Measuring football fever through wearable technology: A case study on the German cup final

Abstract

Football is the world's most popular sport, evoking strong physiological and emotional responses among its fans. Yet, the specific dynamics of fan attachment to matches have received little attention in the literature. In this paper, we quantify these dynamics through a unique case study from professional football: the 2025 cup final of the German Football Association (DFB) between first-division club VfB Stuttgart and third-division club Arminia Bielefeld. We collected high-resolution smartwatch data, including heart rate and stress level, from 229 Arminia Bielefeld fans over approximately 12 weeks, complemented by survey responses on club attachment, match attendance, and personal characteristics from a subset of 37 participants. By combining physiological data with survey information, we analyse variations in emotional engagement across individuals and contexts, as well as physiological reactions to key match events. This approach provides rare, data-driven insights into the football fever that captivates fans during high-stakes competitions. Furthermore, we compare the vital parameters recorded on the day of the match with baseline levels on non-matchdays throughout the entire observation period. Our findings reveal pronounced physiological responses among fans, beginning hours before the match and peaking at kick-off.

Figures (12)

• Figure 1: Boxplot of the average stress level (left) and average heart rate (right) per individual participant across time points for the day of the cup final (May 24, 2025; orange) and regular days within the observation period (May 19 to July 31, 2025; blue).
• Figure 2: Average stress level across participants over the course of the cup final matchday (May 24, 2025; orange) compared with the average stress level across participants and across all regular Saturdays within the observation period (May 19 to July 31, 2025; blue). The shaded areas represent the 10th- and 90th-percentiles of averages across participants for regular Saturdays. For the graphical representation, we smooth averages and percentiles to reduce volatility and increase interpretability.
• Figure 3: Average heart rate across participants over the course of the match (black solid line; left y-axis) and objective uncertainty of the match outcome derived from betting odds measured by $1-\text{Gini}$ (grey dotted line; right y-axis) during the cup final. For the graphical representation, we smooth the average heart rate to reduce volatility and increase interpretability. The grey shaded area marks the halftime break. Goals are indicated by vertical lines (red: VfB Stuttgart; blue: Arminia Bielefeld), together with the resulting scoreline.
• Figure 4: Average heart rate across survey participants with the same viewing context over the course of the match (blue: stadium; orange: public gathering; pink: TV). The shaded areas represent the heart rates' 10th- and 90th-percentiles. For the graphical representation, we smooth averages and percentiles to reduce volatility and increase interpretability. The grey shaded area marks the halftime break. Goals are indicated by vertical lines (red: VfB Stuttgart; blue: Arminia Bielefeld), together with the resulting scoreline.
• Figure 5: Average heart rates across survey participants attending the final in the stadium with the same arrival to Berlin (orange: Friday (or earlier); blue: Saturday) over the 24 hours prior to kick-off. The shaded areas represent the heart rates' 10th- and 90th-percentiles. For the graphical representation, we smooth averages and percentiles to reduce volatility and increase interpretability.