thadhutch/sports-quant
End-to-end NFL data pipeline that scrapes PFF grades and Pro Football Reference game data, builds analysis-ready datasets, and trains ensemble XGBoost models with walk-forward backtesting
This project helps sports bettors and enthusiasts predict outcomes for March Madness and NFL games. It takes historical game data and team performance metrics, then generates predictions for March Madness brackets, including upset calls and survivor pool picks, and NFL over/under bets. It's designed for anyone looking for data-driven insights to improve their sports betting strategy or simply better predict game results.
Available on PyPI.
Use this if you want to make more informed predictions for March Madness tournament brackets or NFL over/under bets based on advanced statistical models.
Not ideal if you're looking for predictions for sports other than college basketball (March Madness) or NFL football, or if you prefer purely subjective, gut-feeling betting.
Stars
12
Forks
1
Language
Python
License
MIT
Category
Last pushed
Mar 18, 2026
Commits (30d)
0
Dependencies
16
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