The order's own prerequisite for the hit-parlay surface was to verify the hit
probability is calibrated. It is not, and the failure is exactly the shape that
destroys a parlay.
Measured on 972 settled hits props: the model is monotonically over-confident
at the top and flat above 0.70. Predicted 0.911 comes back 0.630. Predicted
0.844 comes back 0.630. Predicted 0.747 comes back 0.605. There is no
discrimination at all in the range a parlay is built from, and the error runs
in the flattering direction. Four "91%" legs are 0.686 by the model and 0.157
in fact -- a 4.4x overstatement that compounds with every leg added.
Single props survive a calibration error of that size. A parlay multiplies it.
So chainAcross REFUSES to compound atoms not marked calibrated, and refusing is
the feature rather than a limitation: a ticket built on these numbers would be
confidently wrong in the direction the user pays for.
calibration.js provides the reliability table, the gate (tolerance 0.05,
weighted to the high end because that is where tickets live) and an isotonic
fit. Isotonic is the honest repair here because it is monotone: the model's
ordering survives untouched while the numbers move to what actually happened.
The fitted map says 0.65 -> 0.594, 0.85 -> 0.639, 0.91 -> 0.639.
chain.js is the portable core -- base events plus context, through a chain
function, into a PLUGGABLE aggregator: across players for a compound ticket, up
to the team for expected scoring. The sport-specific parts are inputs rather
than code paths, so basketball plugs in as content. The archetype
redistribution hook is there now, dormant in baseball because a nine-run lead
does not change who bats next, and live in basketball where a blowout fades the
star and feeds the bench.
Two judgement calls worth naming. Treating same-game legs as independent errs
in the FLATTERING direction, since they share pitcher, park and weather -- so
correlation shifts the compound toward the weakest leg, bounded, and is labelled
an approximation rather than a joint distribution. And market divergence does
NOT downgrade confidence: it flags a contested script whose props are either the
best or the worst on the board, and which one is unknown until settled.
Internal inconsistency does downgrade it, because per-entity reads failing to
sum to the team read means one of them is wrong and we do not know which.
Not built: the independent game-script projection. It needs proven team-level
atoms and out-of-sample validation against actual margins, and no atom has
passed the gate yet. Building it now would produce something plausible rather
than something proven, which is the failure mode this whole programme exists to
avoid.
4,269 tests green (339 suites); web build exit 0; counter and frozen clusters
byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9
builtbykev
9c5b968351