kyleskom/NBA-Machine-Learning-Sports-Betting
NBA sports betting using machine learning observed · 2026-10-20
Health v2 · maintenance only
78/100
- Activity 94
- Release rhythm 45
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 74
- age_days: 2517
- days_rel: 285
- days_push: 38
- n_releases_24m: 4
Adoption not part of the score
1747 stars · 569 forks observed · 2026-10-20
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Python application that predicts NBA game winners and totals (over/under) using XGBoost and neural network models trained on historical team stats and sportsbook odds. It includes a data collection pipeline, model training scripts, a CLI for daily predictions with expected value and Kelly Criterion sizing, and a Flask web app for browsing outputs.
Use cases
- predict NBA game winners with machine learning
- predict NBA over/under totals
- calculate expected value for sports bets
- compute Kelly Criterion stake sizing
- scrape NBA team stats and sportsbook odds
- train models on historical NBA game data
When to choose
- you want ready-made ML models for NBA moneyline and totals predictions
- you want an end-to-end pipeline from data scraping to daily predictions
- you want expected value and Kelly Criterion bankroll guidance for NBA bets
When to avoid
- you need predictions for sports other than the NBA
- you need a guaranteed profitable betting system - model outputs are probabilistic estimates
- you need a production web service - the Flask app is a simple browsing interface
- you require a maintained library with a license - the repo has no license
Facets
application · maturity active
machine-learning deep-learning data-science etl web-scraping machine-learning data-science sports python cli cross-platform sports-betting nba xgboost neural-networks kelly-criterion odds-prediction flask gambling
1 source
- readme: https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting · fetched 2026-10-20 · 3ae6f19feceb
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| kyleskom/NBA-Machine-Learning-Sports-Betting | main | 78 |
For agents
markdown · JSON · MCP: product_card(name="kyleskom/NBA-Machine-Learning-Sports-Betting")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem