Sports analytics has captured increasing attention since analysis of the
various data enables insights for training strategies, player evaluation, etc.
In this paper, we focus on predicting what types of returning strokes will be
made, and where players will move to based on previous strokes. As this problem
has not been addressed to date, movement forecasting can be tackled through
sequence-based and graph-based models by formulating as a sequence prediction
task. However, existing sequence-based models neglect the effects of
interactions between players, and graph-based models still suffer from
multifaceted perspectives on the next movement. Moreover, there is no existing
work on representing strategic relations among players' shot types and
movements. To address these challenges, we first introduce the procedure of the
Player Movements (PM) graph to exploit the structural movements of players with
strategic relations. Based on the PM graph, we propose a novel Dynamic Graphs
and Hierarchical Fusion for Movement Forecasting model (DyMF) with interaction
style extractors to capture the mutual interactions of players themselves and
between both players within a rally, and dynamic players' tactics across time.
In addition, hierarchical fusion modules are designed to incorporate the style
influence of both players and rally interactions. Extensive experiments show
that our model empirically outperforms both sequence- and graph-based methods
and demonstrate the practical usage of movement forecasting.

本文介绍了一种基于 PM 图和互动风格提取器的动态图和分层融合运动预测模型（DyMF），用于预测运动员的回球类型和动作位置。该模型在运动预测上实证表现优于基于序列和基于图形的方法。