<div align="center"> <img src="static/icon.png" alt="DeepShot - NBA Game Prediction Model" width="350"> <h1>DeepShot: Predict NBA Games with Machine Learning</h1> </div> <p align="center"> <a href="https://www.buymeacoffee.com/saccofrancesco"> <img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=☕&slug=saccofrancesco&button_colour=FFDD00&font_colour=000000&font_family=Bree&outline_colour=000000&coffee_colour=ffffff" /> </a> </p> <h4 align="center">An advanced NBA game predictor powered by historical data from <a href="https://www.basketball-reference.com" target="_blank">Basketball Reference</a>, rolling statistics, and machine learning — built with <a href="https://nicegui.io" target="_blank">NiceGUI</a> for a seamless experience.</h4> <p align="center"> <img src="https://img.shields.io/github/contributors/saccofrancesco/deepshot?style=for-the-badge" alt="Contributors"> <img src="https://img.shields.io/github/forks/saccofrancesco/deepshot?style=for-the-badge" alt="Forks"> <img src="https://img.shields.io/github/stars/saccofrancesco/deepshot?style=for-the-badge" alt="Stars"> </p> <p align="center"> <a href="#tldr">TL;DR</a> • <a href="#key-features">Key Features</a> • <a href="#quickstart">Quickstart</a> • <a href="#credits">Credits</a> • <a href="#license">License</a> </p> <div align="center"> <img src="./static/usage.gif" alt="DeepShot in action"> </div>

📌 TL;DR

DeepShot is a machine learning-based NBA game predictor using advanced rolling stats (like EWMA) and real historical performance. It helps forecast matchups with visual insights and a clean interactive GUI.

💡 Why DeepShot Stands Out

Uses Exponentially Weighted Moving Averages (EWMA) to capture recent form and momentum
Visually highlights the key statistical differences between teams
Clean, real-time NiceGUI-powered web interface
Works locally across platforms (Windows, macOS, Linux)
Based entirely on free and public data

🔑 Key Features

Data-Driven Predictions – Powered by real NBA stats from Basketball Reference.
Real-Time Interface – Visualize upcoming matchups and model predictions with a sleek NiceGUI web frontend.
Weighted Stats Engine – Uses Exponentially Weighted Moving Averages (EWMA) to reflect recent performance trends.
Key Stat Highlighting – Automatically surfaces differences between teams to help you identify strengths and weaknesses fast.
Cross-Platform Support – Works smoothly on all major OSes.

⚡ Quickstart

git clone https://github.com/saccofrancesco/deepshot.git
cd deepshot
pip install -r requirements.txt
# Train model by running the notebook
# Open `model.ipynb` and run the cell to generate `deepshot.pkl`
python main.py  # Launches the NiceGUI web app

📬 Emailware: Share Your Thoughts

DeepShot is emailware. If it helps you or you find it interesting, I’d love to hear from you!

Send feedback to: francescosacco.github@gmail.com

🙏 Love DeepShot? Support It!

If this project helped you or you just think it’s cool:

⭐️ Star the repo
🧃 Buy me a coffee
💌 Send your thoughts or suggestions by email

🧠 Credits & Acknowledgements

DeepShot uses the following awesome libraries:

📎 You Might Also Like...

Check out more by the same author:

Supremebot: A user-friendly Supreme bot built with NiceGUI to help you buy Supreme items effortlessly.

📜 License

This project is licensed under the MIT License — feel free to use it in your own projects!

GitHub @saccofrancesco

Deepshot

Install / Use

README