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[1J2-GS-10d-02] Classification of Basketball Players Based on Mapper Network
Keywords:Mapper, Unsupervised Learning, Topological Data Analysis, Network Analysis, Sports Data Analysis
Basketball players generally have fallen into one of five positions. Recently, however, the roles of those positions
have become ambiguous and there exist players with different playing styles even in the same position. In this
study, we aim to classify the playing styles of 6,600 players who have played in the past 20 seasons from 2000-01 to 2019-20 by visualizing their playing data with Mapper networks. In addition, we compare the differences of
playing styles in the the past 20 seasons by quantifying the results of the visualizations which are represented by
networks. As a result of the analysis, we classified NBA players into 11 playing styles. Moreover, we found that
the differences in playing styles among players have increased over time.
have become ambiguous and there exist players with different playing styles even in the same position. In this
study, we aim to classify the playing styles of 6,600 players who have played in the past 20 seasons from 2000-01 to 2019-20 by visualizing their playing data with Mapper networks. In addition, we compare the differences of
playing styles in the the past 20 seasons by quantifying the results of the visualizations which are represented by
networks. As a result of the analysis, we classified NBA players into 11 playing styles. Moreover, we found that
the differences in playing styles among players have increased over time.
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