Player evaluation in a racket sport via deep reinforcement learning with technical and tactical contexts

Abstract

Evaluating the performance of players in dynamic competition plays a vital role in effective sports coaching. However, the evaluation of players in racket sports has been still difficult in a quantitative manner, because it is derived from the integration of complex tactical and technical (i.e., whole-body movement) performances. In this paper, we propose a new evaluation method for racket sports based on deep reinforcement learning, which can analyze the player's motion in more detail than the results (i.e., scores). Our method uses historical data including players' tactical and technical performance information to learn the next score probability as Q function, which is used to value players’ actions. We verified our approach by comparing various models and present the effectiveness of our method through use cases that analyze the performance of the top badminton players in world-class events.