  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
