  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.
