Detecting drivers of basketball successful games: an exploratory study with machine learning algorithms
DOI:
https://doi.org/10.1285/i20705948v13n2p454Keywords:
basketball, success drivers, classification, machine learning, CART, Random ForestsAbstract
This paper aims to detect which are the drivers leading to victory forbasketball 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.
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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Published
14-10-2020
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Original Paper
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