Analysis of Line Break prediction models for detecting defensive breakthrough in football

2025-10-31

AI Summary

This study addresses the gap in football analytics where existing research overemphasizes shots and goals while neglecting the underlying mechanisms of defensive line penetration. We formally define and model “Line Break” events—the dynamic offensive process of penetrating the defensive structure to generate scoring opportunities. Methodologically, we integrate multi-source player-tracking and event data to construct an XGBoost classifier with 189 spatial, kinematic, and tactical features, augmented by SHAP-based interpretability analysis. The model achieves outstanding performance (AUC = 0.982; Brier Score = 0.015), and team-level Line Break probabilities correlate moderately with actual conceded goals (r = 0.47). Our key contributions are: (1) introducing the first quantifiable, event-based Line Break metric; (2) establishing a novel analytical paradigm for tactical assessment; and (3) empirically validating its sensitivity to defensive vulnerability and predictive validity for goal-conceding outcomes.

Technology Category

• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Predicting defensive line breaks in football using machine learning
• Analyzing player positions and velocities to identify breakthrough factors
• Quantifying the relationship between line breaks and scoring opportunities

Innovation

• Uses XGBoost classifier for Line Break prediction
• Incorporates player positions and velocities features
• Applies SHAP analysis to identify key factors