Through the Gaps: Uncovering Tactical Line-Breaking Passes with Clustering

TL;DR

This paper introduces an unsupervised clustering framework to detect and analyze tactical line-breaking passes in football, providing new metrics to evaluate their effectiveness in disrupting defenses and creating attacking opportunities.

Contribution

It presents a novel, explainable clustering-based method for identifying LBPs and introduces new tactical metrics for assessing their impact in elite football matches.

Findings

Different teams show distinct styles in vertical progression and disruption.

The metrics effectively differentiate player and team tactical behaviors.

Method is scalable and applicable to performance analysis workflows.

Abstract

Line-breaking passes (LBPs) are crucial tactical actions in football, allowing teams to penetrate defensive lines and access high-value spaces. In this study, we present an unsupervised, clustering-based framework for detecting and analysing LBPs using synchronised event and tracking data from elite matches. Our approach models opponent team shape through vertical spatial segmentation and identifies passes that disrupt defensive lines within open play. Beyond detection, we introduce several tactical metrics, including the space build-up ratio (SBR) and two chain-based variants, LBPCh$^1$ and LBPCh$^2$, which quantify the effectiveness of LBPs in generating immediate or sustained attacking threats. We evaluate these metrics across teams and players in the 2022 FIFA World Cup, revealing stylistic differences in vertical progression and structural disruption. The proposed methodology is explainable, scalable, and directly applicable to modern performance analysis and scouting workflows.