A cluster analysis of basketball players for each of the five traditionally defined positions

Determining the players` playing styles and bringing the right players together are very important for winning in basketball. This study aimed to group basketball players into similar clusters according to their playing styles for each of the traditionally defined five positions (point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C)). This way, teams would be able to identify their type of players to help them determine what type of players they should recruit to build a better team. The 17 game-related statistics from 15 seasons of the National Basketball Association (NBA) were analyzed using a hierarchical clustering method. The cluster validity indices (CVIs) were used to determine the optimum number of groups. Based on this analysis, four clusters were identified for PG, SG, and SF positions, while five clusters for PF position and six clusters for C position were established. In addition to the definition of the created clusters, their individual achievements were examined based on three performance indicators: adjusted plus-minus (APM), average points differential, and the percentage of clusters on winning teams. This study contributes to the evaluation of team compatibility, which is a significant part of winning, as it allows one to determine the playing styles for each position, while examining the success of position pair combinations.
© Copyright 2024 Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology. SAGE Publications. All rights reserved.

Bibliographic Details
Subjects:
Notations:sport games technical and natural sciences
Tagging:Clusteranalyse KPI
Published in:Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology
Language:English
Published: 2024
Online Access:https://doi.org/10.1177/17543371211062064
Volume:238
Issue:1
Pages:55-75
Document types:article
Level:advanced