This article uses matched wake analysis to examine how disparate police response to protestor partisanship evolves in France, Germany, and the United Kingdom. Drawing on the Armed Conflict Location and Event Dataset (ACLED), a comprehensive observational protest database with detailed event descriptions and political actor identification, I develop a novel multinational dataset of direct action group policy positions. Utilizing a supervised machine learning technique, I classify protesters as "left", "center left", "unknown", "center right", and "right", enabling a nuanced analysis of ideological variations. Algorithmically identifying sequences of ideologically similar protests within close temporal and geographic proximity, I create matched protest pairs to isolate how police response to partisanship changes over time. This matched wake analysis spans a six-year period between 2018 and 2024, examining both immediate aftermath effects (1-7 days) and long-term patterns up to 30 days following significant events (Schutte and Donnay, 2014). The research critically assesses whether there is an increasing police presence and/or escalatory police response associated with protestor partisanship across these three countries. Drawing on theoretical insights about institutional path dependencies, the study explores how initial instances of over-policing might create systematic intensifications or diminutions of police response to specific political movements over time (Pierson, 2000; Immergut, 2006; Legewie, 2016).