Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution
This work addresses performance prediction for sports analytics and betting industries, but it appears incremental as it combines existing graph and temporal techniques.
The study tackled predicting player performance in sports by introducing GATv2-GCN, a deep learning method that models dynamic player interactions and temporal statistics, and demonstrated its effectiveness using real-world data.
This study presents a novel deep learning method, called GATv2-GCN, for predicting player performance in sports. To construct a dynamic player interaction graph, we leverage player statistics and their interactions during gameplay. We use a graph attention network to capture the attention that each player pays to each other, allowing for more accurate modeling of the dynamic player interactions. To handle the multivariate player statistics time series, we incorporate a temporal convolution layer, which provides the model with temporal predictive power. We evaluate the performance of our model using real-world sports data, demonstrating its effectiveness in predicting player performance. Furthermore, we explore the potential use of our model in a sports betting context, providing insights into profitable strategies that leverage our predictive power. The proposed method has the potential to advance the state-of-the-art in player performance prediction and to provide valuable insights for sports analytics and betting industries.