Regression Analysis on NBA Players Background and Performance using Gaussian Processes : Can NBA-drafts be improved by taking socioeconomic background into consideration?

Abstract: In the modern society it is well known that an individual’s background matters in his career, but should it be taken into consideration in a recruiting process in general and a recruiting process of NBA-players in particular? Previous research shows that white basketball players from high-income families have a 75% higher chance of becoming an NBA player compared to a white basketball player from a low-income family. In this paper, we have examined whether there is a connection between NBA-player background and the chances of succeeding in the NBA given that the player has been picked in the NBA-draft. The results have been carried out using machine learning algorithms based on Gaussian Processes. The results show that draft decisions will not be improved by taking socio-economic background into consideration.