Interpretable Machine Learning for 100-Yard Freestyle Performance: SHAP-Driven Feature Selection of Lap-Level Stroke, Splits, and Breakout Metrics

Ziqiu Wang

doi:10.54254/2753-8818/2026.CH29709

Article Information

Received: 13 November 2025

Published: 19 November 2025

DOI: https://doi.org/10.54254/2753-8818/2026.CH29709

Article Type: Research Article

Cite this article

Wang,Z. (2025). Interpretable Machine Learning for 100-Yard Freestyle Performance: SHAP-Driven Feature Selection of Lap-Level Stroke, Splits, and Breakout Metrics. Theoretical and Natural Science,151,1-11.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title: TNS Vol.151

Part of Series: Theoretical and Natural Science

ISSN: 2753-8818 (Print) / 2753-8826 (Online)

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