基于机器学习的女子羽毛球单打预测模型和技术和战术决策分析
Hanguang Yuan1, Yaodong Wang1, Kairan Yang2
1Department of Physical Education, University of Mining and Technology (Beijing), Beijing, China.
PloS one
|November 14, 2024
概括
韩国羽毛球明星安塞- (An Se-young) 成为了韩国的羽毛球明星.
科学领域:
- 运动科学 运动科学 运动科学
- 机器学习 机器学习
- 数据分析数据分析数据分析.
背景情况:
- 安西勇以89.5%的胜率主导了2023年羽毛球世界联合会 (BWF) 奥运排名.
- 她的成功凸显了在女子单打羽毛球中需要深入的技术和战术分析的需要.
研究的目的:
- 为了对安赛勇女单羽毛球队的表现进行技术和战术分析.
- 开发一种基于机器学习的预测模型,用于在女子羽毛球单打比赛中得分和输球.
主要方法:
- 提出了一种新的"三阶段"数据分类方法,用于分析安西的统计数据.
- 对10场对阵BWF前5名对手的比赛 (21分比赛) 的视频分析被用来增强数据集.
- 使用了机器学习算法,包括决策树,随机森林,XGBoost,支持向量机和K-Proximity.
主要成果:
- "三阶段"数据分类在预测比赛结果方面被证明是有效的.
- 使用RBF内核的Support Vector Machine模型实现了87.5%的峰值精度.
- 安塞的比赛风格以持续的侵略性为特征,利用对手的错误进行快速的积分转换.
结论:
- 开发的机器学习模型对羽毛球比赛得分表现出强大的预测一致性.
- 安塞利用对手的错误的战略方法是她成功的关键因素.
- 这些发现为专业羽毛球的表现分析和预测提供了宝贵的见解.
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