Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data

Sports teams are nowadays collecting huge amounts of data from training sessions and matches. The teams are becoming increasingly interested in exploiting these data to gain a competitive advantage over their competitors. One of the most prevalent types of new data is event stream data from matches. These data enable more advanced descriptive analysis as well as the potential to investigate an opponent's tactics in greater depth. Due to the complexity of both the data and game strategy, most tactical analyses are currently performed by humans reviewing video and scouting matches in person. As a result, this is a time-consuming and tedious process. This paper explores the problem of automatic tactics detection from event-stream data collected from professional soccer matches. We highlight several important challenges that these data and this problem setting pose. We describe a data-driven approach for identifying patterns of movement that account for both spatial and temporal information which represent potential offensive tactics. We evaluate our approach on the 2015/2016 season of the English Premier League and are able to identify interesting strategies per team related to goal kicks, corners and set pieces.