Big data event analytics in football for tactical decision support - Kröckel, P. (Friedrich-Alexander-Universität Erlangen-Nürnberg, Rechts-und Wirtschaftswissenschaftliche Fakultät Fachbereich Wirtschafts- und Sozialwissenschaften, 2019)
The thesis shows how tactical information in football can be obtained by applying analytics techniques from the fields of network science, machine learning and process mining. It uses professional event tracking data from the European Championship in 2016. The main motivation behind this research is, on the one hand, the lack of studies that use professional tracking data in football, and on the other, the lack of studies investigating real-time decision support in football based on data analytics. Therefore, the thesis aims at demonstrating how event tracking data can support football coaches and their staff to make decisions pre-and post-match, as well as during live games. As a theoretical basis for the analytics concept followed in this thesis, the dynamic system theory is used. According to this theory football teams are dynamic systems, composed of elements (the players) who interact constantly with each other and their environment and who, by their dynamic interactions, form behavioral patterns over time. These patterns are due to the self-organization ability of the players, and thanks to this, they are able to reorganize themselves and regain a state of balance following a perturbation occurrence (e.g., a counter attack). By following the principles of this theory, analytics techniques such associal network analysis, self-organizing maps, and process mining are applied on football event data. Social network analysis answers questions related to the relevance of a player, the structure of a team, as well as sudden changes occurring in the teamrelated to different metrics of interest. Self-organizing maps help to transform highly dimensional data about what happened in the game into more understandable two dimensional maps. Process mining analyzes sequence data of a football team and can be used to gain a quick idea about the team`s behavior, as well as identifying key players. The final chapter demonstrates how some of the results discussed in the thesis can be used for real time decision support. A mockup displays examples of a dashboard and the type of results that a coach can use in order to decide which players should be substituted. The main contributions of the thesis are related to the use of a real world dataset, the methods used for the analysis as well as the discussion of how the results can be used for real-time decision sup-port in football, which has previously not been sufficiently investigated in literature.