Multi-Modal Trajectory Prediction of NBA Players
This work addresses trajectory prediction for basketball analytics, offering incremental improvements in modeling player decision-making.
The paper tackled the problem of predicting multi-modal movement trajectories of NBA players during games, proposing an LSTM-based method with a multi-modal loss function that outperformed state-of-the-art approaches and generated more realistic trajectories while learning individual playing styles.
National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game. As a step towards understanding how players make their decisions, we focus on their movement trajectories during games. We propose a method that captures the multi-modal behavior of players, where they might consider multiple trajectories and select the most advantageous one. The method is built on an LSTM-based architecture predicting multiple trajectories and their probabilities, trained by a multi-modal loss function that updates the best trajectories. Experiments on large, fine-grained NBA tracking data show that the proposed method outperforms the state-of-the-art. In addition, the results indicate that the approach generates more realistic trajectories and that it can learn individual playing styles of specific players.