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

翻译：足球运动预测涉及从球员与球的交互中捕捉复杂动态。本文提出FootBots，一种基于编码器-解码器Transformer的架构，通过等变性特性处理运动预测与条件运动预测。FootBots利用集合注意力块和多注意力块解码器捕捉时序动态与社会动态。评估使用两个数据集：真实足球数据集与定制合成数据集。合成数据集的实验结果表明FootBots的社会注意力机制的有效性及条件运动预测的重要性。在真实足球数据上的实证结果显示，FootBots在运动预测任务中优于基线模型，并在条件预测任务中表现突出，例如基于球位置预测球员、基于球与防守（进攻）球队预测进攻（防守）球队、以及基于所有球员预测球位置。本评估将定量结果与定性发现相结合。https://youtu.be/9kaEkfzG3L8