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Predicting In-game Actions from Interviews of NBA Players
2020
Computational Linguistics
Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. While there is an abundance of computational work on player metrics prediction based on past performance, very few attempts to incorporate out-ofgame signals have been made. Specifically, it was previously unclear whether linguistic signals gathered from players' interviews can add information which does not appear in performance metrics. To bridge that
doi:10.1162/coli_a_00383
fatcat:wefokovrm5g3niykivrtjsdjf4