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

TL;DR

This paper demonstrates how causal inference methods can be applied to soccer analytics, specifically analyzing crossing's impact on shot creation, highlighting the importance of framing questions correctly for accurate insights.

Contribution

It provides a case study illustrating the use of causal inference techniques in sports analytics, emphasizing the distinction between ATE and ATT in evaluating crossing effects.

Findings

Crossing increases shot probability by 1.6% overall (ATE).

Crossing increases shot probability by 5.0% when actually attempted (ATT).

Different matching strategies are used for ATE and ATT estimation.

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.