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Visualization for Potential Pass Courses and Quantification for Offensive and Defensive Players in Basketball

Recently, data analysis and visualization of player and ball position data have attracted attention in basketball. Based on this, we previously proposed a method for predicting potential pass receivers in basketball, which can be utilized in a three-dimensional visualization tool to reproduce games. We propose three methods based on the previous prediction method for potential pass receivers. The first method is used to display representative pass courses for potential pass receivers. The second is used to evaluate potential pass receivers' relation to scores. The third method quantifies information regarding defensive players. The results produced by these three methods can be used to enhance users' understanding of attack patterns and plays in the visualization tool. These methods were also validated using real women's basketball datasets. As a result, it was demonstrated that the proposed methods performed effectively.