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：由于战术优势的潜力以及此类研究在多智能体交互系统中的适用性，运动中的球员预测日益流行。团队运动包含显著的社会成分，影响队友和对手之间的互动。然而，这一成分尚未被充分利用。在本研究中，我们假设每个参与者每次行动都有特定功能，且基于角色的互动对于预测球员未来移动至关重要。我们提出了RolFor，一种新颖的端到端角色预测模型。RolFor使用我们开发的新模块——排序神经网络（OrderNN）——来调整球员的顺序，使每个球员被分配一个潜在角色。随后，该潜在角色通过RoleGCN进行建模。凭借其图谱表示，它提供了一个完全可学习的邻接矩阵，捕捉角色间的关系，并用于预测球员的未来轨迹。在具有挑战性的NBA篮球数据集上进行的大量实验证实了角色的重要性，并证明了我们使用可优化模型对其进行建模的目标。当Oracle提供角色时，所提出的RolFor相比当前最先进的方法表现优越（在ADE误差上排名第一，在FDE误差上排名第二）。然而，训练端到端RolFor涉及排列方法可微性的问题，我们通过实验对其进行了评估。最后，本研究重申了可微排名是一个困难的开放性问题，并指出了其与基于图谱的交互模型结合的巨大潜力。项目地址：https://www.pinlab.org/aboutlatentroles