Cross-individual generalizability of machine learning models for ball speed prediction in baseball pitching

TL;DR

This study evaluates how well machine learning models for predicting baseball pitch ball speed generalize across different individuals, highlighting key biomechanical factors and the impact of expertise levels.

Contribution

It provides empirical insights into the cross-individual generalizability of ML models in sports, emphasizing the importance of evaluation methods and biomechanical features.

Findings

Predictive performance drops significantly in cross-individual evaluation (R-squared from 0.91 to 0.38).

Intermediate pitchers' performance is overestimated compared to experts.

Pivot leg and trunk show relatively high generalization performance.

Abstract

Although machine learning (ML)-based performance outcome prediction is an important topic in contemporary sports science, one important issue is the limited understanding of the cross-individual generalizability of ML models in sports contexts. To address this issue, this study aimed to evaluate the cross-individual generalizability of ML models for predicting ball speed in baseball pitching. A dataset comprising 50 pitchers from various competitive levels was analyzed. Cross-individual generalizability was assessed using leave-one-subject-out cross-validation. Specifically, the effects of expertise level and restrictions on spatiotemporal motion information were examined to identify factors influencing model generalizability. The results revealed that, under cross-individual evaluation, (1) predictive performance was markedly lower than under within-individual evaluation, with R-squared value decreasing from 0.91 to 0.38; (2) the model tended to overestimate the performance of Intermediate pitchers relative to Expert pitchers, with a significant group difference in signed prediction error (p < .05); and (3) the trunk and pivot leg demonstrated relatively high generalization performance, with the pivot leg showing notable generalizability even during the weight-shift initiation phase (R-squared value > 0.25). These findings underscore the importance of cross-individual evaluation in enhancing the practical applicability of ML in sports settings and contribute to a deeper understanding of the biomechanical factors underlying the target movement.