  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.
