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Improving PER (Player Efficiency Rating) in Basketball through Machine Learning

Graduation Year
Student review
The flexibility of the sessions helped a lot when navigating through my busy schedule each week.

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Project description

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.

Improving PER (Player Efficiency Rating) in Basketball through Machine Learning
Project outcome

Presentation for Symposium of Rising Scholars and Research Paper for Journal of Sports Analytics, Curieux Academic Journal, and Research Archive of Rising Scholars

MD Doctor of Medicine candidate
Biology, Comp Sci
Case reports in medicine, computational protein design, cryo-EM image analysis, computational genomics, telemedicine, AI in medicine, biochemistry, web development, data journalism
Mentor review

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.

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