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.
Detecting drivers of basketball successful games: an exploratory study with machine learning algorithms
manlio migliorati
2020-01-01
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.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
