74aa75945e83355b9be9ef7719e822e60cba314b
36 Commits
| Author | SHA1 | Message | Date | |
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 builtbykev
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55b210cb95 |
Fix the dormant basketball window-bug before it ships; guard the class
PHASE 0 — audit. espnStatsAdapter's slice(0,20) was already fixed at |
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builtbykev
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3591c7626e |
Total grade cutover + the ceiling stated as a position
PHASE 0 caught my own repeat of the failure I diagnosed one order ago.
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builtbykev
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91927a4a8a |
Serve an honest grade: the letter was carrying 1/6 the information of the
number beside it PHASE 0 corrects the order's premise. A grade letter has been served all along -- engine1.gradeProp builds it from an additive factor index, computed INDEPENDENTLY of p_win. gradeBands is orphaned for a different reason than assumed: it defines what a letter MEANS from realized outcomes, and every band collapses to base-rate at current resolution. The measurement that changed this order, on 3,417 settled props: grade n realized mean p_win A 8 0.500 0.647 <- the TOP grade did WORST B 985 0.640 0.700 C 1,695 0.602 0.676 D 303 0.558 0.604 F 426 0.535 0.588 letter resolution 0.00116 (0.48% of variance) p_win resolution 0.00715 (2.98%) -> the letter carried 0.16x the information of the number beside it Concretely, from the hand-verify: Christian Encarnacion's 0.95 over graded C and his 0.05 under ALSO graded C -- same hitter, opposite forecasts, same letter. The gap was never that grades don't ship; it is that the weaker of two available signals shipped as the headline. PHASE 1 — model/servedGrade.js derives the letter from p_win with bands anchored on MEASURED realized rates (B+ 0.663 / B 0.646 / C+ 0.615 / C 0.589 / C- 0.548 / D 0.512 / F 0.447, base 0.6005). NO MANUFACTURED A, structurally: A+/A/A- are UNISSUABLE, not rare. The realized rate plateaus at 0.65-0.68 above p_win 0.70, so no band has earned a top letter; a test sweeps every p_win 0..1 and asserts none produces one. Even 0.99 tops out at B+ with its realized 0.663 attached. Raising that ceiling later is a deliberate, visible act. Bands that cannot separate SAY so -- C+/C/C- carry separates_from_base_rate false and copy naming it, which is the honest description of a forecast explaining 3% of variance. Every grade states its basis (forecast_only vs forecast_plus_matchup_factors, naming which factors fired) and calibrated:false. engine1.grade is preserved as engine_grade so nothing downstream breaks. PHASE 2 — refusals render real states: insufficient_data -> "not enough history to call this one"; juiced_no_edge -> "the book has priced the vig past any edge on this side". 1,870 refused snapshots carry exactly those two reasons and both now surface. PHASE 3 — hand-verified on 12 real served props. Freeman/Rice/Encarnacion 0.95 overs now B+ (was B, C, B); the 0.05 unders now F (was C). Refused doubles render NO READ with their reason. never-blank PASS, no-manufactured-A PASS. Serving change; nine frozen model modules unchanged including engine1; p_win never mutated; no calibrated number leaks (deployed set empty); no Bonferroni slot. STILL TRUE: the forecast explains ~3% of outcome variance. This order did not make the model better. It made the letter stop overstating it. 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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2391574f00 |
Live-surface integrity on the repaired champion; fix the label my own
repair falsified
PHASE 0 — four checks PASS, one defect found and fixed.
PASS gradeBands is required by NO serving code -- built across several
orders, never wired. No stale band derived from the retired
ten-game champion can reach a user, because none reaches a user
at all.
PASS CALIBRATION_DEPLOYED is [] and the calibrate loop iterates it, so
calibrate() is never called and p_win_calibrated is never set. The
only assignment site sits inside that empty loop. No withdrawn map
can leak.
PASS projectionFor reads l20_avg, which mlbGameLogFeatures now builds
from fullLog -- so refusals are computed on the repaired
full-window reference, not the retired ten-game one.
PASS factors still fire with correct sign across the repaired base
range (0.35/0.50/0.65/0.80): defense lowers, pitcher-contact
raises, platoon raises at every point. Mechanical firing check
only -- NOT a lift re-measurement, which waits for accrual.
DEFECT FIXED — my own repair falsified a user-facing sentence. The grade
card rendered "Last 20 games average: X" from l20_avg, and l20_avg is now
a FULL SEASON average. The number changed and the label did not, so the
surface was stating something the data no longer supported. Copy now reads
"Season average"; trapDetection's L20 explanations likewise. The field
name is kept -- it is read in many places -- but no rendered sentence
claims a window that isn't there.
That is the same class as everything else tonight, one layer out: a
correct-looking string describing data that moved underneath it.
Serving change; frozen model modules unchanged; p_win never mutated; no
Bonferroni slot.
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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494c83cf76 |
Hunt the window-bug class: three more paths, and the forward re-audit rule
in code PHASE 0 — getStatRows is the single base-rate path, so every branch is audited, plus the feature builders since l20_avg is the season reference projectionFor reads: getStatRows MLB -> estimator base fullLog CORRECT ( |
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builtbykev
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929fd81940 |
Repair the champion: it was reading ten games, not a season
PHASE 0 — the defect is real past the peek. Against a FAIR point-in-time baseline (each player's rate over games strictly before that date, >=10 prior games, box scores back to 05-01), the served champion LOSES on all four stats, three of four CIs excluding zero: hits 0.00251 vs 0.00774 CI [-0.0074,-0.0011] TB 0.00393 vs 0.00619 CI [-0.0055,-0.0003] rbi 0.02481 vs 0.03133 CI [-0.0153,-0.0005] runs 0.00181 vs 0.00683 CI [-0.0114,+0.0008] PHASE 1 — the cause is the WINDOW, not the weights. estimateProbability builds its base rate as the frequency over every row it is handed, and featureCache.getStatRows handed it res.last10. So the "season rate" was a TEN-GAME rate, and 0.4 of the forecast was the last five OF THOSE TEN. The 0.40 recency weight costs resolution on all four stats (-0.00086, -0.00107, -0.00562, -0.00365). Nudges are mixed and small -- harmful on hits and rbi, marginally helpful on TB and runs -- so they are left alone. PHASE 2 — two lines, no new data, no extra API call, because fullLog was already fetched by the same adapter call that produced last10: getStatRows now reads fullLog, and RECENCY_WEIGHT goes 0.40 -> 0.20. hits 0.00251 -> 0.00817 (tripled; now above the fair baseline) TB 0.00393 -> 0.00734 (above baseline; vs old CI [0.0020,0.0067]) rbi 0.02481 -> 0.02727 (still below baseline, CI includes zero) runs 0.00181 -> 0.00436 (still below baseline, CI includes zero) Gate stated exactly: hits and TB now exceed the fair baseline on the point estimate; rbi and runs remain below but EVERY CI now includes zero, so no stat reliably loses to a frequency table. That is a tie on rbi/runs, not a win, and it is reported as one. Only TB's improvement over the old champion is CI-confirmed; the rest are directional. STALE-FIT GATE: CALIBRATION_DEPLOYED is now EMPTY. The low-param maps were fitted on the retired forecast and fromLedger cannot rescue them -- settled ledger rows still carry OLD p_win, so refitting today would refit the retired forecast. Nothing is served calibrated until dates settle under the repaired champion, and the favourite-longshot bias must be re-measured rather than assumed to survive. The shadow duel is void. PHASE 3 — the hits factor lift is NOT re-measured, and cannot be yet: it needs settled rows produced BY the repaired champion, which ships in this commit. Replaying would score the factors against a reconstruction rather than the served forecast. Deferred, explicitly. The factors remain wired and transmitting; only their lift is unquantified on the new baseline. PHASE 4 — standing flag, and it is large: EVERY factor verdict in this programme, every null and every THEATER, was measured against a champion worse than a frequency table. Signal added to noise reads as noise. Prior verdicts may deserve re-audit. Logged, not re-run. Re-queued not built: rbi lineup-slot / RISP opportunity through the two-part gate, now landing on a repaired champion. Serving-path change by design; the byte-identical invariant inverted and all four stats move. Nine frozen model modules verified unchanged. No Bonferroni slot -- resolution accounting on the champion's own knobs. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
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43f65d30cb |
Wire the three proven hits factors pre-grade: transmission proven, gain
inconclusive THE BUG THIS NEARLY SHIPPED AS A FINDING. The first audit reported 0 factors fired on all 1,140 rows. Not a result -- my paging helper ordered by `id`, and batter_spray, team_defense, platoon_splits and statcast_aggregates have composite primary keys with NO id column. The query errored, the loop broke on error, and four fully-populated tables read as empty. hitsFactorContext.js -- the PRODUCTION loader -- had the identical defect, so live wiring would have loaded nothing and served unadjusted while logging success. Third occurrence of this class in one session. Both loaders now order by a real column and THROW rather than degrade. The Phase 2 gate is what caught it: no resolution number was quoted until transmission was proved. PHASE 1 — pipeline is now base -> FACTORS -> CALIBRATE -> GRADE. Context built in snapshotService BEFORE gradeAndCacheSlate (was line 640+, grade at 454), threaded per prop, applied to p_over before p_win is set with p_win_prefactor and a full trace retained. Hits only. Coverage 859/1140 rows (75%): 474 with all three factors, 256 two, 129 one, 281 none. PHASE 2 — TRANSMISSION PROVEN, 12/12 sign-correct, 4/4 per factor, each applied IN ISOLATION. My first table compared each factor's expected sign against the COMPOSITE change and showed 3 false failures -- with three factors firing the net can oppose any single member; that was a flaw in the test, not the wiring. Two under-side rows confirm the flip is handled: a factor raising p(over) correctly lowers p_win. Switch hitters (Bailey, Bell, Rocchio) took no spray adjustment while their other factors fired normally -- the refusal is selective, not a blanket skip. PHASE 3/4 — both maps refit on the factor-adjusted forecast; the shadow-duel baseline is VOID and restarts, since it accumulated against a different forecast. Point-in-time, 765 held-out rows: reliability 0.00795 -> 0.00828 RESOLUTION 0.00229 -> 0.00345 (variance explained 0.93% -> 1.39%) Brier 0.25398 -> 0.25305 delta -0.00093 CI [-0.00225,+0.00002] Resolution rose 51% relative. The CI TOUCHES ZERO on 4 eval dates, so the composition does NOT earn a proven keep -- three isolated passes did not grant a composed pass. INCONCLUSIVE, reported as such. The gain is far below the sum of the isolated effects, which is expected: all three run through the same pitcher-batter confrontation and share signal. PHASE 5 — 1.39% of variance is still far below what band separation needs. The pivot was correct and incomplete: the plumbing defect was real and is fixed, three proven factors reach the served number for the first time, and transmission alone did not buy grade separation. Next arc is factor STRENGTH and BREADTH, not more plumbing. PHASE 6 — rbi anomaly logged, not chased: 14.51% variance explained vs hits 1.03%, on the stat we do not serve corrected and which has no proven factors. Either the biggest lever on the board or a mirage; it deserves its own order. The byte-identical invariant INVERTED for hits by design. All 13 frozen non-hits modules verified unchanged, probabilityEstimator included -- the factors ride outside it. No new Bonferroni slot; the composed OOS claim is reported with its CI and not claimed as a pass. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9 |
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builtbykev
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092f8f09cd |
Build opportunity_drift axis on challengerProjection (arch-v1)
Champion p_win and the live grade path are BYTE-IDENTICAL: the axis writes only to p_win_challenger / challenger_adjustments in the ledger. STEP 1 -- MAP THE INPUT. MLB_LOG_FIELD now maps at_bats -> 'atBats'. Deliberately NOT added to outcomeService's map or liveTracking's LIVE_BOX_FIELD: those exist to SETTLE and TRACK graded props, and nothing grades at-bats, so adding it there would imply a settlement path for a market we do not carry. A test asserts the settle map still lacks it. STEP 2 -- DRIFT, NOT LEVEL. opportunity_drift = mean(last-5 atBats) / (season atBats / games). The LEVEL is collinear with l20_avg (same games denominator; hits/game ~= (hits/AB) x (AB/game)), so the projection already embeds it multiplicatively and adding it would double-count. A deviation from the player's own baseline is the part the projection does not contain. HONEST ABSENCE throughout: fewer than 3 at-bat rows, no at-bats in the logs, or no season baseline all leave drift UNDEFINED -- never 1.0 by default and never 0. Number(null) === 0 here would read as "zero at-bats", the strongest possible fade, invented from missing data. Four tests cover the absent paths. STEP 3 -- THE AXIS. opportunityNudge composes in the same log-odds space as park and platoon (log of a ratio), with two guards the measured axes do not need: a +/-10% DEADBAND (a rest day or a blowout can move a 5-game window without any role change) and a tighter cap (0.15 vs the environment's 0.30) so a noisy PROXY cannot outvote measured signals. Every adjustment carries is_proxy: true and proxy_for: 'confirmed_batting_order' so nothing downstream can mistake it for a lineup feed. The axis can stand ALONE -- without it the early return would gate opportunity off on exactly the thin-classification rows it is most likely to help. Zero extra I/O: analyzeViaEngine1 attaches drift from the feature vector it has already built, and attachChallenger reads it off the grade. Nothing re-fetches in a loop that runs over hundreds of props. COLLINEARITY GUARD added to the coverage probe: Pearson r of drift against l20_avg / l5_avg / ab_per_game, returning null under n=8 rather than reporting a correlation on a handful of rows. If drift just re-encodes the projection, the axis is dead signal and gets shelved. Gates: 4,073 tests / 326 suites green; next build exit 0. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc |
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builtbykev
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b85b351993 |
Grade-board sort: signed signal, takeable-gated p_win, missing sorts LAST
Display ORDERING only. No grade, ledger row, lock_line, scoring, or edge_pct
scale/display change. Push scoring untouched.
Two defects removed from selectTopGrades (wrong at ANY scale, independent of
edge_pct's separate retirement):
1. edge: Math.abs(numOr(g.edge, -Infinity)) — abs() on an already-
direction-signed value ranked the model's strongest DISAGREEMENTS level
with its strongest agreements (177 public ledger rows carry a negative
edge; positive = the model AGREES with the graded side).
2. Math.abs(-Infinity) === Infinity, so a row with NO edge sorted FIRST —
absent data presented as the top pick (the Number(null) class).
New key: grade -> confidence -> takeable-gated p_win (nulls LAST) -> SIGNED
edge (nulls LAST) -> input order. Scales are never mixed in one comparator.
Takeable band = web valueState.isTakeable, asserted byte-equal to the hero's
config/valueEngine.isTakeable (-160..+200) incl. strict-null.
Alt-line ladder (analyzeViaEngine1:506) no longer sorts by edge_pct: ordered
highest-p_win-first derived analytically at zero added compute — P(stat >= k)
is monotone non-increasing in k, so p_win-desc is line-ASC for an over and
line-DESC for an under. base stays marked; no consumer depends on
alt_lines[0]; deskShowcaseService.rungsOf already re-sorted by line.
THREE PREMISE BREAKS found report-first, before code:
- /api/props/top-graded 404s in prod (absent from src/) so the dashboard
board renders receipts/empty — the edge sort orders nothing there today.
The prior order's "97.3% of rows tie" was a LEDGER measurement wrongly
extrapolated to that board. Fix is correct-in-itself and lands when the
feed is restored.
- p_win cannot be a client-side key for all tiers: snapshotGating strips it
for unentitled tiers ("shipping p_win is shipping the model price").
Verified live: prod /api/snapshot carries p_win on 0/8 MLB, 0/25 WNBA.
- Ladder rungs carry no per-rung price, so the takeable gate is inapplicable.
Verified on real data, both sports, both paths: unentitled — WNBA (n=25)
ordering CHANGED, MLB (n=8) unchanged, signed edge non-increasing in every
(grade,confidence) tie group (20 pairs, 0 violations); entitled — 40 real
ledger rows with p_win+locked_odds, p_win-descending, untakeable chalk NOT
promoted (Trea Turner .757 @-275 does not beat Rhyne Howard .745 @-120)
(36 pairs, 0 violations).
Hero consistency, stated honestly: same signal + same gate, different
precedence BY CONTRACT (board = grade-tier-first "top GRADES"; hero =
p_win-first "top read"). Identical within the leading tier (verified); across
tiers the board may lead with an A the hero doesn't pick. Not a contradiction.
Floor: 310 suites / 3864 tests green, web build exit 0. Dashboard + Desk
visuals are auth/feed-gated -> tagged for the Chrome audit, no visual faked.
Held: edge_pct rescale/display retirement (Order B); building the missing
/api/props/top-graded selector; exposing p_win to unentitled tiers.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc
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builtbykev
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83e9da3663 |
Consistency classifier: CV → index of dispersion for low-mean counts
The A/D investigation found CV (std/mean) is scale-broken on count data —
for a Poisson-ish stat cv ≈ 1/sqrt(mean), so EVERY stat with mean < 4 blew
past the boom_bust cutoff regardless of behavior. The S63 stopgap made those
return 'unknown', which silently ate a real +1.0 consistency signal on every
MLB batting prop — steady low-mean hitters never got their earned factor.
Fix, fenced to the low-mean branch of consistencyScore (the only branch that
was returning 'unknown'): classify with the index of dispersion (variance/mean,
Poisson baseline 1.0) — the scale-appropriate, UNBIASED statistic for counts.
mean ≥ 4 keeps the NBA-calibrated CV path BYTE-IDENTICAL (zero NBA blast
radius). This is a bug CORRECTION, not threshold loosening: the CV thresholds
and the engine1 ±1.0 delta are unchanged.
Bands (asymmetric around Poisson 1.0, since counts are naturally mildly
over-dispersed): iod<0.60 elite / <0.85 reliable (+1.0) / ≤1.30 volatile
(neutral) / >1.30 boom_bust (−1.0). Sample floor MIN_GAMES_FOR_IOD=8 so a
thin sample abstains ('unknown') — no small-sample guess.
Validated on real 10-game logs (two-sided): Kwan hits 0.67 / Alonso hits
0.78 → reliable (RECOVERED); Alonso TB 2.57 / Henderson hits 1.33 → boom_bust
(no false consistency); HR mean 0.1 → 1.0 → neutral. Direct engine1 proof: a
strong steady prop that grades B+ today reaches A- once the +1.0 fires; a
boom-bust bat stays B (no inflation). A- now emerges NATURALLY from a real
recovered factor. Standing two-sided test pins all three directions.
Forward-only (settled grades are locked in the ledger, never re-graded).
Emitting A- ≠ proving A- — the A-tier record accrues from emission, still
measurement-gated. Full unit suite green (4 pre-existing redis/timing flakes
pass in isolation); 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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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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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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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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d3ffa1b8c2 |
Retention: model_snapshots live + base64 SSH key support
RETENTION (Phase 2, priority zero). History starts compounding tonight. migration 025 model_snapshots — APPLIED to prod. Append-only, one row per graded prop PER SIDE PER CYCLE, with a unique index on (snapshot_id, player_key, stat, line, side) so a retried cycle cannot duplicate. RLS on, service-role writes only. What it captures that the ledger never did: - features jsonb — the model's INPUTS. Without these a backtest can only grade our own homework; with them any future model can be replayed against the exact conditions this one faced. - REFUSALS (refused + refusal_reason). The ledger drops them, so a gate refusing props that would have WON is invisible — unmeasurable lost edge. Captured via a new onGraded hook in gradeSlateService that fires with BOTH sides before any filtering. - grade_11, the pre-collapse grade. The 4-letter map throws away the entire live C-/C/C+/B- range. - model_version + code_sha on every row. ledger_entries mixes pre/post-fix grades with no marker and cannot be separated retroactively. - p_win / ev_pct / fair_odds / takeable / value — none of which any permanent store held. Wiring: analyzeViaEngine1 attaches _features/_grade_11 (underscore = internal); gradeSlateService fires onGraded then STRIPS them so they never reach a cache or API payload; snapshotService builds rows and persists best-effort. Retention reuses the LEDGER's dateET/gameIdFor helpers so rows share the ledger's natural key exactly — otherwise the settle pass could never join outcomes onto them. Rows are written BEFORE the empty- slate early return: a slate that refused everything is exactly the case worth recording. CONTRACT HELD: retention is injectable and every path is caught. persist() returns errors, never throws; a missing Supabase client is SKIPPED, not an error. A retention failure can never break a snapshot. BACKUP: backup-db.sh now accepts BACKUP_SSH_KEY as base64 (recommended — survives env-var newline mangling, which is how injected SSH keys usually break silently) OR raw PEM, detected by decoding and looking for the PEM header. Verified both forms detect correctly against a real generated key. Suite 279/3325 green, build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
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builtbykev
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26b276fbfb |
Fix the ESPN team-stats parser + report: opponent rank is still underivable
Ran the manual regrade with the internal key (thanks). Results are mixed
and the honest half matters more.
CONFIRMED WORKING — the probability layer is fully alive in production.
After POST /api/internal/snapshot/{mlb,wnba}: p_win, ev_pct, model_odds
and value are present on 32/32 live grades (mlb 7/7, wnba 25/25), up from
0/8 before. That fix is done.
NOT WORKING — the grade-range half did not land, and I am not going to
claim it did. The live distribution is unchanged (wnba B17/C8 before AND
after; mlb B4/C3), no A, no D, same four confidence values. Diagnosis:
matchup_grade is 0/25 on the live board, i.e. opp_rank_stat is still
null, so engine1's +/-1.0 opponent factor still never fires and the
ceiling is still +3.0 against the +4.5 an A requires.
Two distinct causes, both verified against the live ESPN feed:
1. refreshTeamStats CRASHED on every team — "buckets is not iterable",
captured 0 / errored 15. ESPN's current shape is results.stats =
an OBJECT with categories[], not an array. The old parser did for...of
on it. This was invisible until S63 gave the function its first
production caller. FIXED here (now captured 15 / errored 0) with a
regression test covering the current shape, the legacy array shape,
and empty payloads.
2. Even parsed correctly, the endpoint does not carry a
defensive-strength metric at all: defensive_rating, opponent_ppg,
pace and opponent_fg_pct all normalize to null — it returns only a
team's OWN stats. So defensive_rank_normalized cannot be computed and
opp_rank_stat remains underivable from this source. A test documents
the gap and will fail if that ever changes.
Consequence: A STILL DOES NOT EMIT, so the A-RATED marketing hold STAYS.
Reviving the opponent factor needs a different derivation (opponent
points allowed from scoreboard/schedule, or a different ESPN endpoint) —
logged as the concrete next item, not hand-waved as done.
Suite 278/3305 green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA
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builtbykev
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1a94ef5fcf |
Revive the dead probability layer + restore grade range ON MERIT
Folds re-sequenced steps 1+2 into one change (Kev's call): same bug
family — features wired to sources that return null.
THE PROBABILITY LAYER WAS DEAD IN PRODUCTION. p_win/ev_pct/kelly/
model_odds/value were absent on 0/8 live grades because
gameLogService.getGameLogs returns null for MLB by construction and
depends on the offline Python service for NBA/WNBA, so meta.gameLogs was
[] for every sport. This was the S46 bug in a second location — that fix
gave featureCache an MLB branch (why grades still worked) but never the
estimator. featureCache.getStatRows now supplies normalized rows
([{date,[statType]:v}], most-recent-first) for every sport, feeding the
estimator AND consistency AND game_count_in_7d from one fetch.
VERIFIED on real props: p_win 25/25 WNBA, 8/8 MLB (was 0).
GRADE RANGE, ON MERIT — never by rescaling (permanent founder ruling:
minting A's without new information is a relabelled B sold as an A and
corrupts an append-only ledger).
- refreshTeamStats wired into runSnapshot — it had ZERO production
callers, so opp_rank_stat was permanently null and a +/-1.0 factor
could never fire. Test-env no-op (opsNotify precedent).
- L20 made SYMMETRIC: both branches were delta +1.0, so the season
baseline could only ever ADD. No negative path was a structural reason
D was unreachable. New l20_contradicts_* carries -1.0.
- game_count_in_7d derived from real logged dates (heavy_workload_7d).
- NOT wired, deliberately, with reasons inline: teamId (no team_id
column; getFeatures reads it top-level; factor also needs a starter-id
list) and season_type (ESPN 2 = REGULAR season; threading it raw would
fire veteran_in_playoffs in July). Dead code dressed as a fix is the
thing we are removing, not adding.
CALIBRATION GUARD (found by verifying, not assuming): consistency CV is
NBA-tuned; for a Poisson-ish stat cv ~ 1/sqrt(mean), so any stat with
mean < 4 auto-classifies boom_bust. First verification run showed 8/8 MLB
props boom_bust — a blanket -1.0 that dropped the board to all-C. Floored
at CONSISTENCY_MIN_MEAN=4 -> 'unknown' below. Absent beats wrong. MLB
low-count stats therefore still get no consistency factor: honest, not
fixed. Scale-free index-of-dispersion classifier is the open follow-up.
CONFIDENCE IS NOT A PROBABILITY: payloads carry confidence_basis:
'grade_band'. Corrected mlb-grade-degradation.md — its "25/25
grade<->confidence agreement" is a TAUTOLOGY (confidence is derived FROM
the letter, so it would report 25/25 even if every grade were wrong), not
a validation. Removed dead mlbGrader.js (referenced only by its own test)
and the stale computeFeatures comment claiming a penalty that never ran.
VERIFICATION (scripts/verify-grade-range.js, real props/logs/engine):
WNBA 25 props B 68%->32%, C 32%->64%, D 0->1 (4%); 11-step spread went
from 2 steps to 5 (C/C+/B-/D). The D is earned: Angel Reese assists o2.5,
p_win 0.365. Nothing flooded — grades got HARDER. A did not emit locally
because opp_rank_stat needs the Redis cache only prod populates (local
ceiling +3.0 vs the +4.5 A needs); reachability is proven arithmetically
and locked in tests. Prod A-emission is the outstanding fingerprint.
MARKETING HOLD: "A-RATED" (AccuracyBadge, TopSignals) is unsupported
until that fingerprint. Confirmed honest fallbacks render today —
/api/ledger/accuracy has B and C buckets only, so the badge shows
"MODEL · 63% HIT" and TopSignals self-hides. Nothing fabricated ships.
Suite 276/3286 green, web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA
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builtbykev
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7a925f43eb |
Model Train arc 1 (engine): de-vig + EV + takeable/value gates + hero v2 + triplet
Steps 1-6 — make "real opportunities at takeable prices" the engine, not a filter. 1. DE-VIG (src/utils/devig.js): two-way multiplicative de-vig strips the vig and returns fair prob + fair price per side + the overround. One side missing → fair UNAVAILABLE (null), never faked. Method noted in code + the `devig_method` field. 2. EV (devig.evPct): ev_pct = model prob × decimal − 1 at the graded side's ACTUAL price. This is the ranking signal now, replacing raw |model−consensus|. 3. TAKEABLE gate (src/config/valueEngine.js, TAKEABLE_ODDS_CEILING −160 .. +200, env-tunable): promoted surfaces only (hero/featured/alerts). The full board still shows everything; Parlay Lab exempt; JUICE_ODDS_FLOOR (−400) stays the absolute backstop underneath. Strict null-guard (Number(null)===0 would have made a missing price "takeable"). 4. VALUE flag: passes BOTH gates (takeable AND ev_pct ≥ VALUE_EV_THRESHOLD). Grade = read quality; value = the price pays you. Shipped in payloads. 5. HERO v2 (heroPropService): highest ev_pct among takeable A/B reads — a huge gap on a −900 line is trivia, not an opportunity. 6. VALUE TRIPLET: book_odds · fair_odds · model_odds on every read (snapshot, hero, scan — they all spread the grade). Handoff documents the fields; the rendering is Session-2 Design's job. All wired in analyzeViaEngine1's existing p_win/kelly block (real quantile probability × real book odds, or nothing). 33 new tests; suite 276/3306 green. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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348a82b4a0 |
Generalize the no-edge guard: suppress by the BOOK'S PRICE, not a stat whitelist
Follow-up to the rare-event under fix — the whitelist (doubles/triples/HR/SB) was fragile: the same juiced-under problem exists for steals, blocks, and any other low-frequency market, and a new stat would slip through. The real signal is the book's own price. The doubles unders were priced -625 to -1100 — laying 6-11x to win 1x on an ~82% event, with no value the model could recover. So the PRIMARY guard is now stat/sport-agnostic: analyzeViaEngine1 refuses any read whose graded-side odds are past the juice floor (JUICE_ODDS_FLOOR, default -400, env-tunable). That catches every version of this — steals, blocks, anything — and it also keeps the public record honest (those -800 "wins" hit ~82% of the time and would inflate the hit rate, the same class as the projection-0 degradation). The structural rare-event rules stay as the BACKUP for props with no odds (list also expanded cross-sport: + steals, blocks). Normal + longshot prices (-110, -250, +600) are preserved. 16 tests cover both layers. Reported: the doubles projection was REAL per-player (not a fallback); the fix is the price guard, not a bigger list. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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f72f063e6f |
Suppress rare-event 0.5 unders (juiced, no-edge) — config-driven grade + board fix
Betting-logic audit: the CONSENSUS-vs-MODEL board flooded with fake reads like "DOUBLES u0.5 · MODEL 0.2 · +edge" — the juiced under side of rare counting-stat markets (doubles/triples/HR/SB), which is never a takeable edge and violates the no-unders-default doctrine. Report finding (item 3/4): the doubles projection is REAL per-player, not a flat fallback — 'doubles' maps to a real game-log field (MLB_LOG_FIELD doubles→ doubles) and the live values varied (0.03/0.16/0.2/0.22). So no projection-gate refusal for fakeness; the problem is purely structural (a rare event's real projection always sits below a 0.5 line, so the under always "wins"). Fix (config-driven — src/config/rareEventMarkets.js, tunable stat list + line threshold): - Grade layer (analyzeViaEngine1): a rare-event UNDER at ≤0.5 is always REFUSED (grade null + suppressed flag/reason). A rare-event OVER at ≤0.5 is refused UNLESS the model genuinely projects the event above the line — because a 0.2-over-0.5 carries the SAME |edge| as the suppressed under and would just take its rank on the board. The over grades normally once projection > line. - Board layer (marketBreadth.collectBreadth): drops null-model rows so a suppressed/ungraded prop can't rank a "MODEL —" placeholder onto the board. 10 suppression tests + config locks; also fixed a settingsPage book assertion left over from the ESPN→theScore swap. Suite 274/3289 green, web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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888d103f95 |
Fix MLB grade degradation: projection>0 gate, edge semantics, letter=confidence
The #1 board item — three grading bugs the phone audit surfaced, all in the live Node grade path (engine1 + analyzeViaEngine1), fixed at the source. 1. PROJECTION=0 NOW REFUSES. projectionFor returned l5_avg even when it was 0 (finite, so the `== null` gate passed it) — 9/25 live grades graded on a zero projection, producing a degenerate edge and a hollow grade. Now a non-positive reference is not a projection: projectionFor skips it and falls through to the next POSITIVE reference (l5 -> l20 -> per_90 -> xg); when none is positive it returns null and the read REFUSES (insufficient_data). The gate also gained an explicit `> 0` guard so the invariant is structural — a grade can never be emitted with a non-positive projection. Fewer graded props, honest. 2. EDGE_PCT. The formula was already (model - line) / line signed by direction — Kev's intended semantics. The broken {20,60,100,140} cluster was the proj=0 degeneracy ((line - 0)/line = 100%); with #1 those refuse, so the fabricated 100s vanish and real edges flow. The main-line edge now reuses the VALIDATED projection (edgePctFor accepts an optional ref) so edge and the persisted projection can never diverge. Frontend |edge|>40 guard stays as a safety net. 3. LETTER == THRESHOLD_TABLE(CONFIDENCE). engine1's hand-rolled GRADE_TO_CONFIDENCE drifted a full sub-tier low (B -> 0.55, which the canonical grade_thresholds.json calls B-) — the "B at 45%" the audit caught. Now confidence is DERIVED from each grade's band MIDPOINT in grade_thresholds.json (one source of truth, shared with the Python engine), so applying the threshold table to any grade's displayed confidence resolves back to the same letter. Proven for all 11 grades. Regression locks: tests/unit/mlbGradeDegradation.test.js (14 tests). Backend suite 269/3253 green, web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
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287c1c047a |
Wave 0: NBA/WNBA grade unlock — free ESPN per-athlete gamelog source
The Python nba_api service (gameLogService) is offline in prod, so
featureCache's non-MLB branch produced no l5/l20 averages →
projectionFor returned null → the ENTIRE NBA/WNBA slate refused
(insufficient_data). Only MLB actually graded.
Fix (free, no-auth, verified live):
- espnStatsAdapter.getPlayerGameLog(name, sport) — resolves name→ESPN
numeric athlete id via the v2 search (the v3 /search now returns
count:0; the v2 uid carries a:<id>, defaultLeagueSlug disambiguates
league), fetches the per-athlete gamelog, and parses per-game rows
keyed by VYNDR stat names (points/rebounds/assists/threes/steals/
blocks/turnovers + computed pra). Columns are indexed by the
response's own names[] array (NBA and WNBA orders DIFFER), never
positionally. Most-recent first, defensive (null on unrecognized
shape, never throws), cached (espngamelog:{sport}:{id} 4h + memory).
- featureCache.gameLogFeatures — falls back to the ESPN gamelog for
nba/wnba when the Python source returns null/empty, producing
l5/l10/l20 + rest_days + minutes_per_game via a new local
NBA_LOG_FIELD map + pure nbaGameLogFeatures (S11 three-map-split:
separate from MLB_LOG_FIELD).
Grade gates already whitelist all 8 NBA/WNBA stat types in both Node
paths (analyze.js + scan.js); no gate change needed.
Tests (hermetic, no network): espnGameLog.test.js (parser/resolver/
adapter) + featureCacheNba.test.js (the UNLOCK proof — empty features
refuse, ESPN-derived features grade). 3098 tests green; next build
exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
|
d242b11b4b |
S1 (a1): feature-promise audit — no claim survives unverified
PROMISE-AUDIT.md: every /pricing claim → verified/built/reworded. BUILT (was vapor): alt line ladder + edge ranking (same-features regrade at shifted lines, Desk-gated at the API), quarter-Kelly (engine quantile P(win) x real captured odds — either missing → no sizing), free-tier kill-condition locked previews. FIXED (was false): analyst 15/day cap vs the Founder 'Unlimited reads' promise → analyst unlimited; every '40+ factors' claim (real count: 22 named features) reworded truthfully in 7 files. VERIFIED: cascade alerts (real, wired), phi correlation, leg history, cross-book comparison, WC soccer, real-time feed. Locked by tests/unit/promiseAudit.test.js. Jest now ignores .claude/worktrees (parallel agents' suites no longer leak into runs). 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
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2ae8a5697e |
Session 56: Full audit — PropLine + boxscore + pipeline + sport coverage (2289 tests)
Research (verified against live MLB Stats / ESPN / The Odds APIs): - specs/propline-audit.md — every stat_type mapped against our 4-layer pipeline; real MLB boxscore fields; sport coverage status; pipeline gap analysis. - specs/vyndr-roadmap.md — priority-ordered Sessions 57–64 + coverage targets. - scripts/propline-audit.js + specs/audit-data/ (raw capture). Headline bug: oddsNormalizer mapped batter_rbis → 'rbis' while the whole grade/feature/outcome chain keys on 'rbi' — every PropLine RBI prop silently failed to grade AND settle. Fixed (+ regression test). Phase 4 — wired missing MLB stats end-to-end: - PropLine MLB markets 6 → 12 (+runs, walks, doubles, earned_runs, hits_allowed, outs — same request, no extra quota). - doubles/outs/triples added to featureCache + outcomeService MLB_LOG_FIELD and all three grade whitelists (analyze/scan/validation.py). Phase 6 — pipeline resilience: - opsNotify.js: ntfy alerts (never throws, test-disabled). Snapshot success/ stale/failure alerts; retry-once on hard odds error (not on empty slate). - Missed-cron watchdog (mostRecentExpectedSlot/isSnapshotOverdue); status probe now returns `overdue`. Coverage truth: MLB is the only end-to-end-live sport; outcome settlement is MLB-only (WNBA/NBA/soccer never settle) — documented as the #1 roadmap gap. Backend 2276 → 2289 tests (+13). Web build exit 0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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builtbykev
|
78db55d499 |
Session 47: Name normalization + grade intel + ticker polish (2149 tests)
- Name normalization completed: NICKNAMES table (Matt↔Matthew, Mike↔Michael...)
resolved in nameKey, parenthetical team-tag strip "(STL)", verified accent-fold
(Iván/Ivan, José/Jose). Slate strip now DISPLAYS the normalized de-dotted name
("AJ Ewing" not "A.J. Ewing") via buildPlayerStripsFromProps.
- Complete MLB VYNDR INTELLIGENCE: mlbGameLogFeatures derives rest_days (days off
between latest games; 0=B2B) + ab_per_game (usage). buildIntelFields renders
usage as "X AB/G", rest as B2B/Xd, matchup from bvp_advantage fallback.
- Ticker SCAN dedup: pushTickerItems keeps one SCAN per sport (sport field or
text-prefix parse for legacy); MOVE/GRADE preserved; cap 50.
- BOMBER threshold prorated for mid-season (hr>=15 strong / >=10 mod) so June
sluggers classify BOMBER not FLEX/DRIVER.
Backend 2122 -> 2149 tests (+27), 179 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
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c8fc9f577e |
Session 46: Grade card intel + name normalization + pitchers (2122 tests)
Three focused P1 fixes on the Session-45 snapshot model.
- Grade card intel ROOT CAUSE: gameLogService is NBA/WNBA-only (offline Python),
so MLB props never got l5_avg/l20_avg and buildIntelFields returned {}. Wired
MLB game logs into featureCache.gameLogFeatures via mlbStatsAdapter.getPlayerStats
(pure mlbGameLogFeatures + MLB stat_type->field map). buildIntelFields gained
playerStats/projection fallbacks for partial intel.
- Player name normalization: src/utils/playerName.js (+ web/src/lib copy):
normalizeName -> {display,key}. Strips periods, de-dots suffix, accent-folds
the key. Applied in snapshotService grouping, slateAdapter grade index +
player-strip merge (variants collapse, longest name shown), and
playerIntelService. "A.J. Ewing"/"AJ Ewing" + "Jazz Chisholm"/"Jr." now merge.
- MLB starting pitchers: new GET /api/schedule/:sport/pitchers (probablePitchers
service wrapping mlbStatsAdapter.getScheduleWithPitchers + best-effort ERA).
Slate fetches it, builds a team->pitcher map (full name + mascot match),
attaches pitchers to MLB GameCardData. + Next proxy.
Backend 2100 -> 2122 tests (+22), 176 suites. Web build clean (exit 0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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builtbykev
|
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> |
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builtbykev
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73b65a0248 | Session 16: Live hero prop, sport-specific markets fix, soccer weather, Sentry CSP (1429 tests) | ||
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builtbykev
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167996d99a |
Session 15: Intelligence hardening — park factors, weather, Tank01 prefetch, pace factors, signal audit, founder pricing fix (1405 tests)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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builtbykev
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f5d79cf70d | Session 14: Africa checkout, Tank01 NBA/MLB wiring, WNBA+MLB odds proxies, OAuth icons, loading skeletons (1330 tests) | ||
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builtbykev
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b55dcbd614 | Session 9: api-football + FootApi + Tank01 adapters, grace period middleware, cookie consent, /pricing page, OOM fix documented (1240 tests) | ||
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builtbykev
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ad5ea8d5a8 | Session 7j: Soccer intelligence - 9 leagues, 11 signals, 6 traps, poller, prefetch, 131 new tests (1173 total) | ||
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builtbykev
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4815ceac03 | Sessions 7e+7f: Grade adapter, normalize consolidation, computeFeatures, analyzeViaEngine1, scan/parlay migrated to engine1 | ||
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builtbykev
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012c0ef47e | Session 7e: Grade adapter, normalize consolidation, ARCH-2 banners | ||
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builtbykev
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6f4a353de9 | Session 7d: Audit fixes - rate limiting, error leak, parallel parlays, analyze cache, bundle analyzer | ||
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builtbykev
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1fa04dc776 | Sessions 5-7a: 955 tests, deployment ready |