Department of Exercise and Medical Science, Graduate School, Dankook University, Cheonan, Republic of Korea.
Institute of Medical-Sports, Dankook University, Cheonan, Republic of Korea.
PLoS One. 2024 Apr 2;19(4):e0298870. doi: 10.1371/journal.pone.0298870. eCollection 2024.
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.
体能（PF）包括多种对运动表现有重大影响的因素。分析体能对于根据运动员所参加的运动项目为他们制定定制化的训练方法至关重要。以前分析体能的方法依赖于统计或机器学习算法，这些算法侧重于预测运动员的受伤或表现。在这项研究中，我们使用了六种机器学习算法来分析来自五个运动项目（田径、足球、棒球、游泳和羽毛球）的 1489 名男性青少年运动员的体能。此外，我们还使用了机器学习模型来使用 XGBoost 的特征重要性和 SHAP 值来分析体能的基本要素。结果表明，XGBoost 表现最佳，平均准确率为 90.14%，曲线下面积为 0.86，F1 得分为 0.87，表明各运动项目之间存在相似性。XGBoost 的特征重要性和 SHAP 值通过比较五个运动项目中每个运动项目内的两项运动，为评估运动项目中体能的相对重要性提供了定量评估。这项分析有望有助于分析不同运动项目中运动员的基本体能要素，并相应地推荐个性化的运动方法。
