Multi-Modal Trajectory Prediction of NBA Players
- URL: http://arxiv.org/abs/2008.07870v1
- Date: Tue, 18 Aug 2020 11:35:44 GMT
- Title: Multi-Modal Trajectory Prediction of NBA Players
- Authors: Sandro Hauri, Nemanja Djuric, Vladan Radosavljevic, Slobodan Vucetic
- Abstract summary: 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.
Experiments on large, fine-grained NBA tracking data show that the proposed method outperforms the state-of-the-art.
- Score: 14.735704310108101
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
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