用机器学习预测足球进攻突破防线，准确率超98%
Analysis of Line Break prediction models for detecting defensive breakthrough in football
- 基于189个位置速度特征，用XGBoost建模预测突破
- 模型AUC达0.982，Brier得分仅0.015，精度极高
- 揭示速度、防守空档和阵型分布是关键因素
在足球中，进攻方试图突破对方防守线以创造得分机会，这一行为称为‘突破’（Line Break），是进攻效率与战术表现的重要指标。以往研究多关注射门或进球机会，而忽视了突破本身。本研究利用2023年J1联赛的比赛事件与追踪数据，构建机器学习模型预测突破事件。模型包含189个特征，涵盖球员位置、速度及空间布局，并采用XGBoost分类器估算突破概率。模型表现出极高的预测性能，AUC达0.982，Brier得分为0.015。SHAP分析表明，进攻球员速度、防守线空档大小及进攻方空间分布是影响突破的关键因素。此外，团队层面的突破预测概率与失球射门数和传中数呈中等正相关。结果表明，突破与得分机会密切相关，为理解足球战术动态提供了量化框架。
原文摘要 · Abstract (English)
In football, attacking teams attempt to break through the opponent's defensive line to create scoring opportunities. This action, known as a Line Break, is a critical indicator of offensive effectiveness and tactical performance, yet previous studies have mainly focused on shots or goal opportunities rather than on how teams break the defensive line. In this study, we develop a machine learning model to predict Line Breaks using event and tracking data from the 2023 J1 League season. The model incorporates 189 features, including player positions, velocities, and spatial configurations, and employs an XGBoost classifier to estimate the probability of Line Breaks. The proposed model achieved high predictive accuracy, with an AUC of 0.982 and a Brier score of 0.015. Furthermore, SHAP analysis revealed that factors such as offensive player speed, gaps in the defensive line, and offensive players' spatial distributions significantly contribute to the occurrence of Line Breaks. Finally, we found a moderate positive correlation between the predicted probability of being Line-Broken and the number of shots and crosses conceded at the team level. These results suggest that Line Breaks are closely linked to the creation of scoring opportunities and provide a quantitative framework for understanding tactical dynamics in football.
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