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

TL;DR

This paper develops a machine learning model to predict Line Breaks in football using event and tracking data, achieving high accuracy and revealing key factors influencing offensive breakthroughs.

Contribution

It introduces a novel predictive model for Line Breaks in football, incorporating extensive features and providing insights into tactical dynamics.

Findings

Model achieved AUC of 0.982 and Brier score of 0.015

SHAP analysis identified key factors like player speed and defensive gaps

Moderate correlation between predicted Line Breaks and shots/crosses

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.