Preprint Article Version 1 NOT YET PEER-REVIEWED

Predicting the Outcome of NBA Playoffs Based on Maximum Entropy Principle

Version 1 : Received: 26 September 2016 / Approved: 27 September 2016 / Online: 27 September 2016 (11:10:50 CEST)

A peer-reviewed article of this Preprint also exists.

Cheng, G.; Zhang, Z.; Kyebambe, M.N.; Kimbugwe, N. Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle. Entropy 2016, 18, 450. Cheng, G.; Zhang, Z.; Kyebambe, M.N.; Kimbugwe, N. Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle. Entropy 2016, 18, 450.

Journal reference: Entropy 2016, 18, 450
DOI: 10.3390/e18120450

Abstract

Predicting the outcome of a future game between two National Basketball Association
(NBA) teams poses a challenging problem of interest to statistical scientists as well as the general
public. In this article, we formalize the problem of predicting the game results as a classification
problem and apply the principle of maximum entropy to construct NBA maximum entropy
(NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA
playoffs by the NBAME model. The best NBAME model is able to correctly predict the winning
team 74.4 percent of the time as compared to some other machine learning algorithms which is
correct 69.3 percent of the time.

Subject Areas

Maximum entropy model; K-means clustering; accuracy; classification; sports forecasting

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