  Motion prediction in soccer involves capturing complex dynamics from player
and ball interactions. We present FootBots, an encoder-decoder
transformer-based architecture addressing motion prediction and conditioned
motion prediction through equivariance properties. FootBots captures temporal
and social dynamics using set attention blocks and multi-attention block
decoder. Our evaluation utilizes two datasets: a real soccer dataset and a
tailored synthetic one. Insights from the synthetic dataset highlight the
effectiveness of FootBots' social attention mechanism and the significance of
conditioned motion prediction. Empirical results on real soccer data
demonstrate that FootBots outperforms baselines in motion prediction and excels
in conditioned tasks, such as predicting the players based on the ball
position, predicting the offensive (defensive) team based on the ball and the
defensive (offensive) team, and predicting the ball position based on all
players. Our evaluation connects quantitative and qualitative findings.
https://youtu.be/9kaEkfzG3L8
