  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.
