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.

翻译：在足球比赛中，进攻方试图突破对手的防线以创造得分机会。这一被称为“越线”的行为是衡量进攻效率和战术表现的关键指标，然而先前的研究主要关注射门或得分机会，而非球队如何突破防线。本研究利用2023年J1联赛赛季的事件数据与追踪数据，开发了一种机器学习模型来预测越线事件。该模型整合了189个特征，包括球员位置、速度及空间配置，并采用XGBoost分类器来估计越线发生的概率。所提出的模型取得了较高的预测准确度，其AUC值为0.982，Brier分数为0.015。此外，SHAP分析表明，进攻球员速度、防线缺口以及进攻球员的空间分布等因素对越线的发生具有显著影响。最后，我们发现球队层面被预测的“被越线”概率与所承受的射门次数和传中次数呈中等程度的正相关。这些结果表明，越线与得分机会的创造密切相关，并为理解足球战术动态提供了量化分析框架。