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

Project by Polygence alum Raghav

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

Project's result

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

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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.



Polygence mentor

MD Doctor of Medicine candidate


Computer Science, Biology


Case reports in medicine, computational protein design, cryo-EM image analysis, computational genomics, telemedicine, AI in medicine, biochemistry, web development, data journalism




Graduation Year


Project review

“The flexibility of the sessions helped a lot when navigating through my busy schedule each week. ”

About my mentor

“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. ”