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

In: Advances in Econometrics, Operational Research, Data Science and Actuarial Studies

Author

Listed:
  • Ioanna Papadaki

    (University of Crete)

  • Michail Tsagris

    (University of Crete)

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.

Suggested Citation

  • Ioanna Papadaki & Michail Tsagris, 2022. "Are NBA Players’ Salaries in Accordance with Their Performance on Court?," Contributions to Economics, in: M. Kenan Terzioğlu (ed.), Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, pages 405-428, Springer.
    • Handle: RePEc:spr:conchp:978-3-030-85254-2_25
    DOI: 10.1007/978-3-030-85254-2_25
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