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From sidelines to scoreboards: Regression modelling for predicting NBA game outcomes
- 1 Appleby College, Oakville, L6K 3P1, Canada
- 2 Shanghai Experimental School, Shanghai, 200011, China
- 3 Philips Exeter Academy, Exeter, 03833, United States
* Author to whom correspondence should be addressed.
Abstract
This research paper examines the diverse factors that affect score difference between teams in the NBA scene. With this intent, by utilizing data from the 2012-13 regular season, this research aims to develop a predictive model that can forecast the score difference between teams for the last 50 games of the season. Additionally, the same model can be expanded and used for many different seasons of NBA data. To accomplish this, the methodology implemented first involved a data collection process, where many years of injury data and NBA season data were gathered. Next, extensive cleaning was done so all the variable names matched, and only significant information remained. Then, by merging the injury data with data from the 2012-2013 NBA season, a larger, more comprehensive file was created. As last, through the use of regression modelling, a base model was created. In addition, factors impacting the score difference were considered and adjusted the model accordingly. To validate the final model’s prediction, actual score differences in the last 50 games will be compared to the differences given by the model, with statistical measurement methods to quantify the accuracy. By doing so, this research hopes to provide a more valuable system that produce insight towards basketball sports betting.
Keywords
Regression Modelling, Statistics, NBA, Sports Gambling, Point Spread
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Cite this article
Wang,Y.;Ji,H.;Dai,D. (2025). From sidelines to scoreboards: Regression modelling for predicting NBA game outcomes. Applied and Computational Engineering,131,60-71.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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