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

TL;DR

This paper introduces GATv2-GCN, a deep learning model combining graph attention networks and temporal convolution to accurately predict sports player performance by modeling dynamic interactions and temporal data.

Contribution

It presents a novel deep learning approach that integrates graph attention and temporal convolution for improved sports performance prediction.

Findings

Effective in predicting player performance using real-world data

Outperforms existing models in accuracy

Provides insights for sports betting strategies

Abstract

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.