Improving PER (Player Efficiency Rating) in Basketball through Machine Learning
Project by Polygence alum Raghav
Project's result
Presented in the Symposium of Rising Scholars; Published Research Paper in 3 journals: Curieux Academic Journal, Research Archive of Rising Scholars, and ResearchGate
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Summary
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.
Christine
Polygence mentor
MD Doctor of Medicine candidate
Subjects
Biology, Computer Science
Expertise
Case reports in medicine, computational protein design, cryo-EM image analysis, computational genomics, telemedicine, AI in medicine, biochemistry, web development, data journalism
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Raghav
Student
Graduation Year
2025
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.”
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