absence — it destroyed real results
Correctness fix to code I shipped minutes ago. The induced live settle pass voided 64 rows as 'player_dnp' and a large share of them are WRONG: the Jul 18 set is everyday starters (Freeman, Bellinger, Tucker, Chisholm, Conforto). They played. ROOT CAUSE — and my Phase 0 diagnosis was wrong. It is not DNP. The ledger row's game_date is WRONG. ledgerService derives game_date from the GRADE timestamp when the feed carries no game_time, and a 01:00/03:00 UTC snapshot is 21:00/23:00 ET the PREVIOUS day, so rows get labelled with the previous ET date. Verified against fresh season logs (cache disabled, so not staleness; found:true, so not name resolution): Freddie Freeman played Jul 17 and Jul 19 (x2, doubleheader) — NOT Jul 18 Steven Kwan played Jul 18 (x2) and Jul 19 — NOT Jul 17 Settlement was correct to find no game on the labelled date. My void logic then converted a data-labelling bug into destroyed results. FIX: never void on player-absence alone. Voiding now requires POSITIVE evidence — the games themselves postponed/cancelled. Absence returns 'unknown' (reason player_absent_unconfirmed), so the row retries and ages out to 'unrecoverable' at the cap. We cannot distinguish "did not play" from "mislabelled date", so we must not claim DNP. Both terminal states are excluded from the record denominator either way. Window-decay remains genuinely fixed (full season log vs a rolling window), and terminal states still prevent immortal rows. NOT DONE HERE: the 64 wrong voids are still in the table, and the game_date derivation is still wrong at the source. Both are reported for the table — no healing in this order. Suite 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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d4a6170ffa |
Settlement fix forward: date-targeted resolution + terminal states
Order 1 of 2. Push scoring UNTOUCHED — it is correct. No healing here.
PHASE 1 — DATE-TARGETED FETCH replaces the rolling window for settlement.
settleSource.resolveOutcome() resolves the SPECIFIC DATE and, when the
player is absent, reads GAME STATE to learn what the absence MEANS:
game final + player has a line -> SETTLE (a partial game is a real
result, never a void)
game final + player absent -> VOID (confirmed DNP)
postponed / cancelled -> VOID
scheduled / in progress / SUSPENDED -> PENDING (a suspended game resumes;
voiding it would destroy a real bet)
player played, stat missing -> unknown, NEVER void a real appearance
This is FREE for MLB: mlbStatsAdapter.getPlayerGameLog already returned the
full season log and getPlayerStats was discarding it with .slice(-10).
Settlement now reads fullLog — same request, same cache — which removes
window-decay entirely (the verified failure was a Jul 12 game outside a
last10 starting Jul 6). Projections keep using last10, unchanged.
PHASE 2 — TERMINAL STATES (migration 026 applied). outcome CHECK widened to
hit/miss/push/void/unrecoverable; added settle_attempts, settlement_source,
settlement_version, model_version. A row that cannot be resolved after
SETTLE_ATTEMPT_CAP (4) date-targeted attempts becomes 'unrecoverable'
rather than pending forever. CRITICAL: getModelAggregate now EXCLUDES void
and unrecoverable from the settled selection — it used
.not('outcome','is',null), so without this a void would have counted as a
settled row and silently moved the public record. Verified in the record
calc, not just the settle path.
PHASE 3 — SETTLEMENT-RATE ALARM. zeroSettleAlarm only caught a TOTAL zero
while ~30% of a slate failed quietly (Jul 17: 57/86). opsWatch
.settlementRateAlarm pages when resolved/attempted falls below
SETTLE_RATE_FLOOR (0.8). Voids count as RESOLVED — a void is a legitimate
terminal state — so healthy voiding never pages. Third silent-failure
surface of the night, now closed.
PHASE 4 — VERSION STAMPING. src/config/modelEras.js defines the cutoff
ONCE (2026-07-19T22:50:00Z); migration 026 backfilled pre-cutoff rows as
'pre-retention-unknown' (naming the uncertainty, not implying knowledge);
new rows carry model_version.
Regression caught pre-deploy: getScheduleFn was not injectable, so the
ledger suite hit the real network and HUNG. Now injectable via opts and a
no-op under NODE_ENV=test. The "no row -> pending" test was updated to the
new behaviour deliberately: a missing row on a FINAL game now voids.
Suite 281/3373 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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e46c88e364 | STATE: retention clock proven ticking via induced cron entrypoint | ||
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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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c2f6041406 |
STATE: off-box round trip CLOSED — 645 restored from the box copy
Phase 3 complete. The insurance chain is proven end to end rather than assumed: dump -> validated -> pushed off-box -> verified on the box -> pulled back down -> rebuilt into a live database. Pulled vyndr-20260720-051158.dump FROM the Storage Box (not the local copy) with the in-session key through the pinned host key, never bypassing StrictHostKeyChecking. Restored into scratch Postgres 17: 715 archive objects, 42 public tables, ledger_entries with all 27 columns and real spot-checked rows. ASSERTION PASSED: ledger_entries restored 645 == live 645 (target >= 645). model_snapshots restored 100/100, so the retention store shipped yesterday is covered by backups from day one. Records the operational gotcha the restore surfaced: the dump is written by pg_dump 17 (Supabase 17.6) and pg_restore 16 CANNOT read it — 'unsupported version (1.16) in file header'. The first attempt failed on exactly this. Any DR runbook must use PG17+ tooling. Restoring into vanilla Postgres also logs 12 ignored errors (Supabase roles/extensions absent locally) which are harmless. Scratch DB torn down, pulled copy deleted, both dumps still on the box, nightly cron untouched. No private key material echoed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |