nba-edge

Reusable Elo, win-probability, and Kelly-sizing primitives for NBA-style models.

Why This Repo Matters

This is the library layer of the sports ML stack:

• reusable rating logic instead of notebook snippets
• portable win-probability helpers for downstream services
• Kelly and implied-probability helpers for decision support
• small, dependency-light package design

Pairs well with nba-clv-dashboard for evaluation UI. Employer one-pager: case study.

What It Includes

• Elo updates
• logistic win probability
• Kelly fraction sizing
• American odds to implied probability

Install

pip install nba-edge

Example

from nba_edge import logistic_win_prob, update_elo, kelly_fraction, american_to_implied_prob

p = logistic_win_prob(rating_diff=120)
new_h, new_a = update_elo(1600, 1580, 1.0)
stake = kelly_fraction(p, -110, fraction=0.25)

Publish

pip install build twine
python -m build
twine upload dist/*

Non-goals

• No bundled NBA database or scrapers
• Not a tipster product
• Not a full modeling workflow by itself

CI

pytest + ruff on Python 3.10-3.12.

Related Repos

• sports-betting-ml: applied modeling demo
• nba-clv-dashboard: evaluation dashboard

License

MIT