Hey devs, sports fans, and data nerds!

After weeks of work, I'm excited to share DeepShot – an advanced NBA game predictor powered by historical data from Basketball Reference, machine learning, and a clean NiceGUI-powered web interface.

What it does: DeepShot uses team-level rolling averages (including Exponentially Weighted Moving Averages) and an Elo rating system to accurately predict NBA game outcomes. All predictions are visualized in real time through a sleek, responsive UI.

Key Features: Data-Driven Predictions using past performance & rolling trends EWMA-based Weighted Stats Engine Elo Ratings for contextual team strength Cross-platform interface built with NiceGUI Key stats highlight to visualize matchup advantages at a glance Tech Stack: Python Pandas, Scikit-learn, XGBoost BeautifulSoup, Requests NiceGUI for the frontend Hosted locally, runs on Windows/macOS/Linux Clone it here → github.com/saccofrancesco/deepshot

Want to see how predictive modeling and sports analytics come together? This is for you.

Feedback, stars, forks, and PRs are more than welcome!

Let me know what you think, or drop your ideas for improvements — always open to suggestions!

#NBA #Python #MachineLearning #SportsAnalytics #OpenSource #NiceGUI #PredictiveModeling #GitHub #XGBoost #EWMA #EloRating #Basketball