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

Genís Ruiz-Menárguez, Llorenç Badiella

TL;DR

Results show that while offensive formations like 4-3-3 and 4-2-3-1 offer modest statistical advantages in possession and corners, their impact on goals is limited and no evidence supports the idea that defensive formations, commonly associated with parking the bus, increase a team's winning potential.

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 advantages in possession and corners, their impact on goals is limited. Furthermore, no evidence supports the idea that defensive formations, commonly associated with parking the bus, increase a team's winning potential. Additionally, red cards appear unaffected by formation choice, suggesting other behavioral factors dominate. Although this approach does not fully capture all aspects of playing style or team strength, it provides a valuable framework for coaches to analyze tactical efficiency and sets a precedent for future research in sports analytics.

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

TL;DR

Results show that while offensive formations like 4-3-3 and 4-2-3-1 offer modest statistical advantages in possession and corners, their impact on goals is limited and no evidence supports the idea that defensive formations, commonly associated with parking the bus, increase a team's winning potential.

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 advantages in possession and corners, their impact on goals is limited. Furthermore, no evidence supports the idea that defensive formations, commonly associated with parking the bus, increase a team's winning potential. Additionally, red cards appear unaffected by formation choice, suggesting other behavioral factors dominate. Although this approach does not fully capture all aspects of playing style or team strength, it provides a valuable framework for coaches to analyze tactical efficiency and sets a precedent for future research in sports analytics.
Paper Structure (21 sections, 6 equations, 5 figures, 3 tables)

This paper contains 21 sections, 6 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Data Processing
  3. Confounding Variables
  4. Methodology
  5. Double Machine Learning
  6. New Approach for DML with categorical treatment
  7. Categorical Treatment Properties
  8. Project Methodology
  9. Formation Analysis Results
  10. Formation Treatment
  11. Model Performance
  12. Formation Matrix
  13. Target Variable 1: Goals Difference
  14. Target variable 2: Red Cards difference
  15. Target Variable 3: Yellow Cards Difference
  16. ...and 6 more sections

Figures (5)

  • Figure 1: $\hat{\beta}_{i,j}$ coefficients and p-values for goals difference
  • Figure 2: $\hat{\beta}_{i,j}$ coefficients and p-values for red cards difference
  • Figure 3: $\hat{\beta}_{i,j}$ coefficients and p-values for yellow cards difference
  • Figure 4: $\hat{\beta}_{i,j}$ coefficients and p-values for possession difference
  • Figure 5: $\hat{\beta}_{i,j}$ coefficients and p-values for corners difference