An analysis of player-formation graphs for predicting football passesStropnik, Vid (Avtor) Šubelj, Lovro (Mentor) association footballsoccergraph neural networkgraph datasetIn modern association football, data and the knowledge derived from it play a crucial role in forming tactical plans and analyzing games. In this Master's thesis, we explore the capability of modeling a passer's decision-making when selecting a target during a football match, using graphs and graph neural networks. We present a methodology for constructing a dataset of graphs from open data, where nodes represent players on the field and edges represent interactions between them. We investigate three different graph configurations and analyze the utility of advanced features, such as player movement trajectories and pitch control models. Our final dataset contains 80,332 graph representations from 166 matches. We evaluate the usefulness of the dataset on three tasks: regression of the coordinates of a successful pass, classification of the role of the pass recipient, and prediction of the target zone of the pass, based on the positional play pitch division. Our models, based on the Graph Transformer architecture, achieve the best results in all tasks. The results demonstrate that our approach effectively models the passer's decision-making and that graph models enriched with appropriate features can significantly contribute to advanced football analytics.20242024-11-15 15:20:00Magistrsko delo/naloga164917VisID: 37127COBISS_ID: 216990723sl