Time-dependent structural equation modeling of fans' football fever using activity tracking data during the 2025 DFB Cup final

TL;DR

This study models the temporal dynamics of football fans' emotional and physiological responses during a major match using wearable data and a novel time-dependent structural equation approach.

Contribution

It introduces a latent variable model capturing the unfolding of football fever over time, incorporating autoregressive effects and handling high-dimensional data.

Findings

Football fever exhibits a V-shaped trajectory during the match.

Significant heterogeneity exists among fans in baseline and response dynamics.

Wearable data effectively reflect the latent emotional state of fans.

Abstract

Football fans frequently exhibit pronounced emotional and physiological reactions during high-stakes matches. However, the temporal dynamics of this football fever are rarely modeled as a latent process. Using intensive longitudinal data from Arminia Bielefeld supporters who wore smartwatches during the 2025 German Football Association (DFB) Cup final, we investigate how football fever unfolds. The devices recorded heart rate, stress level, and related indicators in short intervals, allowing us to construct a latent variable for football fever and model its dynamics. We specify a time-dependent structural equation model with latent growth components and autoregressive effects to capture both overall trends and short-term carry-over effects in fans' physiological responses. Results are aggregated across multiple imputations of missing measurements. Model fit is evaluated using adjustments for the high data dimensionality. The results show that football fever follows a V-shaped trajectory: high at kick-off, followed by a steady decline until the renewed arousal in the second half, with substantial between-fan heterogeneity in both baseline level and temporal dynamics. Our findings demonstrate that football fever can be adequately represented as a latent variable using structural equation modeling and reflected by wearable technology data. This highlights the importance of accounting for temporal dependence when studying dynamic emotional phenomena, e. g., in sports spectatorship.