ORCID
Nick Riccardi: 0009-0004-8349-1083
Document Type
Article
Date
Spring 4-9-2024
Keywords
team performance, NBA, clustering, complementary, roles
Language
English
Disciplines
Applied Statistics | Data Science | Econometrics | Sports Studies | Statistical Methodology
Description/Abstract
This study aims to quantify the effect that complementary player types have on team success in the National Basketball Association. Using cluster analysis, player-seasons are redefined from their traditional basketball positions to better encompass the roles that players play. For the 10 seasons of data, the best player for each of the 30 teams in the league is determined and teams are grouped based on the cluster of their best player. Ordinary Least Squares regressions are performed to test what player types fit together best. The results of this study show the importance of complementary workers to a firm’s success.
Recommended Citation
Riccardi, N. (2023). Optimizing NBA Roster Construction. Academy of Economics and Finance Journal, 14, 28-38.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Applied Statistics Commons, Data Science Commons, Econometrics Commons, Sports Studies Commons, Statistical Methodology Commons
