Cross-individual generalizability of machine learning models for ball speed prediction in baseball pitching
This study evaluates the cross-individual generalizability of machine learning models for predicting baseball pitch speed across 50 pitchers, revealing that while within-individual performance is high, generalization drops significantly and varies by expertise level and specific body segments, highlighting the need for cross-individual validation to improve practical applicability in sports science.