Modeling Turn-Based Sequences for Player Tactic Applications in Badminton Matches

Wei-Yao Wang

Abstract: In recent years, a growing body of research has started to explore applying artificial intelligence to the sports industry due to the availability of data and the advancement of techniques. Such applications not only play an important role during matches but also have a great influence on the training stage. However, applying deep learning techniques to sports analytics has several technical challenges, which also remain untouched in badminton analytics as there is no public dataset of stroke event records. Therefore, this dissertation intends to explore challenging research questions and application issues that have not been addressed for benefiting both the research and badminton communities. To achieve the objectives, we, for the first time, propose a unified badminton language to describe the process of the shot, which enables us to conduct downstream applications. Specifically, our first task is to measure the win probability of each shot in badminton matches by considering long-term and short-term dependencies. Second, we introduce a framework with two encoder-decoder extractors and a position-aware fusion network to forecast the possible tactics of players, which is still unexplored in turn-based sports. To provide the transparency of these models, we aim to design a post-hoc explainer by computing feature attributions with Shapley values as the third task. In this manner, researchers can investigate model behaviors for advanced improvement, and the badminton community benefits from coaching the players and determining the strategies. This dissertation is supervised by Wen-Chih Peng ([email protected]).