Learning Robust Latent Space of Basketball Player Trajectories for Tactics Analysis

Tactic analysis of trajectory data plays an important role in the field of sports science. However, the tactical labels of trajectories are usually insufficient, which limits the deep models to learn the general concept of tactics. In this work, we combine the recurrent variational autoencoder with attention module to learn a robust latent space of the basketball offensive trajectories without the need of labeled data. The learned latent space can be further applied to advanced tasks such as supervised tactics classification, unsupervised tactics clustering, and defensive trajectory generation. The experimental results show that our proposed model achieves better or competitive performance than the previous methods. Furthermore, our proposed model is the first one that focuses on zero-shot tactics clustering and the performance even outperforms the previous methods with supervised and unsupervised settings, which shows the promise of tactics analysis for trajectories in the case of limited labeled data.