使用梯度增强决策树构建预测模型并分析世界女排的关键胜利因素
1Tianjin University of Sport, Tianjin, China. 202120300009@stu.tjus.edu.cn.
Scientific reports
|November 27, 2025
概括
赢得女排比赛的关键因素包括尖端得分率和阻塞高度. 机器学习模型可以使用这些关键性能指标准确预测比赛胜利率.
科学领域:
- 运动科学 运动科学 运动科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 分析影响比赛结果的因素对于精英体育的表现改善至关重要.
- 之前的研究已经探讨了各种指标,但对于女排,需要综合统计和机器学习方法的综合分析.
研究的目的:
- 确定在国际女子排球比赛中对胜利作出重大贡献的关键绩效指标 (KPIs).
- 开发和验证一个机器学习模型,以根据已识别的KPI来预测比赛胜率.
主要方法:
- 灰色关系分析 (GRA) 用于确定27个比赛指标的贡献,以确定2020年奥运会周期的142场比赛的胜利率.
- 渐变增强决策树 (GBDT) 算法被用来构建一个预测模型,使用选择的有影响力的指标来预测比赛胜利率.
主要成果:
- 峰值得分率和阻塞高度被确定为影响比赛胜利的最决定性因素,特征重要性值分别为0.45和0.3.
- 其他重要的指标包括出色的防守率,服务得分率,阻断得分率,以及服务和阻断得分的比例.
- GBDT模型在测试组上取得了强大的预测性能,MSE=0.002,MAE=0.0322,R2=0.8497,在验证过程中平均相对误差为5.30%.
结论:
- 该研究成功地确定了女性排球成功的关键因素,强调了尖端得分和阻塞.
- 综合的GRA和GBDT方法为分析和预测体育比赛结果提供了一种新且有效的方法.
- 调查结果为教练提供了可操作的,基于数据的见解,以优化训练策略和游戏中的决策.
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