Email this article Detecting drivers of basketball successful games: an exploratory study with machine learning algorithms
Abstract
This paper aims to detect which are the drivers leading to victory for
basketball matches in NBA, the American National Basketball Association.
First games for regular seasons from 2004-2005 to 2017-2018 have been summarized in terms of box scores and Dean's four factors. Then box scores and four factors have been used as classication independent variables to identify victory drivers, focusing on Golden StateWarriors matches. Both CART and Random Forests machine learning techniques have been applied, and results are compared to assess the more suitable approach.
basketball matches in NBA, the American National Basketball Association.
First games for regular seasons from 2004-2005 to 2017-2018 have been summarized in terms of box scores and Dean's four factors. Then box scores and four factors have been used as classication independent variables to identify victory drivers, focusing on Golden StateWarriors matches. Both CART and Random Forests machine learning techniques have been applied, and results are compared to assess the more suitable approach.
DOI Code:
10.1285/i20705948v13n2p454
Keywords:
basketball;success drivers;classification;machine learning;CART;Random Forests
References
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Ho, T. K. (1995). Random decision forests. In Proceedings of the 3rd International Conference on Document, Analysis and Recognition, pages 278-282. IEEE.
Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis. Potomac Books inc.
Zuccolotto, P. and Manisera, M. (2020). Basketball Data Science - with Applications in R. Chapman and Hall/CRC.
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