Football Transfer Fee Predictor

What Are Football Transfers?

Football transfers involve players moving between clubs, either permanently or on loan. These deals are driven by negotiations and analytics—our platform predicts transfer values with remarkable accuracy.

ML Model

Python Based ML Model:

Our Machine Learning Model powered by Python predicts transfer prices with 80% accuracy. By analyzing player positions and stats, we estimate transfer fees close to real market values.

Football Transfer

Player Transfers:

By leveraging historical transfer data, our machine learning models identify emerging talents whose performance metrics indicate a higher market value than currently recognized.

Player Value

Player Transfer Value Determination:

Using tools like XGBoost or SHAP values, our model highlights key factors—goals, age, position, league experience—offering insights beyond human guesswork.

The dataset uses player stats from sources like Transfermarkt and FBref, split into position-specific models for forwards, midfielders, and defenders. Features include goals, assists, xG/xA, minutes played, defensive actions, and club level. This tailored approach improves market value predictions by reflecting each role’s unique contribution and context.

The project uses machine learning—Random Forest and XGBoost—to analyze player data and predict market value based on goals, assists, defensive actions, and club stature. It supports clubs in identifying transfer targets by considering performance, positional needs, and budgets, enabling smarter, data-driven decisions in the football transfer market.