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Examination of National Basketball Association (NBA) team values based on dynamic linear mixed models

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  • Efehan Ulas

Abstract

In the last decade, NBA has grown into a billion-dollar industry where technology and advanced game plans play an essential role. Investors are interested in research examining the factors that can affect the team value. The aim of this research is to investigate the factors that affect the NBA team values. The value of a team can be influenced not only by performance-based variables, but also by macroeconomic indicators and demographic statistics. Data, analyzed in this study, contains of game statistics, economic variables and demographic statistics of the 30 teams in the NBA for the 2013–2020 seasons. Firstly, Pearson correlation test was implemented in order to identify the related variables. NBA teams’ characteristics and similarities were assessed with Machine Learning techniques (K-means and Hierarchical clustering). Secondly, Ordinary linear regression (OLS), fixed effect and random effect models were implemented in the statistical analyses. The models were compared based on Akaike Information Criterion (AIC). Fixed effect model with one lag was found the most effective model and our model produced consistently good results with the R2 statistics of 0.974. In the final model, we found that the significant determinants of team value at the NBA team level are revenue, GDP, championship, population and key player. In contrast, the total number of turnovers has a negative impact on team value. These findings would be beneficial to coaches and managers to improve their strategies to increase their teams’ value.

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  • Efehan Ulas, 2021. "Examination of National Basketball Association (NBA) team values based on dynamic linear mixed models," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0253179
    DOI: 10.1371/journal.pone.0253179
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    References listed on IDEAS

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    1. Donald L. Alexander & William Kern, 2004. "The Economic Determinants of Professional Sports Franchise Values," Journal of Sports Economics, , vol. 5(1), pages 51-66, February.
    2. Nuno Mateus & Bruno Gonçalves & Eduardo Abade & Hongyou Liu & Lorena Torres-Ronda & Nuno Leite & Jaime Sampaio, 2015. "Game-to-game variability of technical and physical performance in NBA players," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 15(3), pages 764-776, December.
    3. Shaoliang Zhang & Alberto Lorenzo & Miguel-Angel Gómez & Hongyou Liu & Bruno Gonçalves & Jaime Sampaio, 2017. "Players’ technical and physical performance profiles and game-to-game variation in NBA," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(4), pages 466-483, July.
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    Cited by:

    1. Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A robust method for clustering football players with mixed attributes," Annals of Operations Research, Springer, vol. 325(1), pages 9-36, June.

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