What should clubs monitor to predict future value of football players

TL;DR

This paper identifies key features that predict future football player market value using machine learning, aiding clubs in making smarter transfer decisions by analyzing both obvious and subtle player attributes.

Contribution

It introduces a methodology combining Lasso and Random Forest models to select relevant features for predicting football players' future market value.

Findings

Selected features include both obvious and subtle dependencies.

The methodology effectively ranks Golden Boy nominees.

Forecasts align well with actual market values.

Abstract

Huge amounts of money are invested every year by football clubs on transfers. For both growth and survival, it is crucial for recruiting departments to make smart choices when targeting players. Therefore, it is very important to identify the right parameters to monitor to predict market value. The following paper aims at determining the relevant features that successfully forecast future value for football players. Success is measured against their market value from TransferMarkt. To select prominent features, we use Lasso regressions and Random Forest algorithms. Some obvious variables are selected but we also observe some subtle dependencies between features and future market value. Finally, we rank the Golden Boy nominees using our forecasts and show our methodology can successfully compare football players based on their quality.