Version 1
: Received: 30 September 2024 / Approved: 1 October 2024 / Online: 1 October 2024 (11:15:39 CEST)
How to cite:
Drexler, T. T. Sports Analytics with Graph Neural Networks and Graph Convolutional Networks. Preprints2024, 2024100046. https://doi.org/10.20944/preprints202410.0046.v1
Drexler, T. T. Sports Analytics with Graph Neural Networks and Graph Convolutional Networks. Preprints 2024, 2024100046. https://doi.org/10.20944/preprints202410.0046.v1
Drexler, T. T. Sports Analytics with Graph Neural Networks and Graph Convolutional Networks. Preprints2024, 2024100046. https://doi.org/10.20944/preprints202410.0046.v1
APA Style
Drexler, T. T. (2024). Sports Analytics with Graph Neural Networks and Graph Convolutional Networks. Preprints. https://doi.org/10.20944/preprints202410.0046.v1
Chicago/Turabian Style
Drexler, T. T. 2024 "Sports Analytics with Graph Neural Networks and Graph Convolutional Networks" Preprints. https://doi.org/10.20944/preprints202410.0046.v1
Abstract
Graph neural networks and graph convolutional networks are effective at representing complex relationships. We attempt to apply them to sports analytics by reviewing existing applications in sports. These methodologies leverage the inherent graph structure of sports data, capturing interactions among players, teams, and game events. By systematically examining various studies, we highlight how GNNs and GCNs can enhance performance prediction, player evaluation, and tactical analysis in sports. Our review emphasizes the unique advantages these models offer over traditional analytical approaches, particularly in their ability to account for the dynamic and interconnected nature of sports activities. We conclude by identifying potential areas for future research and application, suggesting that the integration of advanced graph-based techniques could significantly advance the field of sports analytics.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
