Given the advances in camera-based tracking systems, many soccer teams are able to record data about the players’ position during a game. Analysing these data is challenging, since they are fine-grained, contain implicit relational information between players, and contain the dynamics of the game. We propose the use of qualitative spatial reasoning techniques to address these challenges, and test our approach by learning a model for pass prediction over a real-world soccer dataset. Experimental evaluation shows that our approach is capable of learning meaningful models. Since we employ an inductive logic programming system to learn the model, it has the added benefit of producing interpretable rules.