基于梯度回归模型的运动员表现预测
Xiaolei Wei1, Shuang Liang2, Wei Diao1
1Physical Education Teaching and Research Department, Changchun University of Technology, Changchun, Jilin, 130012, China.
Scientific reports
|February 18, 2026
概括
与传统方法相比,一种新的梯度回归模型显著提高了运动员表现预测的准确性 (R2=0.923). 这种数据驱动的方法增强了体育科学对培训和人才管理的洞察力.
科学领域:
- 体育科学与分析 运动科学与分析
- 运动中的机器学习
背景情况:
- 传统的统计模型在运动员生理,生活方式和上下文数据中的复杂非线性问题上扎.
- 准确的运动员表现预测对于训练设计,伤害预防和人才管理至关重要.
研究的目的:
- 使用表格数据预测运动员表现得分,专注于预测有效性和概括性.
- 通过基于SHAP的解释来提高可解释性,并确保实际部署的计算效率.
- 提供一个数据驱动的平台,用于识别关键的绩效决定因素,并为体育教练决策提供信息.
主要方法:
- 利用Kaggle运动员绩效预测数据集,包括人口统计,训练,生理和生活方式特征.
- 应用数据预处理技术,包括归算,规范化,编码和特征工程.
- 训练了一种梯度回归模型,经过十倍交叉验证,将其性能与线性回归,回归,支向量回归,随机森林和神经网络进行比较.
主要成果:
- 梯度回归模型实现了0.923的优异R2,超过了神经网络 (R2=0.901) 和随机森林 (R2=0.887).
- 剩余和错误分析证实了最小的偏差和差异,而学习动态显示了高效的融合和稳定性.
- 该模型被证明比基线模型更加准确和可解释.
结论:
- 梯度回归模型为运动员表现预测提供了强大的和可解释的解决方案.
- 结果支持在个性化培训计划和持续绩效监测中的应用.
- 未来的研究应该探索更大的纵向研究和结合生物力学和心理数据的混合框架.
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