Wang, JiachenWu, YihongZhang, XiaolongZeng, YixinZhou, ZhengZhang, HuiXie, XiaoWu, YingcaiBujack, RoxanaArchambault, DanielSchreck, Tobias2023-06-102023-06-1020231467-8659https://doi.org/10.1111/cgf.14825https://diglib.eg.org:443/handle/10.1111/cgf14825Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in-lab behaviors, failed to capture the characteristics of in-situ anticipation behaviors in real matches. This research proposes a data-driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac-Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players' anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors.CCS Concepts: Human-centered computing -> Visual analytics; Information systems -> Data miningHuman centered computingVisual analyticsInformation systemsData mining10.1111/cgf.14825223-23412 pages