Cross-individual generalizability of machine learning models for ball speed prediction in baseball pitching
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917466707329024 |
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| author | Takamido, Ryota Suzuki, Chiharu Nakamoto, Hiroki |
| author_facet | Takamido, Ryota Suzuki, Chiharu Nakamoto, Hiroki |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_05487 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cross-individual generalizability of machine learning models for ball speed prediction in baseball pitching Takamido, Ryota Suzuki, Chiharu Nakamoto, Hiroki Human-Computer Interaction 68T99 J.3 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. |
| title | Cross-individual generalizability of machine learning models for ball speed prediction in baseball pitching |
| topic | Human-Computer Interaction 68T99 J.3 |
| url | https://arxiv.org/abs/2605.05487 |