用改进的xG模型预测进球概率,使用足球协会的事件序列
Ishara Bandara1,2, Sergiy Shelyag1,3, Sutharshan Rajasegarar1
1School of IT, Deakin University, Melbourne, Australia.
PloS one
|October 30, 2024
概括
预测足球投篮通过分析先前事件的新框架得到了改进. 这提高了预期目标 (xG) 的准确性,提供了更好的绩效评估和战略设计.
科学领域:
- 运动分析 运动分析
- 足球表现指标 足球表现指标
背景情况:
- 在协会足球中预测投篮结果对于绩效分析和战略至关重要.
- 现有的预期目标 (xG) 模型提供了有价值的见解,但可以改进.
研究的目的:
- 提出一个新的框架,以提高预期目标 (xG) 度量的准确性.
- 将先前事件的时间特征纳入xG建模.
主要方法:
- 利用一个随机森林模型,结合先前探索的和新的时间特征.
- 引入了新的功能,如"进步因素"和"玩家位置列".
主要成果:
- 与基于单个事件的模型相比,拟议的框架显示出更高的性能.
- 通过包括先前事件信息,可以显著提高模型准确性.
结论:
- 新的框架通过考虑事件的顺序来提高xG预测的准确性.
- 确定了关键事件序列,例如从18码的盒子侧面的积累和传球到远端的帖子,以改善xG.
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