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AI · April 14, 2025

I made a machine learning model to predict 66.45% of NBA games

Introducing DeepShot: An NBA Game Prediction Model 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…

I made a machine learning model to predict 66.45% of NBA games launched on April 14, 2025 with 17 votes, #1,302 of 7,818 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 1 year on.

Visit github.com ↗Alternativestop 17% of April 2025

In plain words

DeepShot is an NBA game prediction tool that forecasts game outcomes with 66.45% accuracy using machine learning models trained on historical basketball data. It combines team rolling averages, exponentially weighted moving averages, and Elo ratings to analyze matchups, then displays predictions through a web interface built with NiceGUI. The tool is designed for sports fans, data analysts, and developers interested in sports prediction models and machine learning applications.

written from the facts on this page · September 2026

From the sources

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 (66.45%)

AI-powered NBA game outcome predictor that uses advanced team stats and trend-based features to forecast winners and track model performance - saccofrancesco/deepshot

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. 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! This project is licensed under the MIT License — feel free to use it in your ownfrom github.com

In the maker’s words, at launch

Introducing DeepShot: An NBA Game Prediction Model 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…

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