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Regression Analysis on NBA Players Background and Performance using Gaussian Processes: Can NBA-drafts be improved by taking socioeconomic background into consideration?
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. , 46 p.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-153767OAI: diva2:753639
Educational program
Master of Science in Engineering -Engineering Physics
Available from: 2014-10-08 Created: 2014-10-08 Last updated: 2014-10-08Bibliographically approved

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Ludvig Persson Lejon & Fredrik Berntsson Kandidatexam(947 kB)218 downloads
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