Abstract
Evaluating the quality of shots in basketball is crucial and requires considering the context in which they are taken. We introduce a graph neural network to process a graph based on player and ball tracking data to compute expected shot quality. We evaluate this model against other models focusing on calibration. The messages between spatial and temporal features are separated, and an attention mechanism is implemented, making the graph neural network interpretable. We use the GNNExplainer to further show the importance of node features. To demonstrate possible practical applications, we analyse the embeddings of the graph neural network concerning different situations like the mean of all player predictions or similarity between created shots and compare this to existing methods.