Are NBA Players’ Salaries in Accordance with Their Performance on Court?

Ioanna Papadaki () and Michail Tsagris
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Ioanna Papadaki: University of Crete

A chapter in Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, 2022, pp 405-428 from Springer

Abstract: Abstract Researchers and practitioners ordinarily fit linear models in order to estimate NBA player’s salary based on the players’ performance on court. On the contrary, we first select the most important determinants or statistics (years of experience in the league, games played, etc.) and utilize them to predict the player salary shares (salaries with regard to the team’s payroll) by employing the non-linear Random Forest machine learning algorithm. We are further able to accurately classify whether a player is low or highly paid. Additionally, we avoid the phenomenon of over-fitting observed in most papers by external evaluation of the salary predictions. Based on information collected from three distinct periods, 2017–2019, we identify the important factors that achieve very satisfactory salary predictions and we draw useful conclusions. We conclude that player salary shares exhibit a relatively high (non-linear) accordance with their performance on court.

Keywords: NBA; Salaries prediction; Variable selection; Non-linear models (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-030-85254-2_25

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DOI: 10.1007/978-3-030-85254-2_25

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