Risk-Guided Maintenance of Sports AI Under Seasonal Shift: Empirical-Bayes Recalibration with Anytime-Valid Certification

Probabilistic artificial-intelligence (AI) models in sports are often reused across seasons despite changes in teams and context. We develop Monotone Empirical-Bayes Transport Recalibration (MEBTR), a model-agnostic maintenance layer that partially pools local calibration maps, requires a prespecified worthwhile Brier-risk improvement before updating, and monitors gains with anytime-valid e-processes. A predictable-support construction sharpens the universal Brier-difference bound using outcome-free bounds known once both forecasts are frozen. Across logistic regression, random forest, histogram gradient boosting (HGB), and multilayer perceptron learners, 400 paired replications retain Raw AI in 94.25–96.00% of exact-identity runs and select local MEBTR in 82.50–85.50% of affine-shift runs, improving guarded Brier risk by 3.27–3.47% under shift. In leakage-audited NCAA basketball, prospective HGB recalibration improves Brier from 0.224853 to 0.224395, calibration slope from 0.9489 to 0.9883, and ICI from 0.02182 to 0.02050. A 10,000-game bootstrap confirms positive average gain, but the prespecified δ = 0.0005 threshold is not anytime certified. Whole-game-disjoint and repeated-season audits show why the risk guard is necessary: candidate improvements can be small and finite-sample action selection is not oracle model selection.