The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity

Article


Nguyen, Nguyen Hoang, Nguyen, Duy Thien An, Ma, Bingkun and Hu, Jiang. 2022. "The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity." Journal of Information and Telecommunication. 6 (2), pp. 217-235. https://doi.org/10.1080/24751839.2021.1977066
Article Title

The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity

ERA Journal ID213291
Article CategoryArticle
AuthorsNguyen, Nguyen Hoang, Nguyen, Duy Thien An, Ma, Bingkun and Hu, Jiang
Journal TitleJournal of Information and Telecommunication
Journal Citation6 (2), pp. 217-235
Number of Pages19
Year2022
PublisherTaylor & Francis
Place of PublicationUnited Kingdom
ISSN2475-1839
2475-1847
Digital Object Identifier (DOI)https://doi.org/10.1080/24751839.2021.1977066
Web Address (URL)https://www.tandfonline.com/doi/full/10.1080/24751839.2021.1977066
Abstract

Basketball is known for the vast amount of data collected for each player, team, game, and season. As a result, basketball is an ideal domain to work on different data analysis techniques to gain useful insights. In this study, we continued our previous study published in 2020 Computational Collective Intelligence (12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings) reviewing some important factors to predict players’ future performance and being selected in an All-Star game, one of the most prestigious events, of National Basket Association league. Besides traditional Machine Learning, Deep Learning is also applied in this study for prediction purpose. However, compared to traditional Machine Learning, Deep Learning’s performance is not as good for our dataset. It is understandable when our data are relatively small and structured with a few predictor variables which limited Deep Learning’s ability to deal with a vast amount of Big Data. Our final results, through both Regression and Classification Analysis, indicated that scoring is the most important factor from the primary players for any team and also basketball fan’s favourable style.

KeywordsData mining; machine learning; deep learning; sport; imbalanced data
ANZSRC Field of Research 20204207. Sports science and exercise
Byline AffiliationsTexas Tech University, United States
University of Southern Queensland
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