Audit an AI football prediction
A seven-part test for publication proof, model identity, evaluation quality and a complete result history.
Evidence-first learning centre
Football prediction guides built for verification
Start with the probability, then follow its evidence. These guides explain what a forecast says, how a genuine model should be tested and which public records are needed before an accuracy claim is worth attention.
Published by Football Proof AI · Updated · Educational material, not betting adviceStart here
Read the number before judging the prediction
A model output is a distribution of possible outcomes. The first task is to understand that distribution; the second is to verify how it was produced and recorded.
Read football probabilities
Interpret a complete home-draw-away split, calibration, fair odds and Brier score without treating uncertainty as certainty.
Use the probability and Brier calculator
Rebuild a three-outcome Brier score and see how one result changes the error assigned to a probability split.
Inspect the system
Separate model evidence from AI presentation
A polished explanation cannot validate a probability. Model inputs, time-ordered evaluation, artifact identity and data limitations must remain inspectable independently of the interface that describes them.
Audit a football prediction record
Paste or upload a complete 1X2 history and audit timing, exclusions, hit rate, uncertainty, Brier, log loss and calibration locally.
Measure football prediction accuracy
Reproduce hit rate, multiclass Brier score, calibration, ECE, log loss and Wilson intervals from one worked 1X2 forecast.
Validate a football model through time
Build rolling-origin folds, point-in-time features, separate calibration windows and fixed gates without future leakage.
Inspect the model method
Follow the feature window, leakage controls, LightGBM estimate, isotonic calibration and walk-forward acceptance gates.
Open the public model cards
Check registered model versions, evaluation windows, sample sizes, metrics and immutable artifact fingerprints.
Review data sources and provenance
See which fixture, historical, model and settlement data enters the product—and which inputs remain deliberately disabled.
Verify the record
Use results, revisions and limitations together
Accuracy is credible only when misses remain visible, corrections preserve the original call and responsible-use limits sit beside the forecast rather than behind it.
Audit the public accuracy ledger
Audit every eligible settled forecast, league and model split, calibration bucket and row-level result.
Read the corrections policy
Understand how later score corrections are appended without silently rewriting the original probability record.
Study Probability Neighbours
Compare complete probability shapes using only historical rows that had settled before the target was published.
Use predictions responsibly
Keep uncertain model output separate from staking, profit promises and harmful gambling behaviour.