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This study presents a novel deep learning method, called GATv2-GCN, forpredicting player performance in sports. To construct a dynamic playerinteraction graph, we leverage player statistics and their interactions duringgameplay. We use a graph attention network to capture the attention that eachplayer pays to each other, allowing for more accurate modeling of the dynamicplayer interactions. To handle the multivariate player statistics time series,we incorporate a temporal convolution layer, which provides the model withtemporal predictive power. We evaluate the performance of our model usingreal-world sports data, demonstrating its effectiveness in predicting playerperformance. Furthermore, we explore the potential use of our model in a sportsbetting context, providing insights into profitable strategies that leverageour predictive power. The proposed method has the potential to advance thestate-of-the-art in player performance prediction and to provide valuableinsights for sports analytics and betting industries.

Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution