Date of Award

Spring 5-1-2026

Document Type

Thesis

Degree Name

Bachelor of Science (BS)

Department

Economics

First Advisor

Rodrigo Schneider

Abstract

This paper examines whether professional football rivalries in the English Premier League (EPL) influence crime rates among residents living near host stadiums. The analysis focuses on two classic rivalries and their four host clubs: the Merseyside derby (Liverpool and Everton), and the North London derby (Arsenal and Tottenham Hotspur), over the period 2016–2025. Using publicly available Police.uk street-level crime data aggregated to spatial rings by month level, the analysis links crime to stadium proximity and monthly match schedules. The empirical strategy is a Difference-in-Differences (DiD) design that compares changes in crime by spatial rings located close to a stadium (0–1 km, 1-2km) to changes in crime in areas farther away (2–10 km), across three types of months: months with at least one derby home match, months with only non-derby home matches, and months with no home matches. Each spatial ring is assigned to its nearest one of the four stadiums. This framework is intended to isolate whether “rivalry months” generate additional crime risk beyond general home-match activity. The analysis examines total recorded crime, including criminal damage and arson, violence and sexual offences, and anti-social behavior. Because the crime data are reported at the monthly level rather than by match-day and hour, the results capture net monthly effects of hosting rivalry fixtures, not precise short-run spikes around kickoff. Any rivalry effect detected in this setting is therefore likely to be conservative. In future work with more detailed incident-level data, this design could be extended to a high-frequency event-study around individual match. The results show statistically significant increases in crime within 2 km of stadiums during derby months, with no meaningful spillovers beyond this range. The project contributes to a growing literature at the intersection of urban economics, sports behavior, and public safety by providing a transparent empirical framework that can be replicated using publicly available crime data.

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