The purpose of this study is apply STEAMS (Science, Technology, Engineering, Artificial Intelligence, Math, and Statistics) concepts to establish and to compare linear regression and neural network models in order to improve the prediction accuracy of NBA teams' winning records. Over one hundred basketball parameters of last ten years from 2010 to 2019 were collected from the NBA website and based on multivariate analysis of correlations and dependency, eighteen independent key parameters were chosen for the study. Both multivariate linear regression and neural network regression could predict NBA winning records accurately. However, the neural network model could further improve the prediction accuracy. These models can be utilized to interpret the winning strategies of each team. The number of hidden nodes for the artificial neural network was also optimized and it was found that one node is sufficient for this study without overfitting.
This paper adopts the STEAMS approach (Science, Technology, Engineering, Artificial Intelligence, Math, and Statistics) in sports. In particular, baseball and basketball were used in the study. Modern sports have to apply concepts of science in their playing to enhance their individual capabilities and team performances. For example, the Magnus effect, the force that makes a spinning ball swerve, is prevalent in every basketball shot and in baseball, where the pitches can vary in spin and direction. Regression analysis and AI neural network modeling were used to analyze NBA data to predict the season winning percentage of each team. The accuracy of prediction was improved by neural network models.
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