GabrielPastorello/NBA-Modelo-MVP
Use of Machine Learning tools with Python to observe the patterns in the logic of the MVP choice, verifying which are the most important statistics in this award.
This project helps basketball analysts and fans understand the key statistics that influence the NBA Most Valuable Player (MVP) award. It takes historical NBA player statistics as input and reveals which performance metrics are most crucial for MVP consideration. The primary users are sports statisticians, data-savvy basketball enthusiasts, and sports journalists.
No commits in the last 6 months.
Use this if you want to quantitatively understand what makes an NBA player an MVP candidate based on their season-long performance.
Not ideal if you're looking for real-time predictions or an application for betting purposes.
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Language
Jupyter Notebook
License
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Last pushed
Sep 29, 2022
Commits (30d)
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