Machine learning-based study of the influence factors on the wins of NBA teams

Kaihang Su

doi:10.1117/12.2655912

12 January 2023 Machine learning-based study of the influence factors on the wins of NBA teams

Kaihang Su

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125092A (2023) https://doi.org/10.1117/12.2655912
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

This paper is a sample of machine learning algorithms applied to analyze how different factors affect the game's result among all National Basketball Association teams. To achieve the study's objectives, this paper selects cluster analysis methods in supervised and unsupervised learning, such as the support vector machines, the nearest-neighbor classification, and the k-means algorithm to build the model. The first step is to classify the training and test sets, to calculate the accuracy and thus obtain the most efficient method of distinguishing between strong and weak teams, and subsequently to visualize the results by downscaling the influencing factors to find the main influence factors that differentiate between strong and weak teams. Finally, the cluster analysis led to the conclusion that the degree to which a team's playing style fits the league's seasonal rules and the values of the variables of the basic data is not directly related to the team's win rate.

Citation Download Citation

Kaihang Su "Machine learning-based study of the influence factors on the wins of NBA teams", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125092A (12 January 2023); https://doi.org/10.1117/12.2655912

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