Improving Player Efficiency Rating in Basketball through Machine Learning
DOI:
https://doi.org/10.58445/rars.1000Keywords:
Lasso Regression, Random Forest Regression, Neural NetworksAbstract
This paper explores the intersection of advanced statistical methodologies and basketball with a focus on improving the Player Efficiency Rating (PER) metric. This research delves into three distinct AI models: Lasso Regression, Random Forest Regression, and Neural Networks. These models, each with unique capabilities, allow for more accurate PER ratings which helps teams and coaches to make informed decisions about player rotations and substitutions.
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