Chiharu Suzuki

Ryota Takamido, Chiharu Suzuki, Hiroki Nakamoto

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.

Ryota Takamido, Chiharu Suzuki, Jun Ota et al.

Background: Simultaneously focusing on intra- and inter-individual body dynamics and elucidating how these affect each other will help understand human inter-personal coordination behavior. However, this association has not been investigated previously owing to difficulties in analyzing complex causal relations among several body components.To address this issue, this study proposes a new analytical framework that attempts to understand the underlying causal structures behind each joint movement of individual baseball players using neural Granger causality (NGC) as the explainable AI. Methods: In the NGC analysis, causal relationships were defined as the size of the weight parameters of the first layer of a machine-learning model trained to predict the future state of a specific time-series variable. To verify the approach in a practical context, we conducted an experiment with 16 pairs of expert baseball pitchers and batters; input datasets with 27 joint resultant velocity data (joints of 13 pitchers and 14 batters) were generated and used for model training.Results: NGC analysis revealed significant causal relations among intra- and inter-individual body components such as the batter's hands having a causal effect from the pitcher's throwing arm. Remarkably, although the causality from the batter's body to pitcher's body is much lower than the reverse, it is significantly correlated with batter performance outcomes. Conclusions: The above results suggest the effectiveness of NGC analysis for understanding whole-body inter-personal coordination dynamics and that of the AI technique as a new approach for analyzing complex human behavior from a different perspective than conventional techniques.