I built DeepShot, a machine learning model that predicts NBA games using rolling statistics, historical performance, and recent momentum — all visualized in a clean, interactive web app. Unlike simple averages or betting odds, DeepShot uses Exponentially Weighted Moving Averages (EWMA) to capture recent form and momentum, highlighting the key statistical differences between teams so you can see why the model favors one side. It’s powered by Python, XGBoost, Pandas, Scikit-learn, and NiceGUI, runs locally on any OS, and relies only on free, public data from Basketball Reference. If you’re into sports analytics, machine learning, or just curious whether an algorithm can outsmart Vegas, check it out and let me know what you think:
https://github.com/saccofrancesco/deepshot
zahlman•2h ago
This is a lot of buzzwords to describe what I'm pretty sure is either very standard analysis technique in the field, or else known to be problematic for some reason or other.
> highlighting the key statistical differences between teams so you can see why the model favors one side
This is effectively just debug output and similarly doesn't need to be puffed up like that.
> or just curious whether an algorithm can outsmart Vegas
If it could, why are you here advertising the project rather than doing so yourself?