Graph Encoding and Neural Network Approaches for Volleyball Analytics:
  From Game Outcome to Individual Play Predictions
Rhys Tracy Haotian Xia Alex Rasla Yuan-Fang Wang Ambuj Singh
This research aims to improve the accuracy of complex volleyball predictions
and provide more meaningful insights to coaches and players. We introduce a
specialized graph encoding technique to add additional contact-by-contact
volleyball context to an already available volleyball dataset without any
additional data gathering. We demonstrate the potential benefits of using graph
neural networks (GNNs) on this enriched dataset for three different volleyball
prediction tasks: rally outcome prediction, set location prediction, and hit
type prediction. We compare the performance of our graph-based models to
baseline models and analyze the results to better understand the underlying
relationships in a volleyball rally. Our results show that the use of GNNs with
our graph encoding yields a much more advanced analysis of the data, which
noticeably improves prediction results overall. We also show that these
baseline tasks can be significantly improved with simple adjustments, such as
removing blocked hits. Lastly, we demonstrate the importance of choosing a
model architecture that will better extract the important information for a
certain task. Overall, our study showcases the potential strengths and
weaknesses of using graph encodings in sports data analytics and hopefully will
inspire future improvements in machine learning strategies across sports and
applications by using graphbased encodings.