In this post, we take a closer look at how SciSports developed a Player Valuation Model generating Estimated Transfer Values (ETV) to support accurate transfer forecasts.
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.
For practice use cases of the values, see this article.
Learning the model
A machine learning model was trained on around 600,000 historical transfers to find patterns in the transfer fees paid for these transfers. For all these transfers we collect the SciSkill, Potential, recent performance, contract duration, age and position of the player at the moment of the transfer. In that way, the Machine Learning model can find patterns such as that players with a higher SciSkill tend to have a higher transfer fee, whereas players running out of their contract will have lower transfer fees.
Our model is capable of identifying even more complex patterns such as that for younger players their potential and recent performance is more important whereas for older players their SciSkill and contract duration can be more important. Our model was developed in close collaboration with experts from FootballTransfers who provided feedback in the development process such that we could keep improving our model.
For instance, in a first version of the model we noticed that young players such as Haaland were underestimated due to a lack of information on recent performance in our model. By adding this information we could overcome this issue.
Broadly the information taken into account when predicting transfer fees can be grouped as follows (kind of ordered in importance):
Information group | Short description | Influence |
Player current and potential skill | SciSkill, Potential, resistance factor at time of transfer | In general, the higher the SciSkill and/or Potential, the higher the predicted transfer fee. |
Player age | Age of player at time of transfer | In general, the lower the age, the higher the predicted transfer fee. |
Contract duration | Number of days left and indications of whether the contract ends within a year, 2 years or longer. | In general, the longer the contract duration, the higher the predicted transfer fee. |
Player position | Player offensive share is higher the more upfront the player plays | In general, the higher the offensive share, the higher the predicted transfer fee. This means that, in general, forwards get higher predicted transfer fees than goalkeepers and defenders. |
Player experience on different levels | All corrected for age: Number of matches and minutes played in career. Number of matches and minutes played in tier 1 competitions (top 5), tier 2 competitions and tier 3 competitions. Number of matches and minutes played in tier 1 tournaments (CL, EL, Copa Libertadores, etc). | In general, the more experience on a higher level, the higher the predicted transfer fee. |
Player recent playing minutes on different levels | In last 0.5 year, 1 year and 2 years: Number of matches and minutes played in entire career. Number of matches and minutes played in tier 1 competitions (top 5), tier 2 competitions and tier 3 competitions. Number of matches and minutes played in tier 1 tournaments (CL, EL, Copa Libertadores, etc). | In general, the more minutes played on a higher level more recently, the higher the predicted transfer fee. When a player didn’t play much in the previous season his fee will likely drop. |