Features selection in NBA outcome prediction through Deep Learning

• URL: http://arxiv.org/abs/2111.09695v1
• Date: Wed, 17 Nov 2021 16:32:11 GMT
• Title: Features selection in NBA outcome prediction through Deep Learning
• Authors: Manlio Migliorati (University of Brescia, Department of Economics and Management, Italy)
• Abstract summary: It is shown how models based on one feature (Elo rating or the relative victory frequency) have a quality of fit better than models using box-score predictors. Features have been ex ante calculated for a dataset containing data of 16 NBA regular seasons.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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