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.

这项研究旨在提高复杂排球预测的准确性，并为教练和运动员提供更有意义的见解。我们引入了一种专门的图编码技术，将额外的接触 - 接触排球上下文添加到已有的排球数据集中，无需任何额外的数据收集。我们演示了对这个丰富的数据集使用图神经网络（GNNs）进行三种不同排球预测任务：比赛结果预测、局部位置预测和击球类型预测的潜在优势。通过对比基线模型的性能并分析结果，我们更好地理解了排球比赛中的潜在关系。我们的结果表明，使用我们的图编码和 GNNs 进行数据分析明显提高了整体预测结果。我们还展示了通过简单调整（例如删除被封阻的击球）可以显著改进这些基线预测任务的重要性。最后，我们展示了选择更好地提取特定任务的重要信息的模型架构的重要性。总的来说，我们的研究展示了在体育数据分析中使用图编码的潜在优势和不足，希望能够鼓舞未来在体育和其他领域通过使用基于图的编码进行机器学习策略改进的方法。