Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

How to assess leader capabilities: Applying AI algorithms to evaluate NBA head coaches

How to assess leader capabilities: Applying AI algorithms to evaluate NBA head coaches This study proposes a novel machine learning–based approach for assessing leadership capability by quantifying the season-level impact of head coaches in the National Basketball Association (NBA). Harnessing 24 seasons of NBA data (1999–2023), we estimate each team's theoretical win probability for every game using only the prior season's player statistics, deliberately excluding coaching effects. The discrepancy between these predictions and actual outcomes is interpreted as the coach's marginal contribution. To validate the robustness of this framework, we applied multiple machine learning algorithms, with LightGBM achieving the highest prediction accuracy at 68.50%. Although the improvement over the baseline accuracy is modest (1.25%), this finding carries nontrivial implications in professional sports, where small performance margins can yield substantial competitive and economic benefits. In contrast to traditional win–loss or tenure-based metrics, our method establishes a performance-adjusted baseline for leadership evaluation applicable to both sports and non-sports contexts. Furthermore, the study advances leadership assessment by providing benchmarks that transcend conventional win-rate metrics, thereby offering scalable, data-driven tools to measure managerial effectiveness in high-performance settings. Overall, this framework contributes to both sports analytics and organizational leadership by furnishing an interpretable model for evaluating leadership capability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Sports Analytics IOS Press

How to assess leader capabilities: Applying AI algorithms to evaluate NBA head coaches

Kim, Philsoo; Lee, Sanghyun
Journal of Sports Analytics , Volume 11: 1 – Jul 1, 2025
Download PDF
16 pages

Loading next page...
 
/lp/iospress/how-to-assess-leader-capabilities-applying-ai-algorithms-to-evaluate-WEmLeYlhjM

References (60)

Publisher
IOS Press
Copyright
© The Author(s) 2025
ISSN
2215-020X
eISSN
2215-0218
DOI
10.1177/22150218251357538
Publisher site
See Article on Publisher Site

Abstract

This study proposes a novel machine learning–based approach for assessing leadership capability by quantifying the season-level impact of head coaches in the National Basketball Association (NBA). Harnessing 24 seasons of NBA data (1999–2023), we estimate each team's theoretical win probability for every game using only the prior season's player statistics, deliberately excluding coaching effects. The discrepancy between these predictions and actual outcomes is interpreted as the coach's marginal contribution. To validate the robustness of this framework, we applied multiple machine learning algorithms, with LightGBM achieving the highest prediction accuracy at 68.50%. Although the improvement over the baseline accuracy is modest (1.25%), this finding carries nontrivial implications in professional sports, where small performance margins can yield substantial competitive and economic benefits. In contrast to traditional win–loss or tenure-based metrics, our method establishes a performance-adjusted baseline for leadership evaluation applicable to both sports and non-sports contexts. Furthermore, the study advances leadership assessment by providing benchmarks that transcend conventional win-rate metrics, thereby offering scalable, data-driven tools to measure managerial effectiveness in high-performance settings. Overall, this framework contributes to both sports analytics and organizational leadership by furnishing an interpretable model for evaluating leadership capability.

Journal

Journal of Sports Analytics – IOS Press

Published: Jul 1, 2025

Keywords: head coach capabilities; machine learning; NBA; organizational leadership assessment; performance prediction; sports analytics

There are no references for this article.