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Entropy 2016, 18(12), 450; doi:10.3390/e18120450

Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

1
Department of Information Engineering, Xiangtan University, Xiangtan 411105, China
2
Department of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China
*
Author to whom correspondence should be addressed.
Academic Editor: Adom Giffin
Received: 26 September 2016 / Revised: 10 December 2016 / Accepted: 10 December 2016 / Published: 16 December 2016
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Abstract

Predicting the outcome of National Basketball Association (NBA) matches poses a challenging problem of interest to the research community as well as the general public. In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model. Our results reveal that the model is able to predict the winning team with 74.4% accuracy, outperforming other classical machine learning algorithms that could only afford a maximum prediction accuracy of 70.6% in the experiments that we performed. View Full-Text
Keywords: maximum entropy model; k-means clustering; accuracy; classification; sports forecasting maximum entropy model; k-means clustering; accuracy; classification; sports forecasting
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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