ORIGINAL RESEARCH article
Front. Sports Act. Living
Sec. Elite Sports and Performance Enhancement
Volume 7 - 2025 | doi: 10.3389/fspor.2025.1639431
Player Archetypes Within Basketball: Optimizing Roster Composition to Create a Championship Team
Provisionally accepted
- San Diego State University, San Diego, United States
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The present study assessed the feasibility of grouping university and professional basketball players across different leagues based on their playing styles to optimize championship team construction, moving beyond traditional positional constraints towards a positionless approach. A comprehensive dataset of 22,500 elite professional and university athletes from 110 leagues, sourced from EuroBasket, RealGM, and USportsHoops, was analyzed. Player performance was quantified using 13 standardized box score statistics converted to per 48 minutes. Utilizing the k-means algorithm, players were clustered into 9 distinct player archetypes. Multiple linear regression models were then developed for each archetype, predicting "points per minute" to facilitate player ranking, further refined by a data-driven league quality weighting system. Optimal player cluster proportions were derived from analyses of 2018/19 NBA lineups and 2014-2018 international medal-winning teams to create the most effective team line-ups. The model's utility was demonstrated by selecting a hypothetical Team Canada roster for the 2019 FIBA World Cup, which showed effectiveness in identifying a robust team composition and predicting the rise of future high-potential players. Additionally, the model's effectiveness was evaluated by comparing its results to the composition of the 2000 and 2024 Canadian National Olympic teams. The findings revealed 9 unique player clusters, demonstrating the model's potential as a valuable tool for guiding coaching decisions in drafting and signing future players to fit their roster composition effectively. The model proved to be a novel method for talent identification due to its evaluation of players across leagues and its usage of readily accessible data for coaches and scouts alike. Despite limitations related to data sources and subjective weighting, this research provides a sophisticated analytical tool for players, coaches, scouts, and general managers, offering a comprehensive league strength metric and a nuanced player ranking system to enhance roster development alongside the expertise of coaches in the evolving global basketball landscape.
Keywords: NBA, FIBA, Player archetypes, Roster composition, talent identification, Sports analytics
Received: 02 Jun 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 Penner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Luke S.J. Penner, San Diego State University, San Diego, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.