Framing Causal Questions in Sports Analytics: A Case Study of Crossing in Soccer

2025-05-17

AI Summary

This study investigates the causal effect of crossing on shot creation in football, distinguishing between the average treatment effect (ATE) and the average treatment effect on the treated (ATT). Using high-resolution event data from Shandong Taishan’s 2017 season, we employ a propensity score matching framework to estimate counterfactual outcomes. Results reveal a critical interpretational divergence between ATE and ATT in sports tactical analysis: crossing increases the per-play shot probability by 1.6% on average across all potential crossing opportunities (ATE), but yields a substantially larger 5.0% increase in shot probability specifically within actual crossing instances (ATT), underscoring the strong context dependence of tactical efficacy. Our primary contribution lies in advocating for causal identification goals—rather than mere statistical balance—to guide matching strategy design, thereby advancing rigor and granularity in causal modeling for sports analytics.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

Causal inference has become an accepted analytic framework in settings where experimentation is impossible, which is frequently the case in sports analytics, particularly for studying in-game tactics. However, subtle differences in implementation can lead to important differences in interpretation. In this work, we provide a case study to demonstrate the utility and the nuance of these approaches. Motivated by a case study of crossing in soccer, two causal questions are considered: the overall impact of crossing on shot creation (Average Treatment Effect, ATE) and its impact in plays where crossing was actually attempted (Average Treatment Effect on the Treated, ATT). Using data from Shandong Taishan Luneng Football Club's 2017 season, we demonstrate how distinct matching strategies are used for different estimation targets - the ATE and ATT - though both aim to eliminate any spurious relationship between crossing and shot creation. Results suggest crossing yields a 1.6% additive increase in shot probability overall compared to not crossing (ATE), whereas the ATT is 5.0%. We discuss what insights can be gained from each estimand, and provide examples where one may be preferred over the alternative. Understanding and clearly framing analytics questions through a causal lens ensure rigorous analyses of complex questions.

Problem

• Analyzing causal impact of soccer crossing tactics
• Comparing ATE and ATT in shot creation
• Demonstrating matching strategies for causal inference

Innovation

• Uses causal inference for sports tactics analysis
• Employs distinct matching strategies for ATE and ATT
• Analyzes crossing impact on shot creation in soccer