deepshot
Deepshot is a machine learning model designed to predict NBA game outcomes using advanced team statistics and rolling averages. It combines historical performance trends with contextual game data to deliver highly accurate win predictions (71%)
Stars: 124
DeepShot is an advanced NBA game predictor that utilizes machine learning algorithms and historical data from Basketball Reference to forecast matchups. It stands out by using Exponentially Weighted Moving Averages (EWMA) to capture recent form and momentum, visually highlighting key statistical differences between teams, offering a clean NiceGUI-powered web interface, working locally across platforms, and being based entirely on free and public data.
README:
An advanced NBA game predictor powered by historical data from Basketball Reference, rolling statistics, and machine learning — built with NiceGUI for a seamless experience.
TL;DR • Key Features • Quickstart • Credits • License
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.
- 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
- 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.
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 appDeepShot is emailware. If it helps you or you find it interesting, I’d love to hear from you!
Send feedback to: [email protected]
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
DeepShot uses the following awesome libraries:
Check out more by the same author:
- Supremebot: A user-friendly Supreme bot built with NiceGUI to help you buy Supreme items effortlessly.
This project is licensed under the MIT License — feel free to use it in your own projects!
GitHub @saccofrancesco
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