b818626870cf3abbfb99c48433a15626aa110ce5
96 Commits
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 builtbykev
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b818626870 |
BUILD-STATE: under-querying vs out-of-data session
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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6452926732 |
Retain raw weather, and record platoon severity 48 rows short
Two fixes in the weather path, and the second was hiding behind the first. The scalar weather_mod cannot express a hit-TYPE conversion at all -- wind out and warm turning fly balls into extra bases, and cold heavy air turning them into outs, collapse to the same number once multiplied -- so the raw temperature, wind speed and wind direction are now retained alongside it. And the old guard only kept the environment when the multiplier was not 1, which silently discarded the forecast for every ordinary night. That is the majority of games, and precisely the rows a hit-type model would need in order to learn what ordinary looks like. Platoon severity is built and measured at n=452, which is 48 rows short of the gate: CANDIDATE_PENDING, neither proven nor theatre. It moves less than flat platoon (0.021 against 0.026), consistent with the pattern, and its Brier point estimate is favourable but the corrected interval still spans zero. Worth naming: the refusal costs sample, and that is the design working. Flat platoon scores 741 rows because it will happily apply a boost to anyone; severity scores 452 because the other 289 are hitters whose split we cannot actually read at 60 plate appearances on the short side. Buying those rows back by shrinking instead of refusing would have produced a number indistinguishable from a measured league-average split, which is a different claim from the one the data supports. Park dimensions are ingested and verified in production across fifteen venues, joined by the venue the game is actually at rather than inferred from the home team -- neutral-site and international games break that assumption without surfacing an error. The park-and-weather-to-hit-type atom is NOT built. Its inputs landed this session and carry a single as_of date, so testing it on total_bases would be scoring games with inputs that postdate them. Building it now would produce something plausible rather than something proven. Proven factors for hits remain pitcher_contact_profile and defense_by_direction. 4,307 tests green (344 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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20c45cbcd1 |
The causally-correct defence atom proves where the crude one did not
Kev's insight holds, and the data says so cleanly. defense_by_direction PROVES on hits -- n=528, Brier -0.0034, interval [-0.0059, -0.0009] at the 99.9% level the cumulative correction now demands -- while team-average defence remains not proven, its interval still spanning zero. Same signal, same rows, different unit. The detail worth keeping is that the causally-correct atom moves the number LESS THAN HALF as much as the crude one, 0.013 against 0.030, and is the one that is reliably right. The team average was moving more and knowing less. Big movement is not evidence of a good factor; it is frequently the tell. Both halves turned out to be free, as the order expected. Savant's batted-ball leaderboard carries pull/straight/oppo crossed with ground/air for 609 hitters -- the statcast leaderboard we already pull does not, it has nineteen columns and no direction at all -- and the OAA feed already carries each fielder's position, so per-position defence is a regrouping of last week's ingest rather than a new source. Verified in production: 609 spray profiles, 31 teams. Handedness is what joins them and getting it backwards would have been invisible. Pull for a right-handed hitter is the left side; for a left-handed hitter it is the right side. A model that ignored `bats` would send half the league's grounders to the wrong infielders and still look like it was reading defence, and nothing downstream would have caught it. Switch hitters bat opposite the pitcher, which this does not resolve, so they are unreadable rather than guessed. Unmeasured zones are renormalised away rather than contributing a zero, since a zero asserts an exactly-average fielder standing there, and coverage states honestly what share of a hitter's contact we could actually read. ATOM 2 is input-blocked rather than sample-blocked, and the distinction matters because waiting will not fix it. The weather free-source check passes -- Open-Meteo is already wired and exposes temperature, wind speed, wind direction and precipitation -- but those raw fields are collapsed into a single scalar modifier and wx_forecast is empty on all 1,119 settled rows. Park DIMENSIONS are not ingested at all; parkFactors holds coefficients, not wall heights or fence distances. A park-and-weather-to-hit-type conversion needs both, so it is scoped rather than half-built: retaining the raw weather fields is the cheap half, dimensions are the missing one. Proven factors for hits are now pitcher_contact_profile and defense_by_direction, both pooled; every per-archetype slot remains sample-blocked. 4,297 tests green (342 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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a9ee55550b |
Build the two-part factor gate: one factor proves, and zero are theatre
The question was whether the hit grade reads tonight's game or just says he is due. Answering it needed a gate that correlation cannot provide, because correlation cannot separate the two ways a factor looks alive: it reads the game, or it moves the number and reads nothing. The second is what a product ships by accident -- arch-v1 moved 76% of rows by 2.5 points, changed resolution by 0.0000, and was live for months, and no user could have told. So a factor must now clear both conditions: move the prediction off the player's own leave-one-out base rate, AND improve out-of-sample Brier. Brier rather than correlation, because correlation asks whether the ordering improved and this asks whether the NUMBER got closer to what happened -- and for a graded probability the number is the product. The correction applies to the interval itself, which turned out to matter more than expected. A plain 95% CI is the right bar for one test; at fifty cumulative tests roughly two or three intervals exclude zero by chance alone. Widening to 1 - 0.05/tests, currently 99.9%, flipped both defence and platoon out of "proves". A 95% interval would have shipped two unproven factors into the grade, with reasoning text explaining them to users. That forced a distinction I had initially collapsed. Defence and platoon have FAVOURABLE point estimates whose corrected intervals merely span zero, and calling that THEATER would repeat the error this codebase keeps correcting: insufficient evidence is not evidence of absence. THEATER is now reserved for its one real meaning -- moves the number, reads nothing -- and NOT_PROVEN_AT_CORRECTED_BAR names a real candidate held to a bar that rises with every hypothesis the programme tests. Result on 741 settled hits rows: pitcher_contact_profile PROVES, improving Brier by 0.0066 with a 99.9% interval of [-0.0114, -0.0016]. Defence (-0.0043) and platoon (-0.0039) are not proven at the corrected bar. Park is sample-blocked at n=405. Zero factors are theatre, which is the genuinely good news: nothing decorative is being wired. Per-archetype every slot is sample-blocked (BOMBER 252-294, GHOST 67-125). Two spec gaps worth recording. The approach identities the order names -- SPRAY, DAMAGE-DEALER, COUNT-WORKER -- do not exist in the registry; the MLB batter archetypes are BOMBER, GHOST, TORCH, BRUSH, DRIVER, FLEX, ALPHA, HYBRID and CATALYST. And parkFactors maps hits to run_base, so there is no hits-specific park factor at all: a park that turns outs into hits without producing runs is invisible to the input we have. The grade rescale is NOT run. It was explicitly gated on the factor proving, and one pooled factor worth 0.0066 of Brier is not a factor-informed distribution -- rescaling on it would dress a base-rate model as a matchup model, which is the exact thing this gate was built to prevent. 4,286 tests green (340 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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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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9c5b968351 |
chaining-v1: the portable chain, and the gate that blocks the parlay surface
The order's own prerequisite for the hit-parlay surface was to verify the hit probability is calibrated. It is not, and the failure is exactly the shape that destroys a parlay. Measured on 972 settled hits props: the model is monotonically over-confident at the top and flat above 0.70. Predicted 0.911 comes back 0.630. Predicted 0.844 comes back 0.630. Predicted 0.747 comes back 0.605. There is no discrimination at all in the range a parlay is built from, and the error runs in the flattering direction. Four "91%" legs are 0.686 by the model and 0.157 in fact -- a 4.4x overstatement that compounds with every leg added. Single props survive a calibration error of that size. A parlay multiplies it. So chainAcross REFUSES to compound atoms not marked calibrated, and refusing is the feature rather than a limitation: a ticket built on these numbers would be confidently wrong in the direction the user pays for. calibration.js provides the reliability table, the gate (tolerance 0.05, weighted to the high end because that is where tickets live) and an isotonic fit. Isotonic is the honest repair here because it is monotone: the model's ordering survives untouched while the numbers move to what actually happened. The fitted map says 0.65 -> 0.594, 0.85 -> 0.639, 0.91 -> 0.639. chain.js is the portable core -- base events plus context, through a chain function, into a PLUGGABLE aggregator: across players for a compound ticket, up to the team for expected scoring. The sport-specific parts are inputs rather than code paths, so basketball plugs in as content. The archetype redistribution hook is there now, dormant in baseball because a nine-run lead does not change who bats next, and live in basketball where a blowout fades the star and feeds the bench. Two judgement calls worth naming. Treating same-game legs as independent errs in the FLATTERING direction, since they share pitcher, park and weather -- so correlation shifts the compound toward the weakest leg, bounded, and is labelled an approximation rather than a joint distribution. And market divergence does NOT downgrade confidence: it flags a contested script whose props are either the best or the worst on the board, and which one is unknown until settled. Internal inconsistency does downgrade it, because per-entity reads failing to sum to the team read means one of them is wrong and we do not know which. Not built: the independent game-script projection. It needs proven team-level atoms and out-of-sample validation against actual margins, and no atom has passed the gate yet. Building it now would produce something plausible rather than something proven, which is the failure mode this whole programme exists to avoid. 4,269 tests green (339 suites); web build exit 0; 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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a80a775fa0 |
Record the lineup-context ingest: prod-verified, 153 lineups / 149 opportunity
Verified in production via the new on-demand endpoint: 153 batting-order rows across 10 games (orders 1-9), and 149 hitter-opportunity rows with RISP shares ranging 0.170 to 0.528. The data passes its own coherence check on arrival: the highest RISP-share hitters all bat fourth and fifth, which is exactly where the mechanism says the RBI opportunity lives. Nothing was fitted to produce that -- it is the two tables joining and agreeing. 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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ff037e40c2 |
Re-adjudicate: nothing to demote, and close the hole that would have mattered
There is nothing to re-adjudicate. The proven set is empty and always has
been -- verified three ways: proven-status reports EMPTY, validatedSkills()
returns {} for every archetype, and zero conditioning entries have ever
reached PROVEN. The one PROVEN feature is recent_frequency_prior, which is the
incumbent counter itself, proven by the S78 ablation as ~100% of the
champion's resolution. It is the baseline every challenger is measured
against, not a conditioning interaction, and demoting it would leave the model
with nothing to grade from.
A correction to the premise: the cumulative gate did NOT catch a false
positive last session. It caught nothing, because there was nothing in the
proven set to catch. What it did was tighten alpha from 0.0026 to 0.0013
within one session, which demonstrated the mechanism working rather than a
demotion. So steps 3 and 4 -- demote, recalibrate -- are vacuous here, and
readjudicateAll says so plainly rather than glossing a no-op.
But the worry behind the order was well founded, and the audit found the real
exposure: promote() did not require the cumulative denominator. It checked n,
lift and CI, and nothing stopped a future session from testing eight
hypotheses, correcting by eight, and promoting on a p-value that would not
survive the programme's real denominator. That is precisely the hole that
makes a retroactive re-adjudication pass necessary later, so it is closed at
promotion time instead. isSufficient now refuses evidence carrying no
correction, evidence corrected against fewer tests than the cumulative count,
and any p-value that does not clear 0.05 over its own test count. The same
rule guards a PROVEN conditioning entry.
The second audit found two of four analysis scripts still correcting
per-session; pitcher-prove-k and tb-solo-and-interactions now use the
cumulative ledger, so the correction is native on every path.
reAblation.js is the standing second line: pure and injectable, so the
decision rule cannot drift from the gate's, and every verdict records both
p-values and both test counts so a demotion is re-derivable by anyone. A
feature promoted at alpha 0.05/20 can demote on the same p-value once the bar
is 0.05/60 -- correct, because the bar rose only after the programme had more
chances to get lucky. No fresh measurement is PENDING_RETEST and never a
demotion: absence of a re-test is not evidence, and demoting on it would
punish whichever stat happens to be off-season.
Net effect on the proven set is zero. No demotions, no recalibrations, and no
public ledger event -- announcing "recalibrated after re-adjudication" when
nothing changed would itself be a false signal of rigour.
4,238 tests green (337 suites); web build exit 0; counter 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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ece2b9f5f9 |
Ingest defence, and make Bonferroni cumulative across the programme
Two things shipped that stand regardless of sample. DEFENCE. Statcast Outs Above Average is free on the host we already pull six feeds from, so there was nothing to decide. 514 fielders, aggregated to team level -- the unit a batter's prop actually needs, the defence behind the pitcher he faces -- and persisted as 31 team rows. Verified in production. Cubs +56 best, Mariners -29 worst. Unknown is not zero, and it bites unusually hard here: an OAA of 0 is a REAL reading meaning exactly average, so coercing absence to 0 would assert that every unmeasured fielder is league-average, which is the commonest defensive profile there is. team_defense also carries as_of_date in its primary key from the first row -- statcast_aggregates was built upsert-in-place and that silently made every backtest leak the games it predicted, so point-in-time is available here before it is needed rather than after a wrong answer. A bug worth recording as a class: BASE already ends in /leaderboard, so the new feed built a doubled path and 404'd. Because a failing feed degrades to an empty index by design -- correct, so one broken source cannot fail the whole pull -- it surfaced as "fielding_oaa: 0 rows", which reads exactly like "Statcast has no fielding data". Graceful degradation makes a wiring bug look like an honest absence. CUMULATIVE CORRECTION. Bonferroni had been applied per session throughout: a run testing eight features corrected by eight. Across a programme's lifetime that is wrong in the dangerous direction, because every order gets a fresh generous alpha and the false-positive rate compounds quietly. Correcting by 8 when sixty have been tried is how a noise result eventually gets recorded as PROVEN with a p-value to point at. The denominator is now distinct hypotheses ever tested, persisted, and it moved 19 -> 38 within this session alone, alpha 0.0026 -> 0.0013. Re-tests deliberately do not inflate it: re-asking the same question on more data is not a new shot on goal, and counting it would punish the discipline of waiting for sample. THE MEASUREMENT. The differential the theory predicted is present: defence correlates with the counter's residual at +0.130 for GHOST, the contact and speed archetype, and -0.018 for BOMBER, the power archetype. A GHOST's hits depend on whether anyone can range to the ball; a BOMBER's barrels clear the defence entirely. So a flat BOMBER result is the theory working rather than the test failing. It is not a result. GHOST is n=104 against a 500 bar, with p=0.188 against a corrected alpha of 0.0013 -- three orders of magnitude short. Both are recorded as CANDIDATE with their measured lift, tagged contact-skill, so the re-run at full sample compares against a recorded baseline. Nothing proved, so nothing was recalibrated and nothing shipped. 4,228 tests green (336 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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ac1361486e |
Build the conditioning registry, and a probe so "proven" stops drifting
The order opens with "two proven clusters live". They are not proven -- the
proven set is empty -- and this is the fourth consecutive order to start from
a stronger claim than the measurements support. Correcting that in prose four
times has not worked, so this session adds scripts/proven-status.js, which
recomputes the answer from the ledger: hits LOSES (-0.096, CI excluding zero),
total_bases INCONCLUSIVE (+0.004), strikeouts INCONCLUSIVE (+0.259 at n=57).
It deliberately reports sample readiness separately from recorded verdicts, so
"n>=500" can never again be read as "passed".
A counting error worth recording. The first read of the top-volume archetype
said BOMBER x hits was 641 rows -- gate-ready. It is 287. model_snapshots
holds one row per prop PER SNAPSHOT CYCLE, so joining it to ledger_entries
counts each ledger row once per cycle it appeared in. Deduping on the ledger
row id gives the true figure, and my own status script had the same bug until
it was fixed. That is the difference between running the gate and being short
by 213.
So no archetype x stat combination reaches the gate. BOMBER x hits at 287 is
the closest; pitcher archetypes are untestable at 58 settled strikeout rows
across all of them, so the pitcher half of this order could not be run.
The registry is built: recordConditioning keys archetype x underlying-skill x
interaction x status with measured lift, and the skill tag is MANDATORY and
enforced -- untagged entries are refused, and PROVEN without sufficient
evidence is refused. validatedSkills() returns the coherent profile as it
stands, which is {} for every archetype, by design.
BOMBER x hits conditioning was tested across the order's categories and every
result is underpowered: arsenal (barrel x breaking share) incremental +0.043,
batted-ball (launch x pitcher GB) +0.001, contact quality -0.020 and -0.015,
K x K -0.063. Within BOMBER the counter still leads on hits, 0.218 to 0.160,
consistent with the closed pooled negative.
One bug fixed mid-run: fromStatcastRow maps percentage and raw fields only and
does not carry pitch_mix, so the arsenal category first reported n=0 for every
row -- it was measuring nothing rather than failing. Without catching it,
"arsenal doesn't matter" would have been recorded from a column that was never
populated.
On defense: I looked for a derivable proxy before calling it unsourceable, and
there isn't one. We ingest no fielding data at all, and opposing pitchers'
hits-allowed conflates pitching with defense, so it would validate the wrong
skill. It needs Savant's fielding endpoint -- free, same host as the five
feeds already ingested -- and it is not sourced here, because sourcing it to
test at n=282 would answer nothing.
Nothing proved, so nothing was recalibrated and nothing shipped.
4,221 tests green (335 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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9538e11198 |
Derive the lineup K-rate free, and fingerprint the cap fix
Two premise corrections first. Pitcher stuff features have NOT proven solo through the gate -- every one was refused on sample (n=57 against 500). Four exceed the effect-size bar (arm angle -0.250, whiff +0.213, k rate +0.206, chase +0.195), which is why they are worth pursuing, but clearing one of three thresholds is not passing. And the carrier was not blocked only on the lineup input: that input was built and measured last session at 94.7% coverage. What blocks it is n, and n was being throttled by the grading cap. RUNG 1 IS DERIVED AND COSTS NOTHING. Opposing-team K-rate comes from joining the opposing roster to the batter k_pct values already in statcast_aggregates -- no new feed. The improvement this session is that it is PA-WEIGHTED: an unweighted roster mean counts a 12-PA callup the same as an everyday starter, which is not the lineup a pitcher faces. That change alone reversed the term's sign. Unweighted, the lineup term HURT the model (0.1738 -> 0.1285). PA-weighted, it HELPS (0.1738 -> 0.1953). Same hypothesis, same data -- the derivation was the problem, not the signal, which is the entire argument for deriving the best honest version before sourcing anything. Head-to-head is now +0.2592 with a CI of [-0.0167, +0.5645], very nearly excluding zero, at n=57. Within archetype, the two strata come out with OPPOSITE signs -- FLAME incremental -0.152, non-FLAME +0.145 -- and the pooled value (+0.077) sits between them, which is the shape a conditional effect makes and is invisible when pooled. That is what stratifying was for. But n is 20 and 24, the standard error on a correlation there is about 0.22, and the direction contradicts the theory that predicted a stronger effect for finesse arms. It is recorded as a structure to re-test, not as a finding. Rungs 2 and 3 are NOT triggered. A rung fails only once it has been fairly tested, and Rung 1 is n-blocked rather than failed. Sourcing confirmed lineups now would be paying for precision on top of a proxy we have not yet measured. THE RESULT THAT DECIDES THE TIMELINE: yesterday's cap raise is fingerprinted in production at 907 grades per snapshot, up from 334, with strikeouts going 6 to 17. That puts n>=500 for pitcher Ks about a week out instead of three months. Operational note: the manual internal snapshot endpoint now 524s at the Cloudflare edge because grading the full board exceeds 100s -- the run still completes server-side (this very snapshot was written by a 524'd request) and the cron is in-process, so a 524 there is not a failure. Nothing proven, nothing calibrated, nothing shipped. The counter remains anti-predictive on strikeouts at -0.064 and the skill model leads it by 0.26. 4,221 tests green (335 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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843c8c6d4b |
Build the pitcher engine, and find the cap was eating the whole board
Strikeouts are NOT proven -- n=57 against a bar of 500. But the finding that matters is not a correlation. THE CAP. Measured on the live slate via the refusal diagnostic: 1,244 unique gradeable props exist, the 500 cap graded about 334, and because dedupeProps takes first-row-wins in FEED ORDER, what survives is decided by feed position rather than value. Pitchers are 2.6% of a batter-dominated feed, so we were grading SIX strikeout props a slate against 32 available -- putting n>=500 three months away for every pitcher stat. Pitcher props were never being refused (graded 5, refused 0, suppressed 0); it was truncation. Raised 500 -> 1500 on measured cost: 721ms per prop at concurrency 5 is about 179 seconds for the full board, against a cron that runs five times a day and a fire-and-forget caller that never holds an HTTP response. statsapi is free and unlimited. Concurrency stays at 5 -- one variable at a time. This unblocks every n-blocked stat in the programme, not just pitchers. THE ENGINE. pitcherEngine.js is its own engine, not the batter engine pointed at pitchers: the batter model asks whether contact becomes a hit and reads contact quality, the pitcher model asks whether the plate appearance ends without contact at all and reads stuff. Archetypes are FLAME (whiff-led), SCALPEL (chase-led), SINKER (pitches to contact) and DEFAULT, and a test asserts the weight keys are not the batter engine's. The projection is K% by log5 against THIS lineup, times batters faced, through a binomial. An unclassifiable arm gets the balanced map, never a guessed archetype. THE MEASUREMENT, at n=57 and contaminated. Four solo features clear the 0.15 effect bar and fail only on sample: arm angle at -0.250 -- the largest correlation measured anywhere in this programme -- then whiff +0.213, k rate +0.206, chase +0.195. The batter cluster's best was 0.135. Head to head, pitch-v1 resolves 0.1285 against the counter's -0.0639, delta +0.192 with a CI spanning zero. That negative is the interesting number. The counter is ANTI-PREDICTIVE on strikeouts: counting a pitcher's recent Ks is worse than useless, because his recent totals track which lineups he drew and how long he was left in rather than his skill. It is the one stat where the incumbent has no defensible edge. A bug caught on the way. resolveTeam wants an abbreviation and the game log supplies full team names, so the roster join silently resolved nothing and the first run reported 0% lineup coverage -- the theorized stuff x lineup carrier was never being tested, not failing. Fixed; coverage is now 94.7%. The carrier still shows no incremental signal over whiff alone, and adding the lineup term lowered head-to-head resolution, which is recorded rather than dropped. Calibration was not reached: nothing passed the first bar. The batter model and the counter are byte-identical, verified by diff. 4,221 tests green (335 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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c0621e7aa2 |
Measure the batter cluster: the proven set is empty, and hits is closed
PREMISE CORRECTION FIRST, because it defines the bar. total_bases has not
passed BAR 1. Its head-to-head is inconclusive at parity -- delta +0.004 to
+0.007 with a CI spanning zero -- and it is contaminated, and no feature of
its passed the gate. It was described last session as the first challenger
that did not LOSE, which is not the same as proven. If it is installed as the
frozen proven reference and every other stat is held to "the identical bar
total_bases cleared", the bar becomes "be inconclusive at parity" and the
whole cluster passes on a null result. The proven set is EMPTY.
HITS IS NOW A FINAL ANSWER. At n=803 it clears the gate's sample requirement,
so its features were properly TESTED rather than refused: every one fails on
effect size (max marginal |r| 0.053 against a 0.15 bar), every interaction's
incremental contribution collapses to about zero, and the model loses
head-to-head by 0.096 with a CI excluding zero. That is a well-powered
negative and hits should be closed rather than retried.
The rest are n-blocked: total_bases 383, rbi 391, home_runs 228, runs 188,
against a bar of 500. Two leads are worth carrying. home_runs barrel rate has
a marginal r of -0.135, and the sign matters -- higher barrel rate goes with
the counter OVER-predicting, which would be a correction rather than a new
predictor. And runs batterK x pitcherK has the largest incremental in the
cluster at +0.132, with a clean mechanism: strikeouts destroy plate
appearances, and a PA that never happens cannot score.
RBI deserves a caveat rather than a verdict. It is power times OPPORTUNITY,
and we ingest no baserunner state at all, so half its mechanism is missing. A
weak RBI result is evidence that we are modelling half the stat.
total_bases was held frozen: git diff on skillProjection against the prior
commit is empty. The counter is untouched.
Also fixed and verified in production: the point-in-time retention shipped
after yesterday's refresh had already run, so statcast_history was empty, and
its first run then failed on a hand-enumerated schema that had already drifted
from its source ("could not find the 'swing_pct' column"). The refresh itself
still succeeded and wrote all 1,387 aggregate rows, which confirmed the
best-effort guard in prod. The table now mirrors the source via LIKE and the
writer passes rows through whole. Verified live: 1,387 rows retained at as_of
2026-08-03. A usable point-in-time window starts 2026-08-04.
Stage B has nothing to calibrate. Everything now waits on a point-in-time
window and on sample -- both waiting problems, not building problems.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9
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builtbykev
|
4aab18096f |
Prove both on total bases -- and find that my own fix destroyed the backtest
Nothing passed. Nothing promoted. Counter byte-identical. THE BLOCKER, which is the real finding. statcast_aggregates is upserted in place and holds exactly one as-of date. Yesterday's skill backtest was honest only by accident: the nightly refresh was unreachable code, so the profiles sat frozen at 2026-07-21 -- before the settled window. Repairing that cron was right for production and it refreshed them to today, destroying every prior version. Scoring a 2026-07-25 game now uses a season aggregate that contains that game. Point-in-time validation is structurally impossible from that table, so every number in this run is contaminated and directional, and none of it is a gate verdict. Fixed forward: statcast_history retains a dated snapshot on every refresh, so point-in-time becomes "as_of_date < game_date, most recent". Retention is best-effort and cannot fail the refresh; both properties are unit-tested. It has one day of data, which is not yet a window. SOLO BASELINE, n=383, Bonferroni across 12 tests (alpha 0.00417): nothing passes. hard_hit_pct is closest at marginal r 0.135 with p 0.0080, failing both the 0.15 effect bar and the corrected alpha. And it drifted DOWN from 0.153 at n=295 -- an estimate regressing as noise averages out, not an effect firming up. I called that number encouraging yesterday; on 88 more rows it is fading, and it should not keep being quoted at its best value. INTERACTIONS, each scored by partial correlation against the counter residual controlling for both of its own components: none pass. Only barrel x power archetype has an incremental exceeding its parts (-0.101 against 0.019) at n=260 -- the shape Discipline 2 predicts, but a lead, not a finding. A methodological catch worth keeping. The archetype conditioner was first built as barrel_pct over league barrel -- a monotone transform of one of its own components -- so the "interaction" was barrel squared, measuring nonlinearity in barrel rate rather than any archetype effect, and it produced this run's only positive result. A Gauss-Jordan pivot test does not catch that, because the two columns differ by a scale factor. Fixed with a scale-free collinearity check plus real archetype labels joined from model_snapshots. Without it this document would have reported a fabricated interaction as the session's finding. COMBINED vs COUNTER on total bases: 0.2718 against 0.2647, delta +0.0071, CI [-0.065, +0.079] -- inconclusive, and the first time a challenger has not lost. The same engine on hits was -0.116 with a CI excluding zero. That contrast is the whole argument for total bases, and it is what the physics said: contact quality governs extra bases, not whether a grounder finds a hole. Also built: the compound TB projection. skillProjection no longer refuses total bases -- a deterministic bases-per-hit multiplier had made P(TB>=2) exactly P(hits>=1), a relabelled hits curve. It is now a convolution over per-PA base outcomes with hit-type shares shifted by skill. Non-degeneracy is locked by test. 4,204 tests green (334 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
|
c7cc8f5e52 |
Build the gate, run it, and find we were proving things on the wrong stat
PREMISE CORRECTION FIRST. statModel.js and correlateValidator.js do not exist in this repository. The validation spec's only prior form is src/services/python/blueprints/unconventional.py -- a Flask blueprint in the Python service that is offline in production, scoring NBA factors against a warehouse that was never populated -- and tests/unit/supplementSystems.test.js requires only fs and path while defining its own validateFactor inline at line 368. Those tests assert a re-implementation of the thresholds, not an implementation, which is exactly why they passed for months while nothing was connected. The diagnosis behind the order is right -- every challenger was measured without a gate -- but the cause is that there was no gate on the Node side to import. So it is built, to the exact spec. correlateValidator: n>=500, |r|>=0.15, p<0.05, Bonferroni across the sweep. The p-value is exact rather than approximated (t-transform through a regularized incomplete beta) and is verified in the suite against known values, because scipy is not available here. Pairs with an unknown side are dropped, never zero-filled -- a zero-fill inside a correlation does not add noise, it invents a point at the origin. THE RUN, hits, n=570, Bonferroni-8: every skill feature fails, and not narrowly. The strongest marginal correlation against the counter's residual is 0.062 against a 0.15 bar. That is an effect-size failure at a sample that would have found a real effect comfortably -- a clean, well-powered negative. The head-to-head agrees: value engine 0.0499 against the counter's 0.166, delta -0.116 with CI [-0.189, -0.043]. Not promoted. THE RUN, total bases, n=295: cannot be tested, and that is the finding. hard_hit_pct shows a marginal r of 0.153 -- above the threshold -- and exit velo 0.124, refused solely because n is 205 short of 500. It is the most encouraging number this work has produced, and it is what the physics predicts: contact quality governs extra bases, not whether a grounder finds a hole. We have been testing skill inputs on the one stat where they should not matter much. Two things the run forced. Feature verdicts are now PER STAT, because marking these DEAD sport-wide on hits evidence would have killed, for total bases, the features that look most alive there -- per-sport doctrine one level deeper. And the gate now reports r and p even when underpowered, because "not enough data yet" and "nothing here" demand opposite decisions and a bare refusal was hiding the best signal on the board. Next: build the compound TB projection (skillProjection still refuses total bases by design, since a deterministic bases-per-hit made P(TB>=2) identical to P(hits>=1)), accrue to n>=500, re-run this gate. Leave hits alone. 4,200 tests green (334 suites); web build exit 0; counter 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
|
258d8a6655 |
The skill engine: built, gated by construction, and Stage A honestly lost
Built src/services/model/ -- the forward, archetype-selected, skill-based projection, as a challenger. The champion is untouched. featureRegistry makes "earn its place or it's out" structural rather than aspirational: CANDIDATE / PROVEN / DEAD per feature per sport, liveFeatures() returns PROVEN only, promotion requires n>=200 with positive lift and a CI excluding zero, and there is deliberately no override argument. It ships with exactly ONE proven feature -- the incumbent counter, because it is the only one with a measurement. A test asserts that with only PROVEN features allowed the projection returns null, so an unproven model cannot reach a user by accident. The three champion adjustment layers are registered DEAD with their reasons so they cannot be silently rebuilt. skillProjection is a PA outcome tree: K and BB combined by log5 odds-ratio against league (both identities unit-tested), then archetype-weighted contact quality against contact allowed, then Binomial(PA, p_hit) mixed over a PA distribution. Archetype is a FEATURE SELECTOR, not a nudge -- BOMBER reads barrels at 0.50 and ground-ball speed at 0.00, GHOST inverts it -- and a test locks that the same hitter read two ways moves more than 0.15. STAGE A: IT LOSES. Out-of-sample on 570 settled hits props with 91.9% opposing-pitcher coverage, resolution 0.0499 against the champion's 0.166, delta -0.116 with CI [-0.189, -0.043]. It is not selective either: its eight most confident picks hit 50%, a lift of -0.065. Not promoted. The gate did its job on its first real test, which is the point of having built it that way. Two false starts, both recorded because they nearly produced a wrong verdict: statcast_aggregates stores PERCENTAGES, so raw rows made bip = 1-29.6-17.1 and refused 568 of 576 -- the honest-absent guards made a units bug loud instead of silent, and the conversion now lives at one chokepoint. And the first run resolved an opposing pitcher for 1 of 570 rows, because ledger team/opponent are NULL, so it would have reported "skill-v1 loses" while measuring a batter-only model with no matchup in it at all. The verdict above is from the corrected run. The loss is real but partial: park was passed as 1.0, handedness and opportunity_drift never fired, PA is season-PA over a constant, and the skill profiles carry no recency at all while the champion has a last-5 term. Also fixed: the Statcast nightly refresh was unreachable code. It sat inside tick() below "if (!HOURS_UTC.includes(h)) return" while testing h === 11, so it had never run once; the aggregates were 13 days stale and both of its alerts were in the same dead branch. It now runs on its own tick, and the test that passed happily throughout -- it only checked the string existed -- is replaced by one that asserts it is not behind the guard. 4,182 tests green (333 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
|
c551bf0340 |
Reality assessment: the forward model exists, wired to the wrong side of the pipe
READ-ONLY. src/ and web/ untouched. Inventoried every forward-model component against the real objective -- a forward matchup projection, not an edge number. The finding is that all of it already exists and is already loaded in production, and 100% of it sits DOWNSTREAM of the grade in challenger columns nothing serves. The served p_win reads three features and a game log; it has never seen a pitcher. Inputs are HAVE, not missing: statcast_aggregates carries 1,354 rows (750 pitchers, 604 batters) with exit velo, launch angle, barrel, hard-hit, whiff, chase, pitch mix, GB/FB, arm angle, and handedness complete on every row. Real gaps are team defense and catcher/umpire. So Stage A is a plumbing-and- modelling job, not a data-acquisition job. Found along the way: the Statcast nightly refresh is unreachable code. tick() returns for any hour not in HOURS_UTC (14,19,22,1,3) and the refresh block then tests h === 11, which that guard can never admit. The mechanism data has been frozen at its 2026-07-21 backfill for 13 days, and the block's own failure alert sits in the same dead branch -- the identical silently-guarded- out shape as the settlement outage. Design shows the counter: every factor label the SIGNAL BREAKDOWN renders is a restatement of recent frequency (l5_hot_vs_line, l20_over_line, back_to_back, home_game) plus several structurally-NBA labels (referees, coach pace, starters out) inside a baseball product. Not one names a pitcher, pitch type, handedness or park. The card's forward-read slots already exist and go unfilled -- the surface needs feeding, not redesigning. On what changes: the prior measurements were outcome-accuracy, not edge, so the metric was right and the question was narrow. proj-v1.1 and hits-v1 stay correctly refuted as DISTRIBUTION swaps on thin inputs -- neither tested a matchup-fed projection. arch-v1 is a market-relative nudge by construction and is the one component genuinely measured on the wrong axis. AT CEILING is provisional: measured only against features the champion already reads. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
|
d8bf7765db |
Decompose the champion: its whole edge is a hit-rate counter
READ-ONLY. src/ and web/ untouched; 4,159 tests still green. WHAT THE CHAMPION IS. probabilityEstimator is five lines of arithmetic: the empirical frequency of (stat > THIS line) over the game log, blended 0.6/0.4 with the last-5 frequency, then +/-0.03 opponent, +/-0.015 home/away, a cv>0.40 pull toward 0.50, and a clamp to [0.10, 0.95]. It reads three features. featureCache retains a dozen more that p_win never touches. THE ABLATION IS EXACT, NOT A REFIT. Every adjustment is closed-form from stored features and the consistency step is linear, so each layer subtracts algebraically out of the stored p_win -- no re-estimation, no re-fetch, no lookahead possible. Per stat, paired bootstrap: removing ALL THREE adjustments changes resolution by NOTHING on every stat hits -0.0059 total_bases -0.0015 rbi +0.0106 runs +0.0130 walks +0.0008 and rbi's home/away is mildly HARMFUL (+0.0053, CI excludes zero). So ~100% of the champion's resolution is base+recency: how often this player has cleared this number lately. Everything else is decoration. A CORRECTION. Pooled, the champion resolves 0.46; per stat it is 0.196 (hits) to 0.499 (rbi). Pooling stats with different base rates inflates correlation, so 0.46 should not be quoted as the champion's resolution. Last session's paired differences remain valid; only the absolute level was inflated. THE BIGGEST LOSS IS NOT A MISSING FEATURE -- IT IS THE CLAMP. 358 of 1,741 settled rows (20.6%) sit on the boundary, so the model emits a constant there and cannot rank a fifth of the book at all. And that constant hides two opposite failures: 0.900 covers home_runs-under truly winning 99.5% (9.5pts under-confident) next to hits-under truly winning 51.9% (38.1pts over- confident). PROB_CEIL=0.95 makes the 99.5% case inexpressible. Global over-prediction is +3.5pts, +7.6 on total_bases. None of this needs new data. ONE REAL MISSING-WEIGHTING LEAD: opportunity_drift, residual corr +0.156 on hits and +0.145 on total_bases -- it REPEATS across independent stats, unlike the weather hits on TB which sit inside the expected false-positive count (70 tests at alpha .05 expects 3-4). And we already compute it: arch-v1's opportunity axis uses it and extracts nothing (delta +0.0001). Wrong implementation, not a missing feature -- opportunity must scale the rate, not nudge the probability. ARCHETYPE IS UNMEASURABLE, NOT REFUTED. Only 2 of 41 labels (BOMBER, GHOST) reach n>=40 settled rows and every mean residual straddles zero. That is "we have not measured it", and it does not license acting in either direction. Why every challenger has failed is now legible: the ladder and hits-v1 REPLACE the frequency question with a fitted distribution; the environment axis adds inputs the champion ignores. Asking the frequency question at the traded line is the thing that works. Flagged, not fixed: model_snapshots.outcome is NULL on all 22,032 rows -- the retention table built for exactly this replay was never settled, so labels had to be joined from ledger_entries. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
|
3ba3dd28f3 |
Scoreboard every challenger; diagnose the 429 as odds-api, not PropLine
PROMOTE-THE-EARNED. Nothing was promoted, because nothing earned it -- not because the bar was held high. Measured on the same bar that refuted hits-v1: own rows only, direction-aligned, paired bootstrap, promote only on a CI excluding zero. arch-v1 n=1741 delta 0.0000 CI[-0.0050,+0.0054] inconclusive contact-v1 n=1055 delta +0.0008 CI[-0.0052,+0.0069] inconclusive proj-v1.1 n=1664 delta -0.0301 CI[-0.0543,-0.0060] reliably WORSE matchup/tb-v1/hits-v1 n=0 genuinely pending (rows dated 08-02+) arch-v1 is the interesting one: it MOVED 76% of rows by 2.5 points on average and resolution is identical to the champion to four decimals, on the moved rows too. That is active movement carrying no information -- a finding, not a pending verdict. These are true prospective holdouts: arch-v1 and contact-v1 wrote p_win at grade time into their own columns before the game. Nothing recomputed. THE 429, read-only. The premise was that we re-pull the full picture every slot and blow the quota. Measured: PropLine is at 5 calls of 3,000/day -- 0.17%. One snapshot is ONE PropLine call per sport, all markets comma-joined. There is no request-pattern problem, so a change-based pull cannot fix it and no tier upgrade is needed. The 429 is odds-api: 478/500 MONTHLY, blocked at 95%. oddsService falls through silently when PropLine returns empty, and the backup's quota gate throws the error -- so an empty slate is indistinguishable from an outage and the message names the wrong provider. Flagged for its own order. Could NOT verify PropLine movement endpoints: docs are auth-gated and the keys are production-only. Not asserted either way. The movement-as-data argument stands on its own merits and should be justified that way, not as a quota fix it isn't. Book-breadth invariant written down: we never discard books. All are kept and shown (DISPLAY_BOOKS = MODEL + REFERENCE + DFS); DFS pick'em is excluded from PRICING only, because a fixed-payout shaded number is not a market price. Verified this is already what bookRoles.js does. Champion byte-identical; every challenger stays wired. 4,159 tests green (332 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
|
07626de3de |
hits-v1: built on the right structure, measured honestly, REFUTED
Hits was diagnosed as a family mismatch: 84% of hits rows trade at 0.5, so the stat rides on P(0), and a negative binomial has unbounded support and no notion of opportunity at all. hits-v1 models it as the bounded conversion it is -- N ~ the player's empirical at-bat distribution, hits|N ~ Binomial(N,q), with the multiplier scaling q (conversion) and never N (opportunity). STEP 0 confirmed the inputs before the model existed: 30/30 real ledger players, 100% combined-input coverage. Every read goes through knownRate -- a row with no atBats is dropped, never counted as a 0-at-bat game. It FIRES: 158/159 hits props (99.4%) on the live production snapshot, through the real attachProjection path. Scoping by book IDENTITY rather than price shape kept 94 out-of-promotion-band props on the board, 93 of them modelled -- 59% that a price rule would have deleted. And it LOST. Point-in-time replay (game log truncated strictly before each row's game_date, real grade-time multiplier), hits-only, direction-aligned, n=242: resolution champion 0.195 / ladder 0.048 / hits-v1 0.026. Paired bootstrap on the same rows: hits-v1 - ladder = -0.022, CI95 excluding zero. Not promoted. The value is in what it eliminates. The family was wrong AND the mean was not the constraint -- hits-v1 moved the line-0.5 mean 0.554 -> 0.581 toward a 0.598 base rate while resolution fell. What is left is per-prop discrimination: the ladder's inputs, not its distribution. The pre-registered fallback is recorded as WRONG rather than deleted. It said hits might be genuinely low-resolution for anyone; the champion scores 0.276 on the identical 189 rows, so there is real signal and the ceiling claim was the comfortable reading, not the honest one. Its own control refuted it, and that control was already in hand when the branch was written. hits-v1 stays wired as a challenger writing its own ledger columns so the forward accrual can confirm the backtest. Champion, ladder, ranking, calibration, reference ruler and the four accruing verdicts are byte-identical -- the diff has zero deleted lines. Tests 4,156 green (332 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
|
b5d3fd14bb |
S11 (a1): live tracking — the read locked, the game watched
MLB statsapi + WNBA ESPN live boxscores -> per-player current values
(live:{sport}:{date} TTL 90s, /api/live/:sport + Next proxy). Pure
propState math (HIT / ON PACE / NEEDS N / HOLDS / LINE PASSED — never
red in-progress), attachLiveProgress strip join on nameKey+statType,
proximity-to-hit slate float, StatStrip LiveTracker in the ROW-GRAMMAR
outcome slot (spec amended + lock test updated). Grades never change
in-game — tracking, labeled as such. 2698 -> 2757 tests, web build 0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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builtbykev
|
d3637e7abd |
S6 (a1): display — the full picture under the grammar
- specs/ROW-GRAMMAR.md: the row grammar law (slot order, color law, mark
law, mobile stacking, no-truncation), locked by tests/unit/rowGrammar.
StatStrip violations fixed: MovementChip before the grade (market
context before model output); ViabilityChips after the archetype
(identity is one contiguous run).
- Line-movement sparklines: intradayRefreshService.trackHistory captures
real {t,line} points per grade (seeded with the lock, deduped when
flat, capped 24) inside the snapshot write-back; StatStrip.LineSparkline
renders at >=3 points (green toward / amber against / dim flat).
- Last-10 dot strips: services/last10Dots (streaksService accessors) ->
/api/snapshot/:sport attaches last10_dots from rosterlogs:{sport};
StatStrip.DotStrip renders vs the LOCKED line, newest first.
- CLV distribution: getModelAggregate emits clv_distribution (7 signed
buckets, outliers clamped) only past the centralized n>=20 gate;
ledger MODEL header renders the bar strip.
- Global search: SearchModal (cmd-K via GlobalHosts + window.__search),
players via /api/players/search per sport + static lib/teams.js
(soccer deliberately absent); Nav search icon + Search first in the
mobile More sheet. Explore tab untouched.
- Landing LCP: fade-up floored at opacity .6 (hero h1 contentful on
first frame, S33 visible-floor rule) + IBM Plex Mono preload:false
(4 decorative font files off the slow-4G critical path).
2654 -> 2698 tests (226 suites) green; web build exit 0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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builtbykev
|
1d46b446c9 |
Merge S10 (a1): public ledger profiles v1
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> # Conflicts: # BUILD-STATE.md # CLAUDE.md |
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builtbykev
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b20145c215 |
S10 (a1): public ledger profiles v1
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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builtbykev
|
4e49ee0990 |
S9 (a1): slip reader — zero-API OCR
tesseract.js (self-hosted WASM, Apache-2.0) + pure per-book layout parsers (DK/FD/MGM/Caesars) with per-field confidence and needs_review honesty — the reader never guesses. POST /api/slips/parse (auth, free 1/day paid 10/day, 4MB cap) + Next proxy. Gated /slip page: upload or paste, manual-correct UI, per-leg grades through the normal engine (refusals render honestly), add-all to Parlay Lab, share card. Vision model upgrade logged post-revenue. 2574 -> 2608 tests. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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builtbykev
|
aaafc3e0f2 |
Merge S8 (a1): ops — the product watches itself
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> # Conflicts: # BUILD-STATE.md # CLAUDE.md # src/snapshotScheduler.js |
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builtbykev
|
044941d406 |
S8 (a1): ops — the product watches itself
Settlement alarm (settle-pass THROW pages high; morning zero-settle alarm keyed off the Postgres ledger settle results, once per ET date, never on an empty yesterday), per-sport 3-consecutive-slot failure pager (pure opsWatch.createFailureTracker, pages once per losing streak), odds-api >=80% quota alert (once per day, Redis-deduped), systemHealth (statfs + os mem, disk>85 / mem>90 pages), daily 9 AM ET pulse (ONE notification: ledger rows yesterday via ledgerService.countRowsForDate, settles 24h, quota, disk/mem, desk line), docs/OPS-RUNBOOK.md (Uptime Kuma monitors, Coolify deploy-failure -> ntfy, phone subscription). All copy VOICE v1.1 — deadpan, numbers, no exclamation points (tests lint for it). 2398 -> 2437 tests. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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builtbykev
|
a6a81f5d42 |
Merge S2 (a1): compliance + approval pack
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> # Conflicts: # BUILD-STATE.md # web/src/app/about/page.tsx |
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builtbykev
|
fc0c7bfd18 |
Merge S7 (a1): newsletter — THE VYNDR REPORT
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> # Conflicts: # BUILD-STATE.md # CLAUDE.md |
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builtbykev
|
4b262fdd65 |
S2 (a1): compliance + approval pack
- /responsible-gambling rebuilt sincerely: 21+, 1-800-GAMBLER primary, 17-state resource list, warning signs, self-exclusion guidance, links to the existing /settings surfaces. No marketing adjacency. - /terms + /privacy honest drafts with entity placeholders ([ENTITY NAME], [STATE OF FORMATION], [ARBITRATION VENUE], [CONTACT EMAIL]); privacy sub-processors match reality (adds Sentry, honest PostHog description). - NEW /methodology: pipeline -> engine -> letter grades, refusals, VYNDR Originals, ledger settle + CLV + n>=20, why misses are public. - /about audited to North Star framing (THE PROOF card, methodology link). - Footer: 1-800-GAMBLER + Methodology link; compliance audit found zero pages suppressing the global footer. - 5 Ghost seed articles in content/articles/ + docs/GHOST-PUBLISHING.md manual runbook (no Ghost access used). - tests/unit/compliancePages.test.js (repo source-text style). 2429 tests green, web build exit 0. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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builtbykev
|
32d7200571 |
S7 (a1): newsletter — THE VYNDR REPORT
Email capture + daily report assembly + operator-triggered Listmonk send.
- NewsletterCapture (dark terminal, mono) on landing (below FAQ) + /welcome
(the real signup success surface); double-opt-in note; 'Signups open soon'
when Listmonk env is unset.
- POST /api/newsletter/subscribe: public, 10/min IP limit, honeypot,
server-side email validation, forwards to Listmonk subscribers API with
preconfirm_subscriptions:false (Listmonk sends the confirmation).
No env -> calm 200 { ok:false, reason:'not configured' }. Next proxy
web/src/app/api/newsletter/subscribe/route.ts (S25 rule).
- newsletterService.buildDailyReport: signals from snapshot:{sport}:latest,
STREAK WATCH via rosterLogs -> streaksService -> streakLens, THE RECORD via
ledgerService.getModelAggregate (percentage only when hit_pct != null —
n>=20 gate — else 'RECORD BUILDING · N pending'). RG footer (21+,
1-800-GAMBLER, Listmonk-native {{ UnsubscribeURL }}) in html + text.
VOICE v1.1 lint locked by tests: no '!', no banned vocabulary, numbers
only from injected pipeline data.
- sendDailyReport: creates + starts a Listmonk campaign; env-gated no-op;
refuses an empty report. Deliberately UNSCHEDULED — only
POST /api/internal/newsletter/send (internal key) triggers it.
- docs/NEWSLETTER.md: box-side Listmonk runbook (install, double-opt-in
list, API user, Coolify env, test-send).
- Spec: specs/feature-a1-s7-newsletter.md.
Tests 2398 -> 2429 (207 suites, all green); next build exit 0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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builtbykev
|
0996320bd1 |
S3 (a1): affiliate + partner plumbing
Zero out-of-pocket; everything config-flip-ready but DISABLED/organic. - BOOK IT deep links: web/src/lib/bookLinks.js + affiliateConfig.js (all books enabled:false, Impact/Partnerize param shapes documented, empty params skipped). Wired into StatStrip BookItTeaser (real anchor now) + scan hand-off links. Every book anchor renders rel="sponsored noopener noreferrer" (BOOK_LINK_REL). - Best-price marker: slateAdapter.detectBestBook (only when >=2 books post the SAME line and prices differ — absent beats wrong) + subtle signal-green dot in StatStrip. Slate.groupByGame threads the grouped per-book rows (books[]) onto PropRowProp instead of discarding them. - Partner refs: ?ref=CODE -> vyndr_ref cookie (90d, first-touch, PartnerRefCapture in layout) -> signup metadata partner_ref -> internal GET /api/partners/report/:code (requireInternalAuth; honest zeros + note until the TODO migration in docs/PARTNERS.md adds user_profiles.partner_ref — NOT run). Stripe promo-code convention: partner code == promotion code, verbatim. - Tests: +41 (2398 -> 2439, 209 suites); bookItTeaser + vyndrCoreScreens invariants updated to the new (stronger) rel contract. Web build exit 0. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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builtbykev
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7a35c96781 |
Session H (night2): design pass — §2 locks + honest waiting sweep
- QA.20: one meaning per color, locked — STEAM amber (caution), VALUE green (edge), red only at STALE/miss; revisions render the original grade struck through on slate AND ledger. - QA.21: every new data surface (PriorReads, PlayerStreaks, ModelRecord) renders data in mono (tabular-nums via the .mono class). - QA.22: waiting copy derives from the real cron schedule on every surface (StatStrip, dashboard, Explore) — no passive mystery anywhere. - Explore empty state joined the honest-waiting lib. - Sweep result: no raw brand hexes and no sans-serif data in tonight's components (sans only on prose/captions/hero letter — sanctioned); glow confined to A-tier badges + reveal chrome; 390px containment holds (wrapping chips, scroll-contained tables from Phase 3 rules). Backend 2352 -> 2396 tests (205 suites), web build exit 0. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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builtbykev
|
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
|
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
|
c8790fde55 |
Session 57: Phase 0 — Kill the Lies (2309 tests)
Work-order Phase 0 (Jul 10 live audit): every fabricated UI element deleted
or rewired to real data. Deletion sprint — no new product features.
0.1 Fake NBA game: root cause was scheduleService fetching the ESPN
scoreboard with no ?dates= param or date filter — off-season ESPN
returns the NEAREST slate (Jun 13 NYK@SA Finals rendered as tonight).
Now pinned to the requested ET date + defensive filter; undated events
dropped. Honest month-aware per-sport empty states (lib/emptyState.js).
0.2 Fake header counters: liveTick stripped to a bare 1s pulse (the
auto-incrementing "247 graded", sin-driven brain-%, aPlus/cascades are
dead). New GET /api/snapshot/summary (cache-only, before /:sport) +
Next proxy; HeartbeatBar shows the real graded count and SYNC =
elapsed since the last pipeline run (amber past 5 min).
0.3 Ticker: hardcoded fallback items deleted (real snapshot exhaust only);
MOVE kept — computeLineDeltas is real movement. <4 real items → no
ticker; bar publishes --ticker-h so the fixed header collapses cleanly.
0.4 /terminal retired: route redirects to /dashboard; VVI/injury-wire/
leaders layouts preserved unrouted as §12 content-engine templates.
Nav PRIMARY = Slate/Scan/Ledger; BottomTabBar Terminal→Explore; PWA
shortcut Terminal→Ledger; #terminal alias → /dashboard.
0.5 ›Query nav pill deleted (duplicate /scan link).
Backend 2289 → 2309 tests (199 suites), web build exit 0.
Spec: specs/phase-0-kill-the-lies.md. Next: work-order Phase 1 (ledger
persistence + settlement).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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builtbykev
|
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
|
d09a06c054 |
Session 55: Self-learning loop + real-time layer (2274 tests)
Product overhaul core — the two transformative, differentiated systems: Self-learning loop (Phase 2): outcomeService settles locked snapshot grades against real MLB Stats API results → hit/miss/push, rolling accuracy by grade tier (30d window). Idempotent, injectable, unit-tested. New GET /api/accuracy + /api/ledger/accuracy + internal settle triggers + cron hook. AccuracyBadge (dashboard/scan/landing) is honest — "LEARNING" below MIN_SAMPLE, never a fake number. Settled HIT/MISS chips overlay the live slate. Real-time layer (Phase 1): Slate silent 60s auto-refresh (no flash, no wipe on transient blips) + "SIGNAL LIVE · UPDATED Xs ago" freshness strip; Ticker LIVE badge that flashes on fresh events. Landing (Phase 3): TopSignals shows tonight's real top-3 A-rated grades + live accuracy — the product shown, not described. Founder pricing: FOUNDER_CODE_EXPIRY default 2026-06-30 → 2026-12-31 (had lapsed, disabling every founder code + the ClaimMeter pitch). That expiry — not a tier change — was the real cause of the 4 stripe test failures. Backend 2255 (4 failing) → 2274 (all green; +19 new, +4 fixed). Web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
|
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
|
b012da13f8 |
Session 53: Social preview fix — OG meta tags + image (2246 tests)
The link preview still showed engineer-speak ("Bayesian intelligence", "kill
conditions") because the OG meta tags were never updated after the S44 landing
cleanup.
- layout.tsx: de-jargoned the openGraph + main description + titles → "Pre-graded
player props with proprietary archetypes. Correlation-aware Parlay Lab.
Real-time line movement tracking. Built in Detroit." (Used "proprietary
archetypes" not "45" — codebase has 41.)
- NEW app/opengraph-image.tsx (+ twitter-image.tsx re-export): dynamically
generated 1200x630 card — VYNDR wordmark, "The books have every advantage. /
We built this to give it back." + feature row. Node runtime (not edge —
self-hosted standalone). Both routes prerender to a real PNG.
- Removed the stale /og-image.png metadata ref so the file convention owns the
image (no duplicate og:image tag).
Backend 2239 -> 2246 tests (+7), 193 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
|
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
|
f0674ca07d |
Session 51: Complete Team Hub (2234 tests)
Research-depth team view: /team/[abbr] with roster, archetypes, stats, props.
- Team API: mlbStatsAdapter.getTeams/resolveTeam/getTeamRoster (statsapi, abbr→id
+ active roster, cached). teamService.getTeamHub assembles roster → per-player
season stats (bounded concurrency) + archetype (snapshot grade or classify) +
tonight's graded props from grades:{sport}; whole hub cached 15min. MLB real;
NBA/WNBA graceful snapshot roster. GET /api/team/:abbr (404 unknown) + proxy.
- Team Hub page: server page.tsx (generateMetadata) + TeamHub client — header,
sort (archetype/graded/A-Z), archetype filter chips, roster rows (archetype +
player link + position + stats + graded props + parlay "+"), "No active props"
greyed state, loading/error.
- Game cards: team abbreviations are now TeamLinks → /team/:abbr?sport= (green
hover, stops propagation). Team Hub has "← Back to Slate".
Backend 2215 -> 2234 tests (+19), 190 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
|
f1956dc953 |
Session 50: Complete Parlay Lab (2215 tests)
Correlation-aware combined parlay grading — the Desk-tier differentiator.
- Correlation model (parlayService.js, added to S28 funcs): correlationScore
(game-aware 0.7/0.4/0.2/0.0), combinedGrade (avg penalized by avgCorr*0.5),
estimatedPayout (fair-odds product * (1-avgCorr) discount), correlationWarning,
gradeParlay.
- POST /api/parlay/grade (public, 2-6 legs) -> {combined,correlation,payout,legs}.
Fixed the Next proxy (was forwarding to /api/scan/parlay).
- ParlayContext: legs gained team/game/archetype; tier-aware maxLegs; auto-grades
the slip (debounced) when legs>=2 -> live combined/correlation/payout; hasLeg/
legKey/atCap.
- "+" button on every graded prop: StatStrip onAddLeg/isLegActive, wired by
vyndr/GameCard via useParlay (builds leg w/ team + game). GradeResultCard feeds
the same context from the scan page.
- ParlayPanel (replaces legacy ParlayTray): bottom slide-up w/ legs, combined
grade, correlation warning, est payout, CLEAR ALL + floating leg-count badge.
Tier-gated: free 2 legs (payout blurred -> Desk upsell), Analyst 4, Desk 6.
Backend 2185 -> 2215 tests (+30), 187 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
|
3b47b783dc |
Session 49: Complete onboarding flow + name micro-fixes (2185 tests)
Name micro-fixes (close the normalization arc): - collapseInitials merges "J C Escarra" -> "JC Escarra" (display + key); both playerName.js copies. Added mickey:michael nickname. Onboarding flow (end-to-end, complete): - Storage: Supabase user_metadata.preferences (no migration). - API: src/routes/preferences.js GET/POST (requireAuth, admin getUserById/ updateUserById, partial merge + sanitize) + Next /api/preferences proxy. - Page: web onboarding/page.tsx — 3 steps (sports >=1 / books skip / bankroll presets+custom+skip) -> SIGNAL ACTIVE -> POST onboarding_complete:true -> 2s -> /dashboard. Redirects to login when unauthenticated. - Redirect: dashboard fetches /api/preferences fresh; new+incomplete users (created_at >= cutoff) -> /onboarding; never while auth loading; existing users exempt. - Personalization: Slate default tab = prefs.sports[0]; preferred books glow in the card lines grid (lib/books isPreferredBook, threaded dash->Slate->GameCard). - Settings: PREFERENCES section loads + edits + saves sports/books/limit. Backend 2156 -> 2185 tests (+29), 184 suites. Web build clean (exit 0). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
|
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
|
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
|
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
|
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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builtbykev
|
7969a4971a |
Session 44: Make it visible — VYNDR archetype names, grade intel, schedule fix, landing page (2061 tests)
Frontend + wiring only. Wires existing backend into the pages users see. - VYNDR Original archetype rename (41) across archetypeService.js + lib/ archetypes.js + ArchetypeBadge, each keeping legacyName (resolves stale data). Judge -> BOMBER. Old POWER PULL slot -> WHIFF strikeout-artist pitcher. - BACKEND_HANDOFF.md: canonical frontend<->backend data contract. - Grade card intel: scan/page.tsx now forwards the engine's intel fields (season_avg/form/usage/matchup_grade/archetype/...) into mapScanToGradeResult -> STAT CONTEXT + VYNDR INTELLIGENCE sections populate. The chain already preserved them (tierGating + /api/scan spread); the page was dropping them. - Schedule freshness: slateAdapter.isRelevantGame drops completed games >24h old; Slate.filteredGames applies it. (TTL already 60s.) - Landing: Features.tsx rewritten to user-facing copy (no Point-biserial/Zone 14/ABS/Phi-coefficient). - Depth chart Next proxies added (/api/stats/lineup|depth|cascade) - were 404. - GameCard swap DEFERRED (Kev): legacy on-demand card stays as a bridge until the snapshot pipeline populates the grades cache; vyndr/GameCard swaps in then. Backend 2045 -> 2061 tests (+16), 167 suites. Web build clean (exit 0). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
|
80683e71b4 |
Session 43: Data pipeline + audit fixes + depth chart foundation (2045 tests)
P0 fixes + wiring real data into the S42 Player Intelligence architecture. - P0 dropdown z-index: the nav's backdrop-filter stacking context let the Ticker/HeartbeatBar paint over the avatar/More dropdowns and eat clicks. nav now position:relative zIndex:2; menus zIndex:100. Avatar Settings -> /settings. - Real MLB stats: mlbStatsAdapter.searchPlayer + getPlayerStats (name->id-> season+gamelog). playerIntelService.resolvePlayerStats normalizes into the archetype classifier; getPlayerIntel returns found:true + real season + archetype classified from real stats. NBA via nbaStatsClient (degrades). - Game cards: slateAdapter.groupPropsByPlayer (playerStrips, name once) + mapPitchers (MLB probables), folded into mapScheduleToGameCards. Legacy GameCard line grid renders BookChip (brand colors) not grey text. - Grade card intel: analyzeViaEngine1.buildIntelFields computes stat-context + form/usage/matchup/rest from the existing feature vector (zero extra I/O); gradeAdapter lights up the card sections. Archetype deferred (needs season line at grade time). - Depth chart foundation: depthChartService (getLineup/getDepthChart/ getCascadeProjection) + /api/stats/lineup|depth|cascade, graceful + injectable. - Mobile: player hero name overflow-wrap + 24px on <=640px (was clipping). Backend 2011 -> 2045 tests (+34), 163 suites. Web build clean (exit 0). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |