NBA Game Result Prediction Using Feature Analysis and Machine Learning

Fadi Thabtah (), Li Zhang and Neda Abdelhamid
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Fadi Thabtah: Manukau Institute of Technology
Li Zhang: Manukau Institute of Technology
Neda Abdelhamid: Auckland Institute of Studies

Annals of Data Science, 2019, vol. 6, issue 1, No 6, 103-116

Abstract: Abstract In the recent years, sports outcome prediction has gained popularity, as demonstrated by massive financial transactions in sports betting. One of the world’s popular sports that lures betting and attracts millions of fans worldwide is basketball, particularly the National Basketball Association (NBA) of the United States. This paper proposes a new intelligent machine learning framework for predicting the results of games played at the NBA by aiming to discover the influential features set that affects the outcomes of NBA games. We would like to identify whether machine learning methods are applicable to forecasting the outcome of an NBA game using historical data (previous games played), and what are the significant factors that affect the outcome of games. To achieve the objectives, several machine learning methods that utilise different learning schemes to derive the models, including Naïve Bayes, artificial neural network, and Decision Tree, are selected. By comparing the performance and the models derived against different features sets related to basketball games, we can discover the key features that contribute to better performance such as accuracy and efficiency of the prediction model. Based on the results analysis, the DRB (defensive rebounds) feature was chosen and was deemed as the most significant factor influencing the results of an NBA game. Furthermore, others crucial factors such as TPP (three-point percentage), FT (free throws made), and TRB (total rebounds) were also selected, which subsequently increased the model’s prediction accuracy rate by 2–4%.

Keywords: Classification; Data mining; Features selection; Machine learning; NBA; Prediction; Sports analytics (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s40745-018-00189-x

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