Forecasting players in sports has grown in popularity due to the potential for a tactical advantage and the applicability of such research to multi-agent interaction systems. Team sports contain a significant social component that influences interactions between teammates and opponents. However, it still needs to be fully exploited. In this work, we hypothesize that each participant has a specific function in each action and that role-based interaction is critical for predicting players' future moves. We create RolFor, a novel end-to-end model for Role-based Forecasting. RolFor uses a new module we developed called Ordering Neural Networks (OrderNN) to permute the order of the players such that each player is assigned to a latent role. The latent role is then modeled with a RoleGCN. Thanks to its graph representation, it provides a fully learnable adjacency matrix that captures the relationships between roles and is subsequently used to forecast the players' future trajectories. Extensive experiments on a challenging NBA basketball dataset back up the importance of roles and justify our goal of modeling them using optimizable models. When an oracle provides roles, the proposed RolFor compares favorably to the current state-of-the-art (it ranks first in terms of ADE and second in terms of FDE errors). However, training the end-to-end RolFor incurs the issues of differentiability of permutation methods, which we experimentally review. Finally, this work restates differentiable ranking as a difficult open problem and its great potential in conjunction with graph-based interaction models. Project is available at: https://www.pinlab.org/aboutlatentroles

翻译：在体育运动中预测球员因战术优势的潜力和此类研究对多智能体交互系统的适用性而日益流行。团队运动包含显著的社会成分，影响着队友和对手之间的互动，然而这一因素尚未被充分利用。本研究假设每位参与者在每个动作中扮演特定功能，且基于角色的交互对预测球员未来动作至关重要。我们提出RolFor——一种新颖的端到端角色预测模型。RolFor使用我们新开发的排序神经网络（OrderNN）模块来排列球员顺序，使每个球员被分配一个潜在角色，随后通过RoleGCN对潜在角色进行建模。凭借其图表示，该模型提供了一个完全可学习的邻接矩阵，捕捉角色间关系，并用于预测球员未来轨迹。在具有挑战性的NBA篮球数据集上的大量实验验证了角色的重要性，并证实了我们使用可优化模型对其进行建模的目标。当提供角色先验时，所提出的RolFor优于当前最先进方法（在ADE误差上排名第一，在FDE误差上排名第二）。然而，端到端训练RolFor会面临排列方法可微性的问题，我们对此进行了实验性探讨。最后，本研究重申可微排序是一个难题，且其与基于图的交互模型结合具有巨大潜力。项目地址：https://www.pinlab.org/aboutlatentroles