在使用PLS-PM的球员足球表现分析中观察到异质性
1Department of Economics and Management, University of Brescia, Brescia, Italy.
Journal of applied statistics
|November 16, 2023
概括
这项研究分析了2018/2019赛季的足球运动员表现数据,使用了部分最小平方路径模型 (PLS-PM). 结果显示,绩效指标根据球员角色有很大差异,为足球战略提供数据驱动的见解.
科学领域:
- 运动分析 运动分析
- 足球中的数据科学
- 性能测量 性能测量 性能测量 性能测量 性能测量
背景情况:
- 数据科学应用正在扩展到日常生活中,包括体育分析.
- 足球 (足球) 策略在很大程度上依赖于球探,技术人员和管理层的明智决策.
- 监测球员的表现对于职业足球的战略选择至关重要.
研究的目的:
- 分析欧洲前五大联赛2018/2019赛季的足球运动员表现数据.
- 开发一个综合性绩效指标,使用sofifa的关键绩效指数,根据参与者角色进行区分.
- 为了科学验证玩家异质性 (角色和联盟) 在绩效分析中的重要性.
主要方法:
- 利用了来自电子艺术 (EA) 和Kaggle数据科学平台的玩家性能数据.
- 采用了第三阶部分最小正方形路径模型 (PLS-PM) 方法,并得到了足球专家的意见.
- 计算了一个基于sofifa关键绩效指数的复合指标,计算了球员角色和联盟.
主要成果:
- 该研究使用PLS-PM计算了一种复合绩效指标,根据玩家角色进行区分.
- 将衍生的复合指标与EA Sports提供的整体指标进行了比较.
- 结果证实,根据玩家的角色,某些性能子区域具有不同的重要权重.
结论:
- 球员角色显著影响了足球中不同表现指标的重要性.
- PLS-PM方法为评估足球运动员表现提供了一种科学验证的方法.
- 调查结果为足球俱乐部的数据驱动战略决策提供了宝贵的见解.
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