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.

翻译：本研究探讨了足球教练面临的核心战术困境：是采用俗称"摆大巴"的防守策略，还是选择更具攻击性的战术。通过运用先进的双重机器学习框架，本项目开发了一个稳健且可解释的工具，用于评估不同阵型对关键比赛结果（如净胜球、控球率、角球数和纪律处罚）的因果影响。基于欧洲顶级联赛超过22,000场比赛的数据集，我们根据战术结构和专家咨询将阵型分为六种代表性类型。本方法的主要贡献在于通过创新的矩阵化残差处理技术，将DML框架适配于分类处理变量（特别是阵型组合），从而实现对阵型间对抗效应的精细化估计，为教练的战术决策提供依据。研究结果表明：虽然4-3-3和4-2-3-1等进攻阵型在控球率和角球数上具有适度的统计优势，但对进球数的影响有限。此外，没有证据支持与摆大巴战术相关的防守阵型能提高球队的获胜概率。另外，红牌数量似乎不受阵型选择的影响，这表明其他行为因素起主导作用。尽管该方法未能完全捕捉比赛风格或球队实力的所有方面，但它为教练分析战术效率提供了有价值的框架，并为体育分析领域的未来研究树立了先例。