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

About latent roles in forecasting players in team sports