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

arXiv: https://arxiv.org/abs/2506.06666 · HTML: https://mlsanity.com/paper/2506.06666/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)](https://mlsanity.com/paper/2301.13052/) (2023)
- [QPass: a Merit-based Evaluation of Soccer Passes](https://mlsanity.com/paper/1608.03532/) (2016)
- [Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approach](https://mlsanity.com/paper/2311.13707/) (2023)

## Built on by (most cited first, 2 of 2)

- [Structural Pass Analysis in Football: Learning Pass Archetypes and Tactical Impact from Spatio-Temporal Tracking Data](https://mlsanity.com/paper/2603.28916/) (2026)
- [Graph-to-Grid (G2G): Continuous-Coordinate Feature Painting for Soccer Pass Surfaces](https://mlsanity.com/paper/2609.24040/) (2026)

## Abstract (arXiv)

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, LBPCh1 and LBPCh2, 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.