在足球协会中最大限度地提高球运动不可预测性:基于雷尼的方法来优化事件分布随机性的优化
Ishara Bandara1,2, Sergiy Shelyag1,3, Sutharshan Rajasegarar1
1School of IT, Deakin University, Melbourne, Victoria, Australia.
PloS one
|February 25, 2026
概括
在整个足球场上最大限度地提高不可预测性,使用Max (α→∞),与获胜相关. 这种方法同样考虑所有地区,与传统方法相比,大大改善了对决胜利者的预测.
科学领域:
- 运动分析 运动分析
- 复杂的系统复杂的系统.
- 统计建模 统计建模
背景情况:
- 现代足球强调团队战术,但低得分挑战了绩效评估.
- 不能预测的球运动是关键,但其空间分布对成功的影响还没有得到充分研究.
- 诸如空间事件分布随机性 (EDRan) 这样的现有指标并没有区分主导和全面的现场覆盖.
研究的目的:
- 调查强调占主导地位的地区或考虑所有现场区域是否更有效地计算事件分布随机性.
- 分析事件分布随机性的模式,比较获胜和输球队.
- 评估不同基于的指标对匹配结果的预测能力.
主要方法:
- 分析事件分布随机性使用Rényi与变化的α值 (α).
- 相关性分析将度指标与比赛中获胜的表现联系起来.
- 机器学习模型训练了不同的度 (最大度[α→∞],α=0.5,香农度[α=1]) 来预测比赛获胜者.
主要成果:
- 最大度 (α→∞),对所有领域区域均等加权,显示出与获胜绩效的最强相关性.
- 使用Max和α=0.5的机器学习模型在预测赢家方面显著超过了香农 (α=1).
- 最好的模型,利用马克斯,在预测比赛结果方面实现了80.61%的准确性,超过了现有的文献基准.
结论:
- 在整个球场上传播的不可预测性,利用不同的区域,比仅限于主导区域的随机性更能说明足球成功.
- 雷尼,特别是马克斯,比香农提供了与性能相关的更强大的战术不可预测性的衡量标准.
- 这项研究为分析足球比赛动态和预测结果提供了一种新的,高度准确的方法.
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