Predicting Team Performance with Spatial Temporal Graph Convolutional Networks

• URL: http://arxiv.org/abs/2206.10720v1
• Date: Tue, 21 Jun 2022 20:40:35 GMT
• Title: Predicting Team Performance with Spatial Temporal Graph Convolutional Networks
• Authors: Shengnan Hu, Gita Sukthankar
• Abstract summary: This paper presents a new approach for predicting team performance from the behavioral traces of a set of agents. This problem forecasting is very relevant to sports analytics challenges such as coaching and opponent modeling.
• Score: 2.055949720959582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a new approach for predicting team performance from the behavioral traces of a set of agents. This spatiotemporal forecasting problem is very relevant to sports analytics challenges such as coaching and opponent modeling. We demonstrate that our proposed model, Spatial Temporal Graph Convolutional Networks (ST-GCN), outperforms other classification techniques at predicting game score from a short segment of player movement and game features. Our proposed architecture uses a graph convolutional network to capture the spatial relationships between team members and Gated Recurrent Units to analyze dynamic motion information. An ablative evaluation was performed to demonstrate the contributions of different aspects of our architecture.

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