# Features selection in NBA outcome prediction through Deep Learning

arXiv: https://arxiv.org/abs/2111.09695 · HTML: https://mlsanity.com/paper/2111.09695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 75 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)

## Abstract (arXiv)

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.