Player movement metrics in football such as speed and distance are typically analysed as aggregates, sometimes outside of any specific tactical or match context. This research adds context to a player's movement over the course of a match by analysing movement profiles s and bringing together tools from the sport science and sports analytics literature. Position-specific distributions of player movement metrics: speed, acceleration and tortuosity were compared across phases of play and in-game win probability using 25 Hz optical player tracking data from all 52 matches at the 2019 FIFA Women's World Cup. Comparing the distributions using the Kolmogorov-Smirnov test and Wasserstein distances, differences were identified in these movement profiles across, in and out of possession phases, with small negligible overall positional trends across in-game win probabilities. In-game win probabilities are used in tandem with phases to present a player specific case study. The results demonstrate how sports analytics metrics can be used to contextualise a subset of movement metrics from sport science and provide a framework for analysis of further movement metrics and sports analytics modelling approaches.
Keywords: Sports analytics; football; performance analysis; sport science; tactics.