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Essential elements of physical fitness analysis in male adolescent athletes using machine learning

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  • Yun-Hwan Lee
  • Jisuk Chang
  • Ji-Eun Lee
  • Yeon-Sung Jung
  • Dongheon Lee
  • Ho-Seong Lee

Abstract

Physical fitness (PF) includes various factors that significantly impacts athletic performance. Analyzing PF is critical in developing customized training methods for athletes based on the sports in which they compete. Previous approaches to analyzing PF have relied on statistical or machine learning algorithms that focus on predicting athlete injury or performance. In this study, six machine learning algorithms were used to analyze the PF of 1,489 male adolescent athletes across five sports, including track & field, football, baseball, swimming, and badminton. Furthermore, the machine learning models were utilized to analyze the essential elements of PF using feature importance of XGBoost, and SHAP values. As a result, XGBoost represents the highest performance, with an average accuracy of 90.14, an area under the curve of 0.86, and F1-score of 0.87, demonstrating the similarity between the sports. Feature importance of XGBoost, and SHAP value provided a quantitative assessment of the relative importance of PF in sports by comparing two sports within each of the five sports. This analysis is expected to be useful in analyzing the essential PF elements of athletes in various sports and recommending personalized exercise methods accordingly.

Suggested Citation

  • Yun-Hwan Lee & Jisuk Chang & Ji-Eun Lee & Yeon-Sung Jung & Dongheon Lee & Ho-Seong Lee, 2024. "Essential elements of physical fitness analysis in male adolescent athletes using machine learning," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-14, April.
    • Handle: RePEc:plo:pone00:0298870
    DOI: 10.1371/journal.pone.0298870
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    References listed on IDEAS

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    1. Katrijn Opstoel & Johan Pion & Marije Elferink-Gemser & Esther Hartman & Bas Willemse & Renaat Philippaerts & Chris Visscher & Matthieu Lenoir, 2015. "Anthropometric Characteristics, Physical Fitness and Motor Coordination of 9 to 11 Year Old Children Participating in a Wide Range of Sports," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-16, May.
    2. Rabiu Muazu Musa & Anwar P. P. Abdul Majeed & Zahari Taha & Siow Wee Chang & Ahmad Fakhri Ab. Nasir & Mohamad Razali Abdullah, 2019. "A machine learning approach of predicting high potential archers by means of physical fitness indicators," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-12, January.
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