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 & 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!
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 & 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
66.45% is inside the edge of 66% to 72% range typical for almost any model. This is given by the fact that the most favored teams lose between 28% to 34% of the game they are supposed to win. So yeah the model predict the most favored team and sometimes was able to predict some winners that the odds weren't able to find but it's a pretty average accuracy ;) Considering the fact that 100% - 28% = 72% and 100% - 34% = 66% the model is inside that edge of predicting the obvious winner but, 1/3 of the times games' outcomes are very "random" / "unpredictable". Also, professional people who bet knowing and watching almost every game, play, knowing almost every news, trade, injury and external factors are accurate around 68% of the time. For me it's pretty amazing that a model knowing nothin could do this well sometimes and it was very fun creating and working on this for around 3 weeks ;)
The 66.45% it's pretty average and might also be sometimes misleading. The model lacks many features and has been developed for around 3 weeks now. I'm only 20 studying my first year in Software Engineering and the project was a lot of fun to create and to se it in action, sometimes being able to outperform the odds (not so consistently to be used to make and edge obviously as it is practically impossible)
Unfortunately I'm not hosting this anywhere. If you are used to programming in general the steps to reproduce the outcome are very easy. Maybe I'll try to deploy a Docker container for everyone to be able to try this easily ;)
Using the outcome of the last head-to-head matchup as a predictor can be misleading without proper context. The time elapsed since that game matters significantly—it could be from just a few games ago or over 20 games back. In that time, both teams may have undergone considerable changes in form, strategy, injuries, rotations, or momentum.
My model accounts for each team's evolution by incorporating trends from recent performances against all opponents, not just head-to-head matchups. This includes rolling averages and exponentially weighted metrics over the last 25 games, which help capture current form, streaks, and regressions.
As a result, the most recent head-to-head result only holds substantial predictive weight if it occurred recently and aligns with both teams’ current trajectories. Otherwise, it's treated as just one small piece of a much larger picture.
Hello, everyone! I'm excited to share my open-source project—Supreme Bot, a Python-based automation tool for Supreme product tracking and purchasing.
Tech Stack:
Python: The core language used for development.
Playwright: For fast and reliable browser automation.
NiceGUI: Provides an easy-to-use graphical interface for managing the bot.
Key Features:
Automates the Supreme website to monitor and buy products.
Scrapes data like item names, colors, styles, and prices.
Customizable and open to improvements—help me make it better!
Why Contribute?
I'm looking to expand the project and involve more skilled developers. If you are passionate about Python, web automation, or just want to help improve an open-source tool, this is a great opportunity for collaboration!
How You Can Help:
Contribute code: Fix bugs, add features, or refactor existing parts.
Provide feedback: Let me know your thoughts and suggestions!
Help grow the community: Share the project with others who might be interested.
Check out the repository: Supreme Bot GitHub
Looking forward to your contributions and feedback! Let’s make this project better together!