Football tracking data: a copula-based hidden Markov model for classification of tactics in football

Abstract

Driven by recent advances in technology, tracking devices allow to collect high-frequency data on the position of players in (association) football matches and in many other sports. Although such data sets are available to every professional team, most teams still rely on time-consuming video analysis when analysing future opponents, for example with regard to how goals were scored or a team’s general style of play. In this contribution, we provide a data-driven approach for automated classification of tactics in football. For that purpose, we consider hidden Markov models (HMMs) to analyse high-frequency tracking data, where the underlying states serve for a team’s tactic. In particular, as space control in football has been considered a major driver of success, we focus on the effective playing space, which is the convex hull created by the players excluding the goalkeeper. This quantity relates to both playing style and team behavior. Using copula-based HMMs, we model jointly the effective playing space of both teams to account for the competitive nature of the game. Our model thus provides an estimate of a team’s playing style at each time point, which can be beneficial for team managers but also of huge interest to football fans.