  Sophisticated trajectory prediction models that effectively mimic team
dynamics have many potential uses for sports coaches, broadcasters and
spectators. However, through experiments on soccer data we found that it can be
surprisingly challenging to train a deep learning model for player trajectory
prediction which outperforms linear extrapolation on average distance between
predicted and true future trajectories. We propose and test a novel method for
improving training by predicting a sparse trajectory and interpolating using
constant acceleration, which improves performance for several models. This
interpolation can also be used on models that aren't trained with sparse
outputs, and we find that this consistently improves performance for all tested
models. Additionally, we find that the accuracy of predicted trajectories for a
subset of players can be improved by conditioning on the full trajectories of
the other players, and that this is further improved when combined with sparse
predictions. We also propose a novel architecture using graph networks and
multi-head attention (GraN-MA) which achieves better performance than other
tested state-of-the-art models on our dataset and is trivially adapted for both
sparse trajectories and full-trajectory conditioned trajectory prediction.
