Machine learning approach to predicting a basketball game outcome

Roger Poch Alonso and Marina Bagić Babac

International Journal of Data Science, 2022, vol. 7, issue 1, 60-77

Abstract: The outcome of a basketball match depends on many factors, such as the morale of a team or a player, skills, coaching strategy, and many others. Thus, it is a challenging task to predict the exact results of individual matches. This paper shows how to learn from historical data about previous basketball games, including both individual and team features, to predict future matches. It outlines the advantages and disadvantages of existing machine learning systems and tries to apply the best practices focusing on a case study of the National Basketball Association (NBA). In addition, a comparison between different machine learning algorithms in search of the most accurate prediction is provided.

Keywords: machine learning; supervised learning; prediction; KNN; k-nearest neighbours; decision trees; Naive Bayes classifier; basketball; NBA. (search for similar items in EconPapers)
Date: 2022
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