Features selection in NBA outcome prediction through Deep Learning

2021/11/17 by Manlio Migliorati, Migliorati, Manlio
Economics, Econometrics and Finance · Engineering · Medicine · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sports Analytics and Performance #Sports Dynamics and Biomechanics #Sports Performance and Training

paper · pdf · doi:10.48550/arxiv.2111.09695

openalex publication_date 2021/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Match analysis in football: a systematic review

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