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，体育运动中球员预测的研究日益流行。团队运动包含显著的社会性成分，影响着队友与对手之间的互动，然而这一因素尚未得到充分利用。本研究假设每个参与者在每次行动中具有特定功能，且基于角色的交互对于预测球员未来动向至关重要。我们提出了RolFor——一种新型的端到端角色预测模型。RolFor利用我们开发的全新模块——排序神经网络（OrderNN）来排列球员顺序，使每个球员分配至一个潜在角色。该潜在角色随后通过RoleGCN进行建模。得益于其图表示结构，该模型提供完全可学习的邻接矩阵，以捕捉角色间关系，并进而用于预测球员未来轨迹。在具有挑战性的NBA篮球数据集上进行的大量实验验证了角色的重要性，并证明了我们使用可优化模型对其建模的目标的合理性。当提供预定义角色时，所提出的RolFor模型优于当前最先进方法（在平均位移误差ADE指标上排名第一，在最终位移误差FDE指标上排名第二）。然而，端到端RolFor的训练面临排列方法可微性的问题，我们在实验中对此进行了考察。最后，本研究重申了可微排序作为一个尚未解决的开放性问题及其与基于图的交互模型结合的巨大潜力。项目地址：https://www.pinlab.org/aboutlatentroles