Using Machine Learning to predict Tactics from spatiotemporal Data in Football

The goal of my master's thesis was to investigate, to what extent machine learning models were capable of automating tactical analysis in football to benefit coaches and decision makers. Spatiotemporal data (players' and ball's xyz-positions) from 15 professional first-division Bundesliga matches were obtained and manually annotated regarding the respective tactical maneuvers that were employed by the teams. Using this feature-engineered dataset and supervised learning techniques, machine learning models were trained to accurately predict these tactical elements.