eric8395/baseball-analytics

Predicting MLB player salaries and team wins with machine learning regression models.

25
/ 100
Experimental

This project helps MLB team management and sports analysts evaluate player value and predict team wins. By analyzing historical baseball statistics, it forecasts player salaries and identifies which stats are most crucial for team success. It takes in player and team performance data from the last two decades and outputs predicted player salaries and insights into winning factors. Team general managers, scouts, and salary arbitrators would find this tool valuable for strategic roster construction and budget allocation.

No commits in the last 6 months.

Use this if you are an MLB team executive or analyst looking to make data-driven decisions on player contracts and roster optimization under budget constraints.

Not ideal if you are looking for real-time betting odds or predictions for individual game outcomes.

MLB-management sports-analytics salary-arbitration roster-construction player-valuation
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 8 / 25
Community 13 / 25

How are scores calculated?

Stars

8

Forks

2

Language

Jupyter Notebook

License

Last pushed

Aug 10, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/eric8395/baseball-analytics"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.