jrbadiabo/Bet-on-Sibyl
Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)
This project helps sports bettors make more informed decisions by providing predictions for upcoming games across Football, Basketball, Baseball, Hockey, Soccer, and Tennis. It takes historical game statistics and team performance data to generate estimated results and compares these predictions against bookmaker odds. The end-user is a sports bettor looking for data-driven insights to guide their wagers.
276 stars. No commits in the last 6 months.
Use this if you bet on sports and want to leverage algorithmic predictions and performance tracking to inform your strategy.
Not ideal if you are looking for real-time betting advice or a tool for casual, low-stakes entertainment betting.
Stars
276
Forks
97
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 12, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/jrbadiabo/Bet-on-Sibyl"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
georgedouzas/sports-betting
Collection of sports betting AI tools.
roclark/sportsipy
A free sports API written for python
NBA-Betting/NBA_Betting
Using data analytics and machine learning to create a comprehensive and profitable system for...
saccofrancesco/deepshot
AI model predicting NBA game outcomes using advanced stats and trends
cmunch1/nba-prediction
A project to deploy an online app that predicts the win probability for each NBA game every day....