Visual Analytics of Ball Handlers’ Decisions in Basketball Games

In basketball, decision-making is one of the core skills for players. For example, when a player is holding the ball, the success of the team’s offense is primarily determined by her/his decisions (i.e., pass, shoot, or dribble) in response to the dynamics of the game. Understanding players’ decision-making processes in changing game situations can help coaches develop effective strategies, which is critical for the success of a team. However, the decision-making process is influenced by various factors (e.g., player’s playing style, opponents’ defense, and time remaining), making understanding a challenging problem. In this study, we propose HoopScouter, a visual analytics system to help understand ball handlers’ decisions in basketball games. Based on a careful investigation of the analysis requirements, we first introduce a representation learning method that characterizes ball handlers’ decision-making styles. We then design a sketch panel with integrated time information to support exploration of player decisions under similar game scenarios. Facet views and coordinated interactions are also provided to identify the strengths and weaknesses of the ball handler’s decision-making, and to understand when and why ball handlers would make certain decisions. To validate the effectiveness of HoopScouter, we conduct two case studies on real-world basketball games and receive positive feedback from domain experts.