Time-Varying Home Field Advantage in Football: Learning from a Non-Stationary Causal Process

TL;DR

This paper introduces DYNAMO, a novel causal discovery method designed to learn and analyze the non-stationary, time-varying causal structures behind home field advantage in football, revealing insights like referee bias and crowd influence.

Contribution

The paper proposes DYNAMO, a flexible, theoretically grounded method for learning linear and non-linear causal structures in non-stationary environments, applied to sports analytics.

Findings

DYNAMO accurately recovers time-varying causal structures in simulations.

Application to EPL data reveals referee bias and crowd effects on home advantage.

Time-varying causal models improve goal prediction accuracy.

Abstract

In sports analytics, home field advantage is a robust phenomenon where the home team wins more games than the away team. However, discovering the causal factors behind home field advantage presents unique challenges due to the non-stationary, time-varying environment of sports matches. In response, we propose a novel causal discovery method, DYnamic Non-stAtionary local M-estimatOrs (DYNAMO), to learn the time-varying causal structures of home field advantage. DYNAMO offers flexibility by integrating various loss functions, making it practical for learning linear and non-linear causal structures from a general class of non-stationary causal processes. By leveraging local information, we provide theoretical guarantees for the identifiability and estimation consistency of non-stationary causal structures without imposing additional assumptions. Simulation studies validate the efficacy of DYNAMO in recovering time-varying causal structures. We apply our method to high-resolution event data from the 2020-2021 and 2021-2022 English Premier League seasons, during which the former season had no audience presence. Our results reveal intriguing, time-varying, team-specific field advantages influenced by referee bias, which differ significantly with and without crowd support. Furthermore, the time-varying causal structures learned by our method improve goal prediction accuracy compared to existing methods.