calibration

Calibration data for a deployed model

Returns the latest _deploy.json calibration payload published by the relevant AI pipeline. Use GET /v1/models to list valid (sport, model_id) pairs. The shape under data is sport- and task-specific — see the source data on the methodology page.

get/v1/calibration/{sport}/{model_id}

Request

  • Base URL: https://api.predixsport.com (the document declares no server; this is the origin it was published from)
  • URL: https://api.predixsport.com/v1/calibration/{sport}/{model_id}
  • Auth: none declared

Path parameters

sport'nba' | 'euroleague' | 'football' | 'tennis' | 'wta' | 'champions' required
model_idstring required

Headers

authorizationstring nullable

Response

Successful Response

sport'nba' | 'euroleague' | 'football' | 'tennis' | 'wta' | 'champions' required
model_idstring required
source_filestring required

Filename the payload was read from. Either the upstream *_deploy.json (preferred — carries a timestamp in the filename) or, when the upstream copy is missing, the blessed <short>.json synced to web/static/data/calibration.

generated_atstring nullable

Sortable ISO timestamp ("YYYY-MM-DD HH:MM:SS") for the source. Parsed from the filename when read upstream; from the timestamp field in the JSON payload when read from the blessed copy. null when neither carries one.

dataobject required

Calibration metrics as produced by the AI module's calculate_calibration pipeline. Shape depends on model type: binary classifiers return reliability bins; multiclass models return per_class arrays.

READING GUIDE — two independent metric families: • Calibration (does the stated probability match observed frequency?): ece, calibration_mae, calibration_max_error, coverage_90. A well-calibrated Poisson regressor has coverage_90 ~0.90 and small calibration_mae; for noisy sport-stat regression coverage_90 0.78-0.85 with calibration_mae 0.05-0.10 is OPERATIONALLY ACCEPTABLE. • Accuracy (how close is the point estimate to truth?): correlation, mae, rmse, brier_score, accuracy. Independent of calibration — a low correlation on a Poisson regressor reflects high irreducible variance in the target, NOT miscalibration.

There is intentionally NO binary 'pass / fail' verdict in this payload: judging a model from a strict threshold misleads more than it informs on noisy sport-stat domains. Interpret the numeric metrics in context.

summary.source names the upstream file stem the numbers were measured on. It matters where one model id has been served by successive training heads: the NBA per-player models read player_points_full_roster once the full-roster heads are deployed and player_points before that, and the metrics describe the population of the stem that is named.

Changes

No recorded changes to this operation across all 1 revision of this API.