Moneyball Scout ML

Identifying Undervalued Players with Machine Learning

Inspired by the Moneyball philosophy, this project applies statistical analysis to football (soccer). By leveraging machine learning, we identify undervalued players whose on-field performance metrics exceed their current market valuation.

Uncover Value

Analyze comprehensive player performance metrics to identify players whose market values do not reflect their true contributions.

Data-Driven Decisions

Utilize statistical models to support decision-making in football management, particularly in player acquisition and team building.

Tools & Technologies

Python

Pandas

Scikit-Learn

Data Sources

The project analyzes comprehensive metrics including:

✓ Performance (goals, assists, tackles)

✓ Physical attributes

✓ Market value data

✓ Historical trends

Links & Resources

Website

Temporarily unavailable

Offline

GitHub

Explore the repo

View Code →

Email

Questions?

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