Improving PER (Player Efficiency Rating) in Basketball through Machine Learning
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This project 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.
My mentor was extremely knowledgeable in her field and was a great listener who helped and gave me thoughtful advice throughout the entire research paper process.