deepshot

I built DeepShot to predict NBA game outcomes using machine learning and advanced rolling statistics. The model scrapes historical data from Basketball Reference and uses Exponentially Weighted Moving Averages (EWMA) to capture recent team momentum and form. It's powered by XGBoost and achieves roughly 70% prediction accuracy. The web interface (built with NiceGUI) lets you visualize upcoming matchups, see key statistical differences between teams, and get real-time predictions. All data is free and public, and it runs locally across all platforms. The model training notebook is included if you want to retrain or experiment with the features. Would love feedback on the approach or suggestions for improving accuracy! Live demo: https://deepshot.onrender.com

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

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

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

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

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

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

Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?).

Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?).

Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?).

Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?).

Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?).

Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?). Thanks!

Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?). Thanks!

Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?). Thanks!