基于CatBoost回归和随机森林算法进行的网球比赛的势头预测模型
Xingchen Lv1, Dingyu Gu1, Xianghu Liu2
1Department of Arts and Sciences, Suqian University, Jiangsu, 223800, Suqian, People's Republic of China.
Scientific reports
|August 13, 2024
概括
这项研究使用2023年温布尔登数据分析了网球势头. 调查结果证实了势头的发展势头.
科学领域:
- 运动科学 运动科学 运动科学
- 数据分析数据分析数据分析.
- 网球表现 网球表现
背景情况:
- 动量是包括网球在内的球游戏中的关键因素.
- 了解势头可以在网球比赛中提供战略优势.
- 之前的研究还没有完全量化出势头对职业网球成功的影响.
研究的目的:
- 调查职业网球比赛中势头的作用和影响.
- 开发基于势头的网球比赛结果的预测模型.
- 为网球运动员提出数据驱动的获胜策略.
主要方法:
- 在重新处理的2023年温布尔登决赛数据上训练了一个决策树回归模型.
- 使用CatBoost和随机森林回归建立了一个联合回归森林 (CRF) 模型.
- 分析了自相关系数来量化动量与成功之间的关系.
主要成果:
- 显著的非零自相关系数证实了动量和比赛成功之间的相关性.
- 预测分析确定了影响势头转变的关键因素.
- 开发的模型证明了在不同匹配类型和表面的有效泛化.
结论:
- 势头是职业网球成功的统计学显著预测因素.
- 开发的CRF模型为预测比赛结果提供了可靠的工具.
- 基于势头分析的数据驱动策略可以提高玩家的表现.
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