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- W3112052929 abstract "Player behaviors can have a significant impact on the outcome of individual events, as well as the game itself. The increased availability of high quality resolution spatio-temporal data has enabled analysis of player behavior and game strategy. In this paper, we present the implementation and evaluation of an imitation learning method using recurrent neural networks, which allows us to learn individual player behaviors and perform rollouts of player movements on previously unseen play sequences. The method is evaluated using a 2019 dataset from the top-tier soccer league in Sweden (Allsvenskan). Our evaluation provides insights how to best apply the method on movement traces in soccer, the relative accuracy of the method, and how well policies of one player role capture the relative behaviors of a different player role, for example." @default.
- W3112052929 created "2020-12-21" @default.
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- W3112052929 date "2020-01-01" @default.
- W3112052929 modified "2023-09-27" @default.
- W3112052929 title "Predicting Player Trajectories in Shot Situations in Soccer" @default.
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- W3112052929 doi "https://doi.org/10.1007/978-3-030-64912-8_6" @default.
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