Same Winners, Different Quality: Checking Forecast Calibration
Two football models can pick the same winners and still differ sharply in quality. A worked Brier-score example shows how to check confidence, not just accuracy.
Deep dives into football quantitative modeling, expected goals (xG) audits, transparency protocols, and empirical evaluations of AI predictive claims.
Two football models can pick the same winners and still differ sharply in quality. A worked Brier-score example shows how to check confidence, not just accuracy.
A football backtest can look accurate because it uses information unavailable before kickoff. Check timestamps, revised data, rolling windows and validation splits.
What football forecast archives should preserve: pre-match versions, clear scoring rules, failed calls and correction history, not just a headline hit rate.
Why a football accuracy claim needs a defined sample, a baseline and uncertainty, and why Poisson models do not impose a universal 65% upper limit.