Machine learning for sports betting: Should model selection be based on accuracy or calibration?
Selecting models by calibration produced better simulated betting outcomes than selecting by accuracy. After the 2025 corrigendum, average returns were negative for both selection rules (about −9.8% for calibration versus −26.8% for accuracy), while the best single models reached about +23.1% and +10.9%.
Does not show: That calibrated models are profitable; the corrected average returns were negative.
Correction: Corrigendum, Machine Learning with Applications 19 (2025), 100627 (doi:10.1016/j.mlwa.2025.100627). The original 2024 return figures were withdrawn; use the corrected values.