Using Estimated Transfer Value (ETV)

The Estimated Transfer Value (ETV) is a data-driven metric that models the value a player is predicted to represent on the transfer market. It is computed using a model trained on historic transfers and uses features like the SciSkill, Potential, recent playing minutes, current league, contract status and player age.

Within the Recruitment Center, you can use this feature in multiple ways to evaluate a player's financial worth alongside all of the other pre-existing features such as Performance and Player Roles.

As shown below, you can view each player's Estimated Transfer Value (ETV) over time within the player profile.

The graph will show the ETV development from 2016 onwards or later, depending on the debut date of a player, and is updated monthly. You can also view the player's ETV value in the left-hand side bar on the player profile. Here it will be displayed alongside the Market value, so it allows you to compare both values.

Note: The shaded area in the ETV timeline represents the confidence interval (predicted ETV ±10%) that accounts for the model’s prediction error and represents the price bandwidth in which a players is very likely to be sold.

Within the filter section, you can filter by ETV to draw up a list of realistic transfer targets - as shown above with other filters (e.g. age) used alongside ETV.

You can additionally filter by the player's peak ETV since 2016 (or later depending on the player’s debut date). In the image below, we have filtered by players currently worth €10-20 mln but who have previously been valued above €40 mln.

This type of search allows us to find players who may be lower in value now but have previously been worth significantly more. As you can see from the search results, this type of search will tend to result in players who are past their prime but still have a lot to offer.

Adjusting the ETV values of the search to suit your club's needs is strongly advised for the most accurate results.

Our Player Valuation Model is a Machine Learning model that estimates the transfer fee value for a given player. It uses information on the player’s SciSkill, Potential, recent performance, contract duration, position and others to estimate an accurate transfer fee.

You can read more about the model's input here.