[2602.16830] The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus?

[2602.16830] The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus?

arXiv - Machine Learning 4 min read Article

Summary

This study explores the impact of football formations on match outcomes using Double Machine Learning, questioning the effectiveness of defensive strategies like 'parking the bus.'

Why It Matters

Understanding the tactical implications of football formations is crucial for coaches and analysts. This research provides a data-driven approach to evaluate formations, helping teams make informed decisions that could enhance their performance in competitive matches.

Key Takeaways

  • Double Machine Learning framework effectively estimates the impact of different football formations.
  • Offensive formations show slight advantages in possession and corners but limited effect on goals.
  • Defensive strategies like 'parking the bus' do not significantly enhance winning potential.
  • Formation choice does not appear to influence the likelihood of receiving red cards.
  • The study sets a precedent for future research in sports analytics and tactical decision-making.

Statistics > Applications arXiv:2602.16830 (stat) [Submitted on 18 Feb 2026] Title:The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus? Authors:Genís Ruiz-Menárguez, Llorenç Badiella View a PDF of the paper titled The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus?, by Gen\'is Ruiz-Men\'arguez and Lloren\c{c} Badiella View PDF HTML (experimental) Abstract:This study addresses a central tactical dilemma for football coaches: whether to employ a defensive strategy, colloquially known as "parking the bus", or a more offensive one. Using an advanced Double Machine Learning (DML) framework, this project provides a robust and interpretable tool to estimate the causal impact of different formations on key match outcomes such as goal difference, possession, corners, and disciplinary actions. Leveraging a dataset of over 22,000 matches from top European leagues, formations were categorized into six representative types based on tactical structure and expert consultation. A major methodological contribution lies in the adaptation of DML to handle categorical treatments, specifically formation combinations, through a novel matrix-based residualization process, allowing for a detailed estimation of formation-versus-formation effects that can inform a coach's tactical decision-making. Results show that while offensive formations like 4-3-3 and 4-2-3-1 offer modest statistical advant...

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