Modern technologies in image and video processing enable the collection of fine granular data in professional football (soccer). These data provide a digital reproduction of every match consisting of positional (i.e., time-dependent x/y-coordinates from all 22 players and the ball) and event data (e.g., a successful pass from player A to player B at a certain time point). In collaboration with the DFB-Akademie, Deutsche Fußball-Liga (DFL) and Sportec Solutions AG this project combines practitioners‘ expertise with research methodologies from sports and data-science to address three objectives, i.e. the synchronization of the two data sources, the quantification of offensive performance, and the automated detection of tactical patterns.
Based on the synchronization of positional and event data this project evaluates the application of Machine Learning (ML) techniques to objectively quantify and analyze tactical behaviors in football. We demonstrate that a large scale analysis of offensive actions, tactics, and strategies lead to a more accurate quantification of offensive performance than is possible with existing statistics (Anzer, 2022). Moreover, we develop and evaluate ML-based match analysis processes that detect tactical patterns (e.g., counterpressings, counterattacks) automatically and thus provide a basis for more time efficient processes in match analysis (Bauer, 2022).
