Player archetypes within basketball: optimizing roster composition to create a championship team

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 min. Utilizing the k-means algorithm, players were clustered 1nto 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 1nternational 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 1n 1dentifying a robust team composition and predicting the rise of future high-potential players. Additionally, the model's effectiveness was evaluated by comparing 1ts 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 5aluable tool for guiding coaching decisions 1n drafting and signing future players to fit their roster composition effectively. The model proved to be a novel method for talent identification due to 1ts evaluation of players across leagues and 1ts 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 1n the evolving global basketball landscape.

Authors

Institutions

Publication Details

Journal
Frontiers in Sports and Active Living
Published
2025-08-29
DOI
https://doi.org/10.3389/fspor.2025.1639431
Citations
1
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
3.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Player archetypes within basketball: optimizing roster composition to create a championship team

Luke S. J. Penner
1 citations
Frontiers in Sports and Active Living
Sports Analytics and Performance
3.13
article

Player archetypes within basketball: optimizing roster composition to create a championship team

Luke S. J. Penner
article en
1 citations

Abstract

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 min. Utilizing the k-means algorithm, players were clustered 1nto 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 1nternational 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 1n 1dentifying a robust team composition and predicting the rise of future high-potential players. Additionally, the model's effectiveness was evaluated by comparing 1ts 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 5aluable tool for guiding coaching decisions 1n drafting and signing future players to fit their roster composition effectively. The model proved to be a novel method for talent identification due to 1ts evaluation of players across leagues and 1ts 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 1n the evolving global basketball landscape.

Frontiers in Sports and Active LivingVol. 7
San Diego State University (US)
Openalex Percentile: Top 7%
Sports Analytics and Performance
3.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.