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Analytics module · as of 2026-07-18

One methodology, one buildable sport: NBA's market is near-perfectly calibrated

Same methodology, per-sport n honest. nba: market ECE=0.006 (n_games=1593), fav_gap=+0.005/dog_gap=-0.001 (favorite-longshot-consistent), comeback~0.015. Not buildable: mlb, soccer, tennis.
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Xsport Structure
Chart: Xsport Structure -- Same methodology, per-sport n honest. nba: market ECE=0.006 (n_games=1593), fav_gap=+0.005/dog_gap=-0.001 (favorite-longshot-consistent), comeback~0.015. Not buildable: mlb, soccer, tennis.
scripts/platformkit/analytics_showcase/out/xsport_structure.json2026-07-18

What it means

The same three-part check (calibration, favorite-vs-longshot bias, comeback rate) is run per sport. NBA passes: market ECE 0.0064, and favorites at 0.60-plus win 0.8997 of the time against a 0.8948 implied price, a gap of just +0.005. MLB, soccer and tennis lacked buckets meeting the n>=30 floor, so they are marked not_buildable rather than guessed.

Caveats & confounds

This is a single-sport result today (NBA only); MLB, soccer and tennis are not_buildable, and tennis has no reliability map at all.

Method. one reliability-map methodology across sports: tick-weighted market ECE/Brier (b), favorite (>=0.60) vs longshot (<=0.40) outcome-minus-implied gap (a), near-decided (>=0.85) favorite-loses comeback rate (c)

Ask Scout about this
Which sport has the sharpest market by calibration error?Is there a favorite-longshot bias in the NBA market?Which sport should I trust these numbers on most?Ask anything →