DELTA MEASURED on live prod grades (live ordering unchanged): MLB 7/8
props move (87.5%), mean 2.5 places, TOP READ CHANGES (corey seager hits
1.5 under -> jake burger hits 0.5 over). WNBA 25/25 move, mean 4.1, max 12.
This is a large re-ordering, not a tweak.
Caveat recorded rather than buried: MLB had only 8 graded props at
measurement time. The percentages are real; the sample is one small slate.
Re-run before the flip -- it is one call.
PER-SPORT DOCTRINE ENFORCED IN CODE. WNBA moves the most and must NOT
adopt this: its p_win is anti-predictive, so ranking that board by p_win
would sort it by a signal measured to point the WRONG WAY -- worse than
the incumbent, not better. A comment would not have stopped a future flip
from going global, so FORECAST_RANKED_SPORTS = Set(['mlb']) gates the
forecast_rank stamp, with tests asserting no sport inherits MLB's result.
A sport joins only by passing its own holdout.
EDGE IS NOW DIAGNOSTIC-ONLY IN DISPLAY. MobileEdgeBoard.EdgeCell rendered
green (--g-a) for positive edge and red (--miss) for negative. Two things
were wrong: green/red IS a quality claim on a quantity that does not
predict, and ROW-GRAMMAR reserves red for settled-negative ONLY -- a
negative diagnostic is not a settled loss. Now neutral mono with a
diagnostic tooltip; header reads "MKT GAP · DIAGNOSTIC". The number is
still shown -- no display went blank. DeskShowcase neutralised likewise.
PINNACLE LOGGED, NOT ENSHRINED. Per the order, "market-not-sharp" is
PENDING-RECOVERY rather than a confirmed permanent limitation. The single
question for PropLine is in BLOCKERS.md with its evidence, and MASTER-PLAN
now carries the pending status instead of the permanent claim.
Live sorts remain byte-identical: selectTopGrades, flattenToEdgeBoard and
topGradedService all still call the incumbent.
Gates: 4,041 tests / 323 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
MEASURED BASIS (n=200 settled MLB rows): corr(p_win, outcome) = +0.26;
corr(edge, outcome) = -0.010 incumbent ruler / -0.022 consensus ruler.
Subtracting the market destroys the signal under BOTH rulers, so a
quantity that does not predict must not rank, gate or decide.
CHALLENGER-FIRST -- live ordering is byte-identical. rankGrades (the
incumbent, grade-first with edge as its 4th key) is untouched and tested
as untouched.
NEW: rankByForecast -- takeable-gated p_win -> grade -> confidence -> stable
order, with NO edge term anywhere. p_win LEADS and the letter follows,
deliberately: the letter measured r ~ 0.005 and is inverted (B 52.4% <
C 56.9%) while p_win measures +0.26, so leading with the letter would sort
by the weaker signal and use the stronger one only to break ties.
Recorded in the code: isotonic calibration is a MONOTONE transform, so
ranking on raw vs calibrated p_win gives the SAME ORDER. Calibration
matters when p_win is displayed or thresholded; it cannot change a
ranking. Nothing here needs the calibrated value.
rankingDelta + GET /api/internal/ranking-delta measure how far the board
would move before any flip. The endpoint reports p_win coverage alongside
the delta -- if p_win is absent the challenger degrades to grade order and
the delta UNDERSTATES, which is worth saying rather than reporting a clean
zero.
forecast_rank is stamped on snapshot grades BEFORE stripModelPrice, so
every tier gets the correct order without the paid values (the
topGradedService precedent -- an ordinal can travel where the magnitude
cannot). Additive only: nothing sorts by it yet.
RETIRED AS DECISIONS (not rankings, so done now):
- altLineScanner.compareToBookImplied no longer returns value_detected:
edge > 0. Edge is still COMPUTED and returned -- losing the record would
be worse than mis-using it -- but the verdict is an honest null with
value_basis: 'retired:edge_does_not_predict'.
- scanAltLines no longer filters to edge>0 or calls the survivor "optimal".
The whole ladder is returned ranked and labelled
'price_gap_diagnostic_unvalidated'. The module has ZERO callers (verified)
-- unwired like mlbGrader.js, left in place and made honest.
An honest asymmetry recorded there: ranking props AGAINST EACH OTHER must
not use edge, but choosing between RUNGS OF THE SAME PROP is inherently
price-relative -- ranking rungs by model probability alone would always
pick the lowest line, since P(over 0.5) > P(over 2.5) by construction. So
the gap stays the rung key, explicitly labelled unvalidated.
Two superseded tests updated to stronger properties.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc
IDENTITY (CLAUDE.md top + MASTER-PLAN header). VYNDR is a PREDICTIVE MODEL:
it projects what a player will DO and picks accurately. Market edge is a
BYPRODUCT of a good prediction, never the success criterion. Success =
the forecast is honest about its own confidence AND still ranks --
calibration and resolution, both. No edge/CLV term belongs in a pass/fail
gate; they are diagnostics we report, not thresholds a model must clear.
A model tuned to beat a closing line has been fitted to the market instead
of to the game.
Per-sport doctrine (Phillips 2022, classify by what players DO not by
position): each sport is its own model -- own variables, archetypes,
conditions, calibration, honest ceiling. Shared across sports: ONLY the
Bayesian inference math.
Truth Law: no fabricated data; honest-absent over invented; label
limitations in-band; provisional stays provisional until re-run;
documented is not verified.
PHASE 2 -- AGGREGATOR WIDENING (live). normalizeProps now emits every
DISPLAY book instead of 5 of 18. Before this we discarded 13 books of our
own accord and 64.8% of the MLB slate was invisible to users. Every prop
carries book_role (both/takeable/reference/dfs/offshore) so the display
layer can say WHAT a price is -- a fixed-payout DFS number and a two-way
sportsbook price are not interchangeable objects. Unknown books are still
dropped.
PHASE 3 -- MODEL GATE (the model does not move). bookRoles splits
MODEL_BOOKS (the legacy allow-list, character for character) from
DISPLAY_BOOKS. Both model paths re-filter before they pick a line:
gradeSlateService.dedupeProps (before first-row-wins AND before the limit)
and intradayRefreshService.indexOddsProps (which RE-GRADES at the current
line -- without the gate, widening would have silently moved locked lines
onto books the model has never been calibrated against). A test asserts
the graded set is byte-identical through the widening.
CURRENT_RULER_VERSION stays v1_first_book. The gate lifts only when the
MLB calibration is re-run on the consensus ruler and v2 is promoted.
HONEST FRAMING, recorded in the plan: this is an AGGREGATOR win and it
does NOT fix the model. WNBA still abstains -- a model problem, not a
coverage problem; it is better covered than MLB. MLB isotonic still
provisional. The consensus is MARKET, not SHARP: pinnacle, matchbook and
polymarket are 0% on both sports, so no sharp anchor exists in our feed.
Two superseded tests updated to stronger properties rather than deleted:
roleOf now names the KIND of book, and the normalizer test asserts the
display set widens WHILE the model set does not.
Gates: 4,027 tests / 322 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
Serving/gating change only. src/services/ untouched: no grade, model or
settlement-logic change. Pricing = Build 2, migration = Build 3.
WHY THE PRIOR GATE WAS WRONG: freeing grades at resolution made the free tier a
ONE-DAY-DELAYED FEED OF THE WHOLE PRODUCT — settlement is nightly, so a bettor
watching one cycle behind got the entire method free. There is now NO
per-grade resolution flip: an itemized grade, tonight's or last week's, is
Analyst+.
FREE now gets, none of it itemizing the nightly slate:
1. the full data aggregator (unchanged — schedule, per-book lines, stats,
streaks, hubs)
2. the AGGREGATE track record, which ALREADY EXISTS and is public:
/api/accuracy (sample 937, byGrade tiers, per-sport mlb+wnba, min_sample 20)
and /api/ledger/accuracy (per-grade buckets). The honest-record laws are
already honored there — A/D/F return pct:null under the n>=20 threshold
rather than a fake percentage.
3. a CAPPED, day-rotated sample of resolved calls for texture: cap 3, stable
within a day, rotates across days, and only RESOLVED rows are eligible so a
live read can never be sampled. The cap is what kills the exploit — three
rotating past calls cannot reconstruct a nightly slate, whereas the full
settled list is the feed one cycle late.
4. the locked shell of tonight's reads: they exist, and their shape.
EVERY itemized grade for an unentitled tier now loses grade, confidence,
confidence_basis, reasoning, kill_conditions_triggered, projection, edge_pct,
matchup_grade, form, alt_lines and kelly, and is stamped locked. Free-side DATA
survives so the board still reads as real: player, market, line, book_odds,
fair_odds (the de-vigged fair number is the free hook and is never the paywall),
season/last10 stats, archetype — and `outcome`, because a RESULT is a fact
rather than a judgment.
The tease stays aggregate-only (live_locked {count, tiers}) computed from the
ungated rows and never joined back to one, and no gated row carries a grade, so
nobody can work out which prop is the A.
Floor: 319 suites / 3970 tests green, web build exit 0.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc
Serving/gating change only. src/services/ untouched (git diff empty): no grade,
model or settlement-logic change. Pricing and migration are Builds 2 and 3.
Push scoring untouched.
THE RULE: a grade is PAID while its outcome is unknown and becomes FREE the
moment it resolves.
Resolution is read ONLY from a written outcome — never from time, game status or
gradedAt. A game can be final long before the settle pass runs, so treating
"probably over" as settled is exactly how a live edge would leak; a test asserts
an hours-old gradedAt with no outcome is still LIVE. void and unrecoverable ARE
resolutions (terminal results, no live edge left). isResolved FAILS CLOSED:
null outcome, {} with no result, and empty-string result all read as LIVE, so a
settlement failure withholds content rather than exposing it — the same
direction resolveTierFromRequest fails.
FREE/ANON: settled grades pass through IN FULL, reasoning and kill conditions
included — settled reads are the proof product and cost nothing once the outcome
is known. That also converts the previously-unenforced board reasoning leak into
a deliberate rule rather than an oversight.
LIVE grades for unentitled tiers are reduced to a shell: every piece of model
JUDGMENT is dropped (grade, confidence, confidence_basis, reasoning,
kill_conditions_triggered, projection, edge_pct, matchup_grade, form, alt_lines,
kelly) and `locked: true` is stamped so the card renders the unlock prompt. The
free-side DATA stays so the tease is real rather than empty: player, market,
line, book_odds, fair_odds, season/last10 stats, archetype, gradedAt, history.
fair_odds deliberately survives — the de-vigged fair number is the free hook and
is never the paywall. A test asserts the serialized free row carries no trace of
the withheld judgment.
THE TEASE IS AGGREGATE ONLY: live_locked = {count, tiers} computed from the
ungated rows and never joined back to one, and no gated row carries a grade — so
a free viewer learns that N reads exist and their tier shape without being able
to work out WHICH prop is the A.
Gate order in the route: stripModelPrice (S67) first, then gateLiveGrades.
Entitled tiers get the array back by reference — zero cost, zero change.
Floor: 319 suites / 3971 tests green (10 new), web build exit 0.
One test note: the route-level supertest case was removed deliberately — it
needs a live Redis and hangs on ioredis' reconnect timer in a single-suite local
run (known behaviour, CLAUDE.md). The gate contract is fully covered by pure
tests; the wire is verified against prod anonymously in the fingerprint.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc
New READ endpoint. No grade, ledger row, lock_line, or scoring write. Push
scoring untouched.
REVIEW ZERO CORRECTED THE PREMISE: the handler NEVER EXISTED in any commit
(searched git rev-list --all for a /top-graded definition in src/ — zero hits).
Not "removed" — the three axios callers (cheatsheetGenerator, gradeOfTheDay,
widget) and the Next proxy were written against a phantom endpoint, so those
three content generators have silently received [] for their entire life.
Contract recovered from the four consumers, not guessed: {props:[...]},
?sport=UPPERCASE (absent = all sports, which gradeOfTheDay relies on) + ?limit,
rows carrying player/stat/line/direction/sport/grade/confidence? plus the
player_name/stat_type aliases and game_id.
POPULATED-PATH RISK FOUND: the board's populated branch had never run in prod,
and dashboard/page.tsx:463 calls g.stat.replace(/_/g,' ') UNGUARDED (g.player
also feeds the row key, /scan URL and heading; sport must be UPPERCASE for
SportPill). toRow requires non-empty string player+stat and a finite line,
uppercases sport, and DROPS unrenderable rows — a shorter board beats a broken
one.
THE LEAK BOUNDARY (why this is server-side): the browser cannot rank on p_win
for all tiers because stripModelPrice deliberately withholds it from unentitled
tiers. Order of operations is
read cache -> RANK with p_win (every tier) -> map rows incl. model fields
-> stripModelPrice(rows, tier) -> serialize
so a free caller receives the paid RANKING without the paid VALUES. Tier comes
from resolveTierFromRequest, which FAILS CLOSED to 'free'. Cache-Control is
private under a bearer token, public otherwise (the /api/snapshot precedent).
ONE SHARED DEFINITION, no drift: new src/utils/gradeRanking.js
(takeablePWin/descNullsLast/rankGrades). heroPropService now imports
takeablePWin instead of its inline copy (behaviour unchanged — it was that
logic verbatim); the selector imports rankGrades; web/src/lib/slateAdapter
keeps its mirror (the browser cannot import src/, S25) and a test cross-checks
the two on identical fixtures (playerName.js precedent). Board is grade-first
("top GRADES"), hero is p_win-first ("top read") — they differ BY DESIGN and
agree within the leading tier.
HONEST LIMIT: the Next proxy (cachedBackendJson) sends no Authorization header
and caches under a shared key, so via the dashboard every viewer gets the
free-tier payload — correct order, no paid values. That is the SAFE behaviour;
forwarding auth into a shared cache is exactly how a paid payload leaks to
anonymous viewers. Per-tier delivery through the proxy needs a tier-keyed cache
and is not done here.
Verified on real prod snapshot data (anonymous path): MLB 8 props, WNBA 10,
0 paid-field leaks, render-contract safe on every row, sport uppercase.
Floor: 311 suites / 3882 tests green (18 new — leak test uses POPULATED p_win,
not today's nulls: entitled gets p_win and it drove the order, unentitled gets
a byte-identical order with all five MODEL_FIELDS absent and no trace in
JSON.stringify, while book/fair market facts survive). Web build exit 0.
Dashboard visual is auth-gated -> tagged for the Chrome audit, not faked.
Held: edge_pct rescale/retirement (Order B); board columns/contract unchanged;
tier-keyed proxy caching.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc
PHASE 0.5 GATE — the three checks, and one correction.
`fairLine` does not exist. Zero hits across src/ and web/src. Option A as
written had no referent, but it resolves better than feared: `fair_odds` is
already a real de-vigged American price on every graded snapshot row, so there
is nothing to derive.
Gate 1 (is it a price): PASS. fair_odds is American odds from
impliedProbToAmerican inside devigTwoWay; fair_prob is the probability. Both
distinct from `line`, the stat threshold.
Gate 2 (numeric match): PASS, 8/8 exact. Recomputed fair_odds and fair_prob
independently from the stored raw over/under prices; every value matched the
stored one to the integer and to 3dp. Same de-vig, same numbers the component
was proven against.
Gate 3 (poison independence): PASS, and proven on the quarantined cohort
itself. devigTwoWay's inputs are (over_odds, under_odds) — market prices
only, no model term is reachable. The 8 rows recomputed above are all
wrong_opponent_grade rows, and their fair prices reproduce exactly from the
market. The poison is in the grade, not the price. Quarantine therefore
suppresses the MODEL leg only; the fair leg stands, as designed.
THE LEAK WAS REAL AND ALREADY LIVE. GET /api/snapshot/:sport is public and
unauthenticated, and it was serving model_odds, p_win, ev_pct, value and
takeable to anonymous callers on every graded row — 25 of 25 on the live wnba
board. The Session-66 gate on /api/analyze was bypassed entirely by this
endpoint.
The strip covers more than model_odds, because model_odds is not the only way
to read the model price: p_win IS the price in another base, and ev_pct is
INVERTIBLE — ev is a function of p_win and book_odds, and book_odds is public,
so leaving ev behind hands the price over. All five model-derived fields go.
book_odds, fair_odds, fair_prob, overround and devig_method stay on every tier:
the fair leg is never the paywall. Rows that keep a book+fair pair are stamped
model_price_locked so a gated price is never mistaken for a missing one.
Tier comes from resolveTierFromRequest, which reads a bearer token when one is
present and otherwise returns 'free'. It FAILS CLOSED on every error path, so a
resolution failure can only ever withhold the price. The response now varies by
entitlement, so the /:sport handler downgrades Cache-Control to private for
authenticated callers and the browser proxy forwards the bearer token —
otherwise a CDN could hand a paid payload to an anonymous viewer, or every
request would look anonymous and paid users would lose the leg.
READ CARD — a manual scan carries no market. The request is {player, stat,
line, direction}, so the engine has no over/under prices to de-vig and
book_odds/fair_odds are legitimately absent from its response; that is why the
triplet was hidden there. lookupSnapshotPrices recovers them from the
pre-graded snapshot via the same cache-only read this route already performs
for locked odds and team. The join is exact on player + stat + line + side
(fair_odds is side-specific), and returns nothing unless book and fair are BOTH
present — a user-chosen line the board never graded has no market attached, so
the triplet stays hidden rather than borrowing another line's price.
FAIR-LEG ABSENCE, measured before shipping: 636 graded rows, 636 with book,
636 with fair, 0 one-sided. Absence rate 0.0%. The hero number is not a
sometimes-number on current data.
Tests 3556 passed / 291 suites, web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj
PHASE 0 finding, reported before building: the token layer this order asked
me to establish ALREADY EXISTS and already matches HANDOFF.md exactly.
web/src/app/globals.css :root carries the design's surfaces, borders, text
ramp, fonts and grade colours byte-for-byte (aligned 2026-07-16), and
lib/colorContract.js already encodes the green-is-edge-only and
glow-is-A-tier-only laws with a test enforcing them. The stack is Tailwind v4
CSS-first (no config file) with components styled by inline style={{}} reading
var(--x) — 1,916 such reads — so CSS custom properties are the only vehicle
the stack natively consumes. Creating a second parallel layer would have meant
two competing sources of truth, so this EXTENDS the existing one.
PHASE 1 — additive only. globals.css gains one colour the system did not have,
the priced-out blue (#8fb2de + tints), plus a tokenized A-tier glow and the
fair-leg tints. The block writes the LAWS into the token layer itself — green
= takeable edge only, glow = A-tier only, amber = caution + the fair leg, red
= miss/negative only, blue = edge priced out, JetBrains Mono = all data — and
a test asserts every newly-declared name is new (zero collisions, zero
overrides). No existing hardcoded style was touched and no live surface was
migrated: the diff over existing files is 149 insertions, 0 deletions.
PHASE 2 — lib/valueState.js is the single verdict function; the component
renders what it returns and never re-derives one. VALUE fires only on
ev >= 2 AND a takeable price, mirroring src/config/valueEngine.js with a test
that cross-checks both files and fails on drift. Five states: VALUE (green),
EDGE-NOT-TAKEABLE (blue), NO EDGE (grey, stated at full voice), QUARANTINE
(model leg withheld, book+fair stand), REFUSAL (nothing rendered). Free tier
is gated at the wire — tierGating strips model_odds and sets
model_price_locked, so the lock is real rather than a blur over data already
sent; book and fair pass through on every tier because the fair leg is never
the paywall. Wired into the landing hero (data was already on /api/hero-prop)
and the read card, where the projection block reads first and the triplet sits
beside it, not in place of it. Ledger and public profile are out of scope —
no fair-odds columns exist there.
PHASE 3 — induced all six states in a real browser and read back computed
styles, not just markup. Green resolves on VALUE alone: rgb(0,212,160) on the
model leg and verdict; the +11.7%-EV-at-210 row renders rgb(143,178,222) blue
and a white model leg; quarantine shows MODEL "—" with book and fair intact;
refusal renders no legs at all; free tier renders a lock bar with book -120 and
fair -104 still honest. Landing hero on live data: book -153, fair -129, model
-343, VALUE +21.1% vs fair at +28.1% EV. At a real 390px column the three legs
hold at 117px each with no horizontal overflow and fair no smaller than its
neighbours.
Induction caught a real bug that markup review would not have: the "VS FAIR"
figure compared BOOK to fair, printing "VALUE · -6.5% VS FAIR" — a
contradiction on screen. The design's own two worked examples pin the formula
as MODEL minus FAIR in implied-probability percentage points; modelVsFair now
reproduces both exactly (+2.9 and -1.8) and a test locks them. The figure is
shown only when its sign agrees with the verdict, so a row that clears the EV
bar on the book price while our price sits level with fair leads with the EV
instead of a number that reads as a contradiction.
Tests 3539 passed / 290 suites, web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj
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
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>
ESPN BET is defunct — PENN/ESPN terminated the deal; PENN rebranded it to
theScore Bet (Dec 1 2025) and ESPN is now exclusive with DraftKings. Removed
the ESPN BET entries from the BookChip map (web/src/lib/books.js) and added
theScore Bet (mono TS, slug thescore) as the successor. Added 'thescore' to the
backend oddsNormalizer ALLOWED_BOOKS so the feed's lines are accepted; synced
the bookWordmark test list. The ESPN references in src/config/sports.js are
ESPN's STATS API (data provider, unrelated to the sportsbook) — left untouched.
Flagged in specs/design-reference/HANDOFF.md that the design mockups' BookChip
row still shows ESPN BET and needs the same one-swap on the next refresh.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
FREE ESPN news wire + championship-winner futures for the never-dark
offseason hub. Both graceful/empty, never fabricate a market value.
- newsService (mirrors injuryService): per-sport ESPN /news FEEDS, pure
parseNews → { sport, items:[{id,headline,description,published,type,
athlete?{name,key},team?,href}] }; athlete/team from categories[] only
(absent when not present). Cache 15m, injectable, offline-tested.
- oddsNormalizer.normalizeOutrights: NEW branch — outrights outcomes are
{name,price} with no point, so normalizeProps drops them; keeps them with
best-price-across-allowed-books per selection. + americanToDecimal.
- oddsService.FUTURES_KEYS: separate map (mlb/nba/wnba championship winner),
OUT of the daily SPORT_KEYS/snapshot budget.
- futuresService: getFutures(sport,deps) → { sport, updated_at, markets:
[{key,title,selections:[{name,price,prevPrice?,move?}]}] }. One outrights
call per 12h TTL (quota-disciplined), FUTURES_ENABLED gate. Price-move
(shortening/drifting/flat) mirrors computeLineDeltas SHAPE on odds not
line; prev prices persisted inside the futures:{sport} value (no new key).
linkNewsToMoves pure causal-tie helper.
- Routes /api/news/:sport + /api/futures/:sport (registered) + Next proxies.
- Tests: newsService, futuresService, oddsNormalizerOutrights (fail→pass,
no network). Full suite green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
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>
- 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>
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>
- gradeSlateService writes grades:{sport} cache (closes content pipeline →
dataLevel full); fire-and-forget from oddsService.recordDownstream, gated
by shouldGradeSlate (off in test, GRADE_SLATE_ON_FETCH override)
- NFL/NHL wired: oddsService SPORT_KEYS/SPORT_MARKETS (correct the-odds-api
keys americanfootball_nfl/icehockey_nhl), proplineAdapter MARKETS, NHL
MARKET_MAP keys to avoid silent-zero
- rate limiting mounted on 8 public cached routers (odds/parlay 30/min,
rest 60/min)
- jsonlLogger writes to temp under test (no more dirtied tracked artifact);
5MB pipeline test given 20s timeout
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Add NFL keys to oddsNormalizer.MARKET_MAP (defensive; same silent-zero
class as the Session 30 MLB bug) + NFL surface test
- npm audit fix: ws/qs + Supabase transitives, 7 vulns -> 0 (semver-safe)
- Audit findings documented in BUILD-STATE: grades cache has no writer,
NFL/NHL not wired end-to-end, rate limiting only on /analyze, tests
mutate a tracked jsonl, leaked GitHub PAT in origin remote (rotate)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Core intelligence for BetonBLK prop analysis:
- POST /api/analyze/prop — single prop analysis
- POST /api/analyze/batch — multi-prop analysis for parlay scanner
- 6-step pipeline: season avg → recent form → situational splits →
cross-book lines → kill conditions → grade (A/B/C/D)
- 6 kill conditions: low_minutes, small_sample, b2b_high_usage,
blowout_risk, split_conflict, no_opponent_data
- Composite scoring with confidence (30-95), bonuses, penalties
- Added spreads market to Odds API fetch (zero extra credits)
- Full reasoning output with step-by-step breakdown
36 new tests (unit + integration), 128 total across all features
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
builtbykev