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
This commit is contained in:
Kev
2026-08-01 01:24:55 -04:00
parent 7140e62b65
commit 86d123945c
6 changed files with 341 additions and 27 deletions
+21 -6
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@@ -150,15 +150,25 @@ describe('Intelligence Engine', () => {
expect(prob).toBeCloseTo(0.5, 1);
});
test('compareToBookImplied detects value', () => {
// SUPERSEDED 2026-08-01. This asserted `value_detected === true` from a
// positive edge. Edge does not predict outcomes (n=200 settled MLB rows:
// corr -0.010 incumbent ruler / -0.022 consensus, vs corr(p_win) = +0.26),
// so it must not decide anything. The stronger property: edge is still
// COMPUTED and returned as a diagnostic (losing the record would be worse
// than mis-using it), while the verdict is an honest null with a reason.
test('compareToBookImplied returns edge as a DIAGNOSTIC and refuses a verdict', () => {
const result = compareToBookImplied(0.60, -110);
expect(result.model_prob).toBe(0.6);
expect(result.book_implied).toBeCloseTo(0.524, 2);
expect(result.value_detected).toBe(true);
expect(result.edge).toBeGreaterThan(0);
expect(result.edge).toBeGreaterThan(0); // still recorded
expect(result.value_detected).toBeNull(); // never a boolean verdict
expect(result.value_basis).toBe('retired:edge_does_not_predict');
});
test('scanAltLines returns optimal line with edge', () => {
// SUPERSEDED 2026-08-01: 'optimal' was a quality claim edge cannot support,
// and filtering to edge>0 hid rungs. The ladder is now returned whole,
// ranked, and labelled as an unvalidated price diagnostic.
test('scanAltLines returns the whole ladder ranked, labelled unvalidated', () => {
const prop = { projected_mean: 25, projected_stddev: 5, direction: 'over' };
const odds = [
{ line: 22.5, odds: -130, book: 'draftkings' },
@@ -167,8 +177,13 @@ describe('Intelligence Engine', () => {
];
const result = scanAltLines(prop, odds);
expect(result).not.toBeNull();
expect(result.optimal_line).toBeDefined();
expect(result.edge).toBeGreaterThan(0);
expect(result.top_by_price_gap).toBeDefined();
expect(result.ranking_basis).toBe('price_gap_diagnostic_unvalidated');
// every rung survives — a negative gap is an observation, not a reason to hide
expect(result.ranked_lines).toHaveLength(odds.length);
expect(result.ranked_lines[0].edge).toBeGreaterThanOrEqual(result.ranked_lines[1].edge);
// no boolean verdict anywhere in the payload
expect(result.ranked_lines.every((r) => r.value_detected === undefined)).toBe(true);
});
});
+98
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@@ -0,0 +1,98 @@
'use strict';
/**
* Ranking instrument — challenger vs incumbent (2026-08-01).
*
* These lock the reason the challenger exists: measured on n=200 settled MLB
* rows, corr(p_win, outcome) = +0.26 while corr(edge, outcome) = -0.010 under
* the incumbent ruler and -0.022 under the consensus ruler. A quantity that
* does not predict must not rank, gate or decide.
*/
const {
rankGrades, rankByForecast, rankingDelta, gradeKey, takeablePWin,
} = require('../../src/utils/gradeRanking');
const g = (player, grade, p_win, odds = -110, extra = {}) => ({
player, stat_type: 'hits', line: 1.5, direction: 'over',
grade, p_win, book_odds: odds, ...extra,
});
describe('rankByForecast — the challenger', () => {
it('contains NO edge term: edge cannot move the order at all', () => {
const a = [g('A', 'B', 0.62, -110, { edge: -99 }), g('B', 'B', 0.55, -110, { edge: +99 })];
const b = [g('A', 'B', 0.62, -110, { edge: +99 }), g('B', 'B', 0.55, -110, { edge: -99 })];
expect(rankByForecast(a).map((x) => x.player)).toEqual(['A', 'B']);
expect(rankByForecast(b).map((x) => x.player)).toEqual(['A', 'B']);
});
it('leads with p_win, not the grade letter', () => {
// The letter measured r ~ 0.005 and is INVERTED; p_win measures +0.26.
// A high-p_win C must outrank a low-p_win A.
const out = rankByForecast([g('lowPwinA', 'A', 0.51), g('highPwinC', 'C', 0.74)]);
expect(out[0].player).toBe('highPwinC');
});
it('keeps the takeable gate — raw p_win would crown chalk', () => {
const chalk = g('chalk', 'A', 0.93, -300); // untakeable price
const real = g('real', 'B', 0.61, -115);
expect(takeablePWin(chalk)).toBeNull();
expect(rankByForecast([chalk, real])[0].player).toBe('real');
});
it('sorts a missing p_win LAST, never first (Number(null) === 0 guard)', () => {
const out = rankByForecast([g('none', 'A', null), g('has', 'C', 0.58)]);
expect(out.map((x) => x.player)).toEqual(['has', 'none']);
});
it('is stable for genuinely tied rows', () => {
const rows = [g('first', 'B', 0.6), g('second', 'B', 0.6)];
expect(rankByForecast(rows).map((x) => x.player)).toEqual(['first', 'second']);
});
it('drops ungraded rows, like the incumbent', () => {
expect(rankByForecast([g('x', null, 0.9), g('y', 'B', 0.5)]).map((r) => r.player)).toEqual(['y']);
});
});
describe('rankGrades — the incumbent is UNTOUCHED (live ordering byte-identical)', () => {
it('still leads with the grade letter and still consults edge', () => {
const out = rankGrades([g('lowPwinA', 'A', 0.51), g('highPwinC', 'C', 0.74)]);
expect(out[0].player).toBe('lowPwinA'); // grade-first, unchanged
});
it('edge still breaks a true tie in the incumbent', () => {
const out = rankGrades([
g('lowEdge', 'B', 0.6, -110, { edge: 1 }),
g('highEdge', 'B', 0.6, -110, { edge: 9 }),
]);
expect(out[0].player).toBe('highEdge');
});
});
describe('rankingDelta — the challenger-first measurement', () => {
it('reports how far the board moves and whether the top read changes', () => {
const rows = [g('A', 'A', 0.52), g('B', 'C', 0.77), g('C', 'B', 0.64)];
const d = rankingDelta(rows, 3);
expect(d.n).toBe(3);
expect(d.incumbent_top).toBe(gradeKey(rows[0])); // A-grade leads incumbent
expect(d.challenger_top).toBe(gradeKey(rows[1])); // highest p_win leads challenger
expect(d.top_changed).toBe(true);
expect(d.moved).toBeGreaterThan(0);
});
it('reports zero movement when both instruments agree', () => {
const rows = [g('A', 'A', 0.80), g('B', 'B', 0.60), g('C', 'C', 0.40)];
const d = rankingDelta(rows, 3);
expect(d.moved).toBe(0);
expect(d.top_changed).toBe(false);
expect(d.top_n_overlap_pct).toBe(100);
});
it('changes nothing about the inputs (pure)', () => {
const rows = [g('A', 'A', 0.52), g('B', 'C', 0.77)];
const snapshot = JSON.stringify(rows);
rankingDelta(rows);
expect(JSON.stringify(rows)).toBe(snapshot);
});
});