Estimating NBA players salary share according to their performance on court: A machine learning approach

TL;DR

This paper introduces a machine learning approach using Random Forests to predict NBA players' salary shares based on key performance statistics, avoiding overfitting and providing accurate, externally evaluated predictions.

Contribution

It presents a novel application of non-linear machine learning to predict salary shares, focusing on important determinants and external validation, which improves over traditional linear models.

Findings

Key performance factors significantly influence salary shares.

Random Forest model achieves high prediction accuracy.

External evaluation confirms model robustness.

Abstract

It is customary for researchers and practitioners to fit linear models in order to predict NBA player's salary based on the players' performance on court. On the contrary, we focus on the players salary share (with regards to the team payroll) by first selecting the most important determinants or statistics (years of experience in the league, games played, etc.) and then utilise them to predict the player salaries by employing a non linear Random Forest machine learning algorithm. We externally evaluate our salary predictions, thus we avoid the phenomenon of over-fitting observed in most papers. Overall, using data from three distinct periods, 2017-2019 we identify the important factors that achieve very satisfactory salary predictions and we draw useful conclusions.