Unlocking the potential of big data to support tactical performance analysis in professional soccer

In professional soccer, increasing amounts of data are collected that harness great potential when it comes to analysing tactical behaviour. Unlocking this potential is difficult as big data challenges the data management and analytics methods commonly employed in sports. By joining forces with computer science, solutions to these challenges could be achieved, helping sports science to find new insights, as is happening in other scientific domains. We aim to bring multiple domains together in the context of analysing tactical behaviour in soccer using position tracking data. A systematic literature search for studies employing position tracking data to study tactical behaviour in soccer was conducted in seven electronic databases, resulting in 2338 identified studies and finally the inclusion of 73 papers. Each domain clearly contributes to the analysis of tactical behaviour, albeit in - sometimes radically - different ways. Accordingly, we present a multidisciplinary framework where each domain's contributions to feature construction, modelling and interpretation can be situated. We discuss a set of key challenges concerning the data analytics process, specifically feature construction, spatial and temporal aggregation. Moreover, we discuss how these challenges could be resolved through multidisciplinary collaboration, which is pivotal in unlocking the potential of position tracking data in sports analytics.Highlights Over the recent years, there has been a considerable growth in studies on tactical behaviour using position tracking data, especially in the domains of sports science and computer science. Yet both domains have contributed distinctly different studies, with the first being more focused on developing theories and practical implications, and the latter more on developing techniques.Considerable opportunities exist for collaboration between sports science and computer science in the study of tactics in soccer, especially when using position tracking data.Collaborations between the domains of sports science and computer science benefit from a stronger dialogue yielding a cyclical collaboration.We have proposed a framework that could serve as the foundation for the combination of sports science and computer science expertise in tactical analysis in soccer.