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builtbykev	6c97f59546	WNBA truth correction + THE p_win FLIP (live, rollback armed) PART A -- WNBA TRUTH CORRECTION (no behaviour change). WNBA does not "abstain" and is not "anti-predictive". The -0.12 that produced those words was NBA-template machinery run on WNBA data -- WNBA has never had its own archetypes, variables, conditions or calibration, which is precisely the "sport stubbed in on another sport's template" CLAUDE.md forbids. That is an UNBUILT MODEL'S EXPECTED FAILURE, not a verdict on the sport; reading it as a verdict would quietly retire a sport we never actually attempted. Its own build is QUEUED, after MLB. The guard CODE is unchanged -- FORECAST_RANKED_SPORTS = {'mlb'} and the inheritance test are correct live safety either way. Only the meaning is corrected, and generalised into the doctrine-as-a-gate: a sport ranks on p_win ONLY once its OWN model is built and shown to predict (calibration AND resolution on its own holdout). Others are held out as NOT-BUILT, never as failed. Re-labelled across gradeRanking, snapshot route, tests, MASTER-PLAN and the challenger report. PART B -- THE FLIP, gated on a full-slate re-run. The re-run found something better than a bigger sample. An induced snapshot graded 7 props: gradeAndCacheSlate runs with DEFAULT_LIMIT = 25 and ~72% of those refuse for insufficient_data, while 546 props are gradeable. So 8 props IS the board, structurally -- not a small sample of it. Logged as its own finding; the cap is a separate order. For a statistically meaningful delta I used 11 real historical boards (n=328, board sizes 14-57): 79.9% of rows move, mean 5.16 places per board, TOP READ CHANGES ON 9 OF 11 BOARDS. The re-ordering holds at real board size. Query committed. FLIPPED: - rankGrades drops its edge key (safe for every sport: removes a non-predictive tiebreak without putting p_win in front). - selectTopGrades leads on forecast_rank, edge key removed. - flattenToEdgeBoard sorts on forecastRank, not edge -- this board had edge as its PRIMARY key, so the whole mobile board was ordered by a quantity measured not to predict. - forecast_rank threaded onto strip props. Sports whose model is not built supply no forecast_rank, so their boards fall through to the unchanged grade chain -- the fallback is the guard. ROLLBACK ARMED: boards sort by forecast_rank WHEN PRESENT, so FORECAST_RANK=0 reverts every surface on the next response -- no deploy, no client release. Edge is still computed, stored, carried and displayed as a labelled diagnostic. Retired from ranking, not deleted. Eight superseded tests updated to strictly stronger INVERSE properties -- they now fail if edge is ever re-introduced as a ranking key, which the originals could not detect. Gates: 4,045 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	2026-08-01 01:55:43 -04:00
builtbykev	ef4ac60b81	Per-sport rank guard + edge diagnostic-only display + delta report 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	2026-08-01 01:29:20 -04:00
builtbykev	86d123945c	Rank on p_win: challenger instrument + retire edge from decisions 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	2026-08-01 01:24:55 -04:00

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Date

builtbykev

6c97f59546

WNBA truth correction + THE p_win FLIP (live, rollback armed)

PART A -- WNBA TRUTH CORRECTION (no behaviour change).
WNBA does not "abstain" and is not "anti-predictive". The -0.12 that
produced those words was NBA-template machinery run on WNBA data -- WNBA
has never had its own archetypes, variables, conditions or calibration,
which is precisely the "sport stubbed in on another sport's template"
CLAUDE.md forbids. That is an UNBUILT MODEL'S EXPECTED FAILURE, not a
verdict on the sport; reading it as a verdict would quietly retire a sport
we never actually attempted. Its own build is QUEUED, after MLB.

The guard CODE is unchanged -- FORECAST_RANKED_SPORTS = {'mlb'} and the
inheritance test are correct live safety either way. Only the meaning is
corrected, and generalised into the doctrine-as-a-gate: a sport ranks on
p_win ONLY once its OWN model is built and shown to predict (calibration
AND resolution on its own holdout). Others are held out as NOT-BUILT,
never as failed. Re-labelled across gradeRanking, snapshot route, tests,
MASTER-PLAN and the challenger report.

PART B -- THE FLIP, gated on a full-slate re-run.

The re-run found something better than a bigger sample. An induced
snapshot graded 7 props: gradeAndCacheSlate runs with DEFAULT_LIMIT = 25
and ~72% of those refuse for insufficient_data, while 546 props are
gradeable. So 8 props IS the board, structurally -- not a small sample of
it. Logged as its own finding; the cap is a separate order.

For a statistically meaningful delta I used 11 real historical boards
(n=328, board sizes 14-57): 79.9% of rows move, mean 5.16 places per
board, TOP READ CHANGES ON 9 OF 11 BOARDS. The re-ordering holds at real
board size. Query committed.

FLIPPED:
- rankGrades drops its edge key (safe for every sport: removes a
  non-predictive tiebreak without putting p_win in front).
- selectTopGrades leads on forecast_rank, edge key removed.
- flattenToEdgeBoard sorts on forecastRank, not edge -- this board had
  edge as its PRIMARY key, so the whole mobile board was ordered by a
  quantity measured not to predict.
- forecast_rank threaded onto strip props.

Sports whose model is not built supply no forecast_rank, so their boards
fall through to the unchanged grade chain -- the fallback is the guard.

ROLLBACK ARMED: boards sort by forecast_rank WHEN PRESENT, so
FORECAST_RANK=0 reverts every surface on the next response -- no deploy,
no client release.

Edge is still computed, stored, carried and displayed as a labelled
diagnostic. Retired from ranking, not deleted.

Eight superseded tests updated to strictly stronger INVERSE properties --
they now fail if edge is ever re-introduced as a ranking key, which the
originals could not detect.

Gates: 4,045 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

2026-08-01 01:55:43 -04:00

builtbykev

ef4ac60b81

Per-sport rank guard + edge diagnostic-only display + delta report

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

2026-08-01 01:29:20 -04:00

builtbykev

86d123945c

Rank on p_win: challenger instrument + retire edge from decisions

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

2026-08-01 01:24:55 -04:00

3 Commits