Features selection in NBA outcome prediction through Deep Learning

Department of Economics; Italy); Management; Manlio Migliorati (University of Brescia

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Features selection in NBA outcome prediction through Deep Learning

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arxiv 2111.09695 v1 pith:ZIBRB7O2 submitted 2021-11-17 cs.LG

Features selection in NBA outcome prediction through Deep Learning

Manlio Migliorati (University of Brescia , Department of Economics , Management , Italy) This is my paper

classification cs.LG

keywords featuresmodelsbeendeeplearningoutcomepredictionante

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This manuscript is focused on features' definition for the outcome prediction of matches of NBA basketball championship. It is shown how models based on one a single feature (Elo rating or the relative victory frequency) have a quality of fit better than models using box-score predictors (e.g. the Four Factors). Features have been ex ante calculated for a dataset containing data of 16 NBA regular seasons, paying particular attention to home court factor. Models have been produced via Deep Learning, using cross validation.

Features selection in NBA outcome prediction through Deep Learning

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