Using Clustering to Analyze Positions in Professional Basketball Through Different Eras

Basketball is a sport played between two teams of five players, with each player typically assigned to one of five traditional positions. These positions then help define a player's role during a game. The style of play in basketball has evolved over time, and this has caused some to question the usefulness of labelling players using the five traditional positions. This has motivated research into defining new positional roles for professional basketball players. Most of the work in this area has focused on analyzing the modern National Basketball Association (NBA), but little has been done comparing styles of play across different eras. To investigate this further, this project uses k-means clustering to analyze multiple years of performance data from the NBA in order to study the evolution of player positions across different eras. The findings of this research can be used to better understand how the game has changed and can also aid in player evaluation and planning for team composition.

clustering

sports analytics 

Beginner

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2023