  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.
