Category: Statistics

Statistics

FBM Finishing Points

FBM Finishing Points is a predictor of match results. Preliminary results show that FBM Finishing Points predict the correct outcome of a match in 60% of the cases (n=27, p<0.1). In 54% of the cases FBM Finishing Points is a better explanation of the end result than xG and in 50% of the cases FBM Finishing Points is a better explanation than Shots on Target. Which is remarkable as FBM Finishing Points is a predictor. So the FBM Finishing Points value is determined before the match is played. While xG and Shots on Target are descriptive stats that are determined after the match has been played by actually counting what has happened during the match. So FBM Finishing Points are as good as xG or Shots on Target, but FBM Finishing Points have the huge advantage of being available before the match. What this means is that clubs can use FBM Finishing Points primarily to determine what the best starting XI is. Secondarily, clubs can use FBM Finishing Points to strengthen their scouting.

How FBM Finishing Points work

FBM Finishing Points is the sum of all the probabilities that pairs of players on the pitch have to be able to assist and score. FBM Finishing Points are based on the FBM stats of individual players. So it’s a bottom up method that does not include any historical team data to determine the match result. Just the stats of all the players on the pitch.

What the Bayesian match model does to determine the FBM Finishing Points is as follows:

  1. Use all the FBM stats of the players of the home and away team as input in the model.
  2. Calculate the probabilities of successful passes between each pair of players on the pitch given their FBM passing game stat and taking into account distance between these two players and the defensive strength of the opposing players.
  3. Calculate the probabilities of scoring and assisting each other for each pair of players on the pitch, given the likelihood that passes actually reach these players & their FBM finishing stat and taking into account the distance between these players and the defensive strength of the opposing players.
  4. Sum all the probabilities in step (3) to determine the FBM Finishing Points for both teams.
  5. Determining the ratio between the FBM Finishing Points by dividing the lowest team score by the highest team score. The higher the ratio, the more likely the match will end in a draw. The lower the ratio, the more likely the team with the highest number of FBM Finishing Points will win the match.

How to make use of FBM Finishing Points

The primary use of FBM Finishing Points for clubs is match preparation. Before the starting XI of the next opponent is known, FBM Finishing Points is the ideal tool to do scenario planning. That means that a club can determine which starting XI has the best chance of winning in a variety of scenarios of different starting XIs for the next opponent.

Once the starting XI of the opponent is known, FBM Finishing Points can be used to quickly check whether the chosen starting XI is indeed the best possible starting XI.

If during match preparation it turns out that none of all the possible scenarios creates a sufficiently big chance of winning, then what is the matter is that the club doesn’t have enough good players to beat the next opponent. This can be due to the fact that the current players of the club have either too low FBM finishing stat, FBM passing game stat or FBM defending stat. If that is the case the scouts of the club have a clear assignment of finding players that solve this deficit.

How does FBM replacement values compare to TransferMarkt?

Although FBM replacement values are not meant to predict future transfer fees, it was noticed by many people that often the FBM replacement values of a player were much closer to the actual transfer fee than the valuation listed on TransferMarkt. For that reason we counted in how many cases FBM transfer values were closer to the actual transfer fee than the valuation on TransferMarkt.

A group of 1631 players were chosen by other people. This meant we had no influence on selecting which players we looked at. Of those 1631 players 159 had a transfer that would be counted. Transfers that followed loans and transfers in exotic leagues were excluded.

Of those 159 transfers in 108 (67.9%, p-value < 0.001) cases FBM transfer values were closer to the future transfer fee than the valuation at TransferMarkt. That makes FBM replacement values a more reliable source for future transfer fees than TransferMarkt. 

Nevertheless, that doesn’t mean that either TransferMarkt or FBM replacement values are a good predictor of future transfer fees. One can see that in the fact that the average transfer fee in this group was 13.25M and that TransferMarkt on average was off by 4.5M or 34%, while FBM replacement values were on average off by 4M or 30%. In 65 or 40% of the cases FBM transfer values came out exactly right, whereas TransferMarkt only had it exactly right in 7 or 4% of the cases. One can check all transfers here oneself. Please note that we make a distinction between win/win deals where the transfer fee is higher than the replacement value of the player for the selling club, but lower than the replacement value of the buying club on the one hand. And bad deals where either the selling club sells for less than the replacement value of the player for the selling club, or the buying club buys for more than the replacement value of the player for the buying club, or both.

Comparing FBM replacement values to TransferMarkt

Of course, FBM replacement values have one big advantage over the valuation at TransferMarkt and that is that FBM replacement values are actually a range rather than a single number. It is much easier to predict future transfer fees with a range than a single number. Nevertheless, we stand by this approach as in our philosophy it is a bad idea to think that a player has a single fixed value. Instead, we think that every player has a different value depending from which team’s perspective you look at the player. FBM replacement values mirror this idea that players are more valuable to some teams and less to others.

TransferMarkt also has advantages. Because TransferMarkt relies on rumor, knowledge of contracts and years left on contracts, they can and do quickly update as soon as news of a transfer breaks. For that reason, we have taken the previous valuation. This hardly makes a difference as the rumors are wrong in about half of the cases this applies to. FBM replacement values are 100% calculated. This makes it in principle more difficult to compete with human knowledge of transfers. Nevertheless, the 100% calculated FBM replacement values outperform TransferMarkt. We are currently not applying our FBM transfer model to calculate the probable stats of the player at the new club in these cases. Because of that it is highly likely that if we were to do so that the FBM replacement values would be even more precise so that the percentage where FBM replacement values lie closer to future transfer fees than the valuation at TransferMarkt would be even higher.

Validation of the FBM player statistics

Because the FBM player statistics play a big role in the calculation of the FBM replacement values, the fact that FBM replacement values are in 2 out of 3 cases closer to the actual actual transfer fee than TransferMarkt, also validates those FBM player statistics indirectly. For if the FBM player statistics would not be correct, then it would be highly unlikely that the FBM replacement values could be more accurate than TransferMarkt.

As a way to validate FBM player statistics and FBM replacement values even more, we predict that in cases where the actual transfer fee was at least twice as high as the FBM replacement value for the buying club that this player is going to disappoint in the coming season. This disappointment doesn’t have to be that the player is performing badly. It could well be that he performs adequately, but that given the amount that was paid for him, people feel that he is a let down.

The FBM Bayesian Transfer Model

All data is subjective, no matter how hard proponents of objectivity try to make you think differently. Because all data is subjective a player has different stats for different teams. To think that one set of data describes a player for every team is a simplification that many people are happy to make, because they feel football is too complex without simplifications. FBM takes a different approach and embraces complexity.

For that reason FBM player stats are always the stats for that player only playing for a specific team in a specific league. Most of the time this is of course his current team, but one can also easily look back to see how a player has done in different teams in different leagues.

What is harder to do, is to predict how a player will do in a different team. And it becomes a lot harder to predict how a player will do in a different team in a different league. The FBM transfer model solves this problem by using the power of Bayesian statistics. 

Basic principles of the FBM transfer model

The basic principles of the FBM transfer model are:

  • The stronger the league, the less time and space a player gets, the harder it becomes for a player to get good stats. In other words, if a player transfers to a stronger league one may expect that his stats will deteriorate. Of course, this also works the other way around. If a player moves to a weaker league, he will get more time and space and he will probably do better.
  • The stronger the team the player plays in, the better his teammates will be and the better his stats will be. In other words, if a player transfers to a stronger team his stats are likely to improve. And vice versa, if a player moves to a weaker team his stats will deteriorate.

The hardest part of predicting how a player will do is when a player transfers to a stronger league, but also to a stronger team. Or when he transfers to a weaker league, but also to a weaker team. Many bad decisions have been made by smaller clubs in weaker leagues that hiring a player in a stronger league would automatically strengthen the team. Unfortunately, there are many occasions where a player from a stronger league actually weakens the team.

Factors in the FBM transfer model

So factors that are used in the FBM transfer model are:

  • The FBM stats of the new player.
  • The FBM stats of the current player the new player is going to replace on the pitch or backup on the bench.
  • The FBM League Strength score of the league the new player is playing in.
  • The FBM League Strength score of the league the new team is playing in.
  • The FBM Team score of the team the new player is playing in.
  • The FBM Team score of the new team.

What the FBM transfer model does

The first step in the FBM transfer model is harmonizing the stats of the new player and the player he is going to replace or backup. This is done by using the ratio between the new league strength and the old league strength. And by using the ratio between the new team strength and the old team strength. This basically applies the two basic principles to the stats of the new player.

The second step is that the current player to be replaced or backuped by the new player is subtracted from his team, i.e. the team the new player is transferring to. Depending on his FBM player stats his contribution is taken out of the FBM club strength. After subtracting the current player from his team, we add the harmonized stats we found in the first step of the new player to the new team to see how the new team would do playing with the new player instead of their current player. The difference between playing with the current player and the new player is both expressed in a difference in FMB team strength and a plus or minus in the number of points a team is expected to get in the competition.

The third and final step is that the predicted stats of the new player playing for the new team are boosted a bit more if his presence strengthens the team. This reflects that if his teammates are going to play with a better teammate, then they are going to improve as well which then is reflected back onto the new player. This way the right new player can lift a whole squad. Of course the opposite also happens. So if the predicted stats for the new player weakens the team, then his teammates are also dragged down a bit which then reflects back on the new player whose stats deteriorate a bit again. That is why hiring the wrong player is not only a financial problem, but also a sporting problem bigger than just the bad player.

Based on the final predicted stats FBM also calculates what the replacement value of the player will be in one, two and three years. So the club will not only know whether the player is likely to be a good player for the team, but also whether the player is likely to net the team a million euro transfer fee or not, and if so in what time frame.

Validation

Unfortunately, there is currently not enough data to validate the FBM transfer model scientifically as until now only 21 transfers have been made where the FBM transfer model played a minor or major role. In total more than 100 FBM transfer reports have been created for clubs and agents, yet in most cases this has not resulted in a transfer. As most results are for clubs we are currently working for, we can only present the following table:

Predicted successPredicted failure
Actual success154
Actual failure11

There are two caveats:

  1. In some cases it is hard to measure success. For instance, we would consider the player a failure but the club a success and vice versa.
  2. The predicted failure, but actual success is quite high compared with the predicted failure and actual failure. This is in part due to the fact that when the FBM transfer report predicts failure clubs are less likely to hire the player so the chance of getting a predicted failure and an actual failure are way smaller than a predicted failure and an actual success. Also because in the latter case, the club has other sources (video scout and/or live scout) that disagree with the predicted failure. So the predicted failure but actual success category has a much bigger chance of happening than the predicted failure and actual failure category.

When you represent a professional club or a player agent and you want a free sample FBM transfer report, fill in the form below so we will contact you to discuss which transfer you would like to see. 

The FBM Creativity stat

Creativity is what cannot be measured by statistics. Hence the difficulty is creating a creativity stat. Nevertheless, we have developed a FBM Creativity stat and tested it. The FBM creativity stat has a p<0.0001 (n=47) to be correct according to the judgment of scouts. Even though the number of participants in the test is small, the very low p-value makes it a strong scientific proof that the FBM Creativity stat actually measures the creativity of players. Of course, we continue to research this stat and validate it even more.

There are limitations to the FBM Creativity stat. The main limitation is that it doesn’t work for every player. So far the model only works for about one in three players. Fortunately, it is crystal clear when the FBM Creativity stat can be applied to a player and when not. If the FBM Creativity stat can be applied to a player, it is highly likely to be correct.

What is creativity?

Creativity is a subjective judgment that per definition cannot be derived from football statistics. For some players the data scout, video scout and live scout all come to the same conclusion. If that shared conclusion is that the player is a good player, the risk of hiring that player is considerably less than if the shared conclusion is that the player is a bad player. 

Yet, now and then, there is disagreement between these three sources of judgments about the player. The more disagreement, the higher the risk in hiring the player. For creativity the most interesting situation is one where the data is negative about the player, but the video scout and the live scout are positive about the player. Somehow the video scout and the live scout see something in the player that is not reflected in the data. You can give any name you want to whatever the video scout and live scout are seeing and we call it creativity. Hence, the fact that creativity cannot be derived from football statistics per definition.

Nevertheless, it would be very helpful in these cases if there was an algorithm that would calculate the probability of a player being a creative player nevertheless. Although we can’t go into the details of how we did it, after years of studying this problem, the FBM Creativity stat passes the first tests successfully.

Two kinds of creative players

There are two kinds of creative players. The first creative player is a player who does more good than bad on top of being a creative player. That player is probably a superstar and his level of creativity, how wonderful it is in itself, is of lesser importance because his excellent data will carry him anyway. We call him a type I creative player.

Éverton Ribeiro is an example of a Type II creative player

The second creative player is a player who does more bad than good. We call him a type II creative player and we have a whole list of them. In this case the FBM Creativity stat becomes very important, because now you can calculate whether his creativity outweighs the bad football statistics for this specific player. While in general everybody would prefer a type I creative player for his team, the reality is that those players are rare and very expensive. So in reality most teams are unable to afford type I creative players. The question then becomes: how important is creativity that a team would accept lesser football statistics just to increase the level of creativity in the team?

Complexity and predictability

FBM uses a cybernetic model for football. That means that we see the team as a system that has to deal with the environment. The environment mostly exists out of the opposing team, but the referee, quality of the pitch, weather conditions and the crowds are for example also factors in the environment.

Within cybernetics, complexity is defined as the sum of all possible values for all variables. The number of variables during a match is enormous and almost all those variables have lots of different possible values. So in every match the sum of all those possibilities, i.e. the complexity, is higher than the total number of atoms in the whole universe. It’s an extremely large number. That is why playing football is so complex and that is – in part – what makes football so much fun.

If we were to simplify football to the extreme, one could say that every team has eleven variables (the players) and that each variable has only two values: the player is either attacking or defending. The complexity is then: 2 * 2 * 2 * 2 * 2 *2 *2 * 2 * 2 *2 * 2 = 2048. Now we replace a single player in the team with a creative player who has three different values: attacking, defending or doing something creatively. Now the complexity = 2 * 2 * 2 * 2 * 2 *2 *2 * 2 * 2 *2 * 3 = 3072. By adding a single creative player to the team, we have increased the complexity of the match for the opponent by 50%. Complexity is a nonlinear logarithmic function. That is why the complexity of football is so high. Due to the nonlinear logarithmic function, a single creative player can increase the complexity of the match substantially.

Which leads us to predictability. The lower the complexity of the environment, the easier it becomes to predict the behavior of the environment. The higher the complexity of the environment the harder it becomes to predict the behavior of the environment. While the opponent is part of the environment of your team, your team is part of the environment of the opposing team. By adding a single creative player to your team, the complexity of the environment of the opposing team rises considerably and the predictability of the behavior of your whole team becomes less.

Predictable teams find it much more difficult to win matches than unpredictable teams. If the team has a type II creative player, one who does more bad than good, the unpredictable team’s chances of winning have not increased because the team creates more and better opportunities, because that is what a type I creative player does, one who does more good than bad. No, playing with a type II creative player increases your chances of winning due to the fact that the complexity of the match is raised and the opposing team has much more difficulties to deal with this complexity which results in the opposing team making more mistakes. Mistakes that your team can profit from.

Again, we all prefer type I creative players over type II creative players. Nevertheless, most teams have to deal with type II creative players as they cannot afford type I creative players. In which case they have to find a fine balance between good football stats and creativity. Too little creativity and the team becomes too predictable and the chances of winning drops. Too much creativity and the play of the team becomes too bad and the chances of winning also drops. 

The FBM Creativity stat helps you determine this balance. When you want us to analyze your team to see which players have a FBM Creativity stat and if so how high or low their creativity is, please contact us through the form below:

We respect your email privacy

Three policies from the Football Behavior Management course that you can implement right away

Football Behavior Management is Organizational Behavior Management (OBM) for football clubs. Here are three smart policies that help strengthen your club immediately:

I) Start measuring your scouts, training staff and decision makers

Why only use statistics for your players, when statistics works as well – if not better – for scouts, training staff and decision makers.

The first step of FBM or OBM is to specify desired behavior. The number #1 desired behavior for your scouts is to find players that are highly likely to be able to contribute to the team. The same goes for the training staff for as far as they are involved in the recruitment process. The desired behavior for decision makers is to hire players that are highly likely to be able to contribute to the team. Often this means that decision makers have a secondary desired behavior and that is to listen to their scouts and stick to the recruiting rules as they have been decided upon beforehand.

To measure your scouts, training staff and decision makers, you ask them to subjectively grade potential players on a scale of 1% to 99% of how likely they are to be able to contribute to the team before they are actually recruited. 

You can use all of these predictions to actually calculate the risk of hiring this new player as well as the chance for a million euro or more transfer fee. That way you can actually see which player has the best risk/reward ratio. Yet, you can also use these risk analyses to make sure that all the combined small risks don’t make for one big risk. Because for smaller clubs the problem of ruin is very big in football. And even for big clubs the problem of ruin involves too much stress for the people involved. The problem of ruin is that if a small club hires the right players 95% of the time, they will be relegated once every twenty years. So clubs need a very high success rate to stay out of trouble. Formal risk management helps a lot.

At the end of the next season your team and you decide which new players have been successfully contributing to the team. Most of the time this is obvious. If there is a discussion one can look at predicted stats, minutes or his new replacement value. A successful player scores 100% and an unsuccessful player 0%. Then you can use Brier’s Rule to determine how well your team predicted these successes. Now you have the first data on who are good predictors in the club and who are less so.

This information is now fed back into the risk management by giving the good predictors more weight so that for the next season the risk analysis is improved even if all the same people are still working at the club. Keep doing this and the risks go down, the rewards go up and the problem of ruin becomes smaller and smaller.

II) Create a Viable Systems Model of your club

The Viable System Model (VSM) is a cybernetic model that models any organization. Any organization that exists for more than five years follows the general structure of the VSM model. Yet, most of the time these organizational structures are organically grown rather than thought out and structured by design. That means that at best they are inefficient and at worst that they are detrimental to the health of the organization.

The VSM for most clubs is quite easy to model as they are generally organized along the same lines. Most importantly, the VSM model structures who can command who. By using the VSM model you can make it absolutely clear what the relationship and balance between the manager of the first team and the technical or sporting director is. The VSM doesn’t prescribe what to do. The VSM only shows what the best implementation is for your choices. 

Finally, cybernetics teaches us that any regulator of a system is only as good as the model he has of that system. Good regulators have good models and bad regulators have bad models. This is why clubs spend so much time looking for a good manager or a good technical director. They are actually searching for a manager and a technical director with a good model. Do these good managers and good technical directors have an explicit model? Seldom of course. The model is inside their brain. That is what makes good managers and good technical directors so valuable.

By introducing the VSM in your club, you can make these unconscious models explicit so that not only the rest of the club can learn from them, but that you can actually optimize them and use them long after the manager or the technical director has left the club. In other words: creating the VSM model of your club actually enriches the club.

III) Hire one player less

On average, clubs hire six new players each season. Of those six players, two players tend to be unsuccessful, again on average. By hiring one player less and spending his salary and transfer fee, if any, on the scouting and recruiting department, chances are that they suddenly have a much bigger budget than before. As it seems that for most clubs the scouting and recruiting department has too small a budget. At the same time the scouting and recruiting department has the potential to make the club the most money.

This situation of too small a budget for scouting and recruiting seems irrational, but FBM and OBM explain when it is still a rational decision by the decision makers to spend as little as possible on scouting and recruiting. They do this, often unconsciously of course, because they already know that they are not going to listen to their scouts and recruiters. That is why actually listening to your scouts and staff is such an important desired behavior for decision makers. That is why measuring scouts, staff AND decision makers how well they predict is so important. That is why it is important to have a Viable System Model of the club so decision makers understand better what makes the club viable.

Use risk management and risk analysis to determine which player is most likely to fail at the club and refuse to hire him nor any other player. Instead be satisfied with the players you did recruit and spend the money of that one player on recruitment and scouting so that the next time you hire even better players with less risks and bigger chances for big rewards while at the same time keep hiring one player less each and every season. This policy will increase the probability of steering the club towards greater heights while at the same time reducing the probability of ruin.

These are three examples of what is being taught at the Football Behavior Management course we deliver for the VU-university of Amsterdam or in house for clubs. For more info, feel free to connect with us for more information and an introduction or presentation. To connect, please fill in the form below:

We respect your email privacy

How to read a FBM chart

Maybe you have come across a FBM chart like the following on Twitter and you are curious how to read these charts:

What you see is the answer to the following three questions:

  1. What is the probability that this player is able to contribute to the finishing of his current team?
  2. What is the probability that this player is able to contribute to the defending of his current team?
  3. What is the probability that this player is able to contribute to the passing game of his current team?

These probabilities are predictive and hold for the next upcoming game. It is important to note that almost all statistics in football are backward looking and descriptive. As useful as that can be, descriptive statistics is way less useful than predictive statistics like these FBM stats. After the game these FBM probabilities are updated using Bayes Theorem. For reliable players these probabilities are consistent over time. For more unreliable players they fluctuate more. So you can use FBM statistics to determine how reliable a player is.

Finishing consists of scoring goals, giving assists and shooting on target. The probability of being able to contribute to the finishing of the current team decreases due to shots off target.

Defending consists of all actions of the player and results where the player has a contribution to said result while the opposing team is in possession of the ball. The most positive result is of course gaining possession of the ball.  The probability of being able to contribute to the defending of the current team decreases due to the opposing team getting significantly closer to the goal, fouls being made or goals scored against the team.

Passing game consists of all actions of the player and results where the player has a contribution to said result while the team is in possession of the ball. This includes actions without the ball like drawing out defenders or occupying space, progressive passing, packing and the pre-assist. The probability of being able to contribute to the defending of the current team decreases due to losing possession of the ball.

Please note that these FBM stats are for playing in their current specific team in their current specific league. We have a Bayesian transfer model to transfer players virtually to different teams and different league. All these probabilities are then adjusted for the new team and in case of a new league also for the new league.

The distribution of probability

The graphs you see are the Poisson distribution of the underlying FBM stats for finishing, defending and passing game. Whenever you see a football statistic given as a single number be very suspicious. Reality is too complex to be captured in numbers, even if there are a whole bunch of them. In fact, the more different statistics are used, the less valuable the information becomes, because the more data you have the more you can prove. Yet, the more you can prove, the more what you prove is confirmation bias as you are going to prove what you already think you know. That is the reason why statistics should now and then shock you. Because if statistics doesn’t shock, the chance is that you use statistics to confirm your biases.

So rather than present players in single numbers, we present players as a Poisson distribution. The distribution gives you the area where the player’s probabilities will lie after the next match. Of course, given that each match only slightly moves these probabilities, if they move at all, in practice these probabilities hold for the whole season or to whenever a major change occurs, like for instance an injury, a new manager or a new team.

A new team is important, because FBM probabilities are always for a player playing for a specific team in a specific league. As soon as the player moves to a different team or even a different league, these probabilities change. We have a Bayesian transfer model that calculates how these probabilities change whenever you move a player from one team to another. Most of what we do is help clubs understand how likely it is that a potential player they like to hire is going to do well in their team.

How to read these distributions

There is a very simple rule to reading these charts:

The more to the right, the better. Ignore the peaks.

Somehow our attention is being drawn to the peaks, but the peaks are a mathematical artifact of the Poisson distribution. You could say that the Poisson distribution tries to distribute 100 points around the average of the statistics. The less space the Poisson distribution has to achieve this, the higher the peak. But the less space means that the probabilities used are very low. Hence the rule to ignore the peaks and just look for what is most to the right. If you really want to know what the vertical values are: the vertical values are the probability of single column in the graph.

Graphs may overlap, so for instance in our example of Oscar Fraulo, he both maxed out on finishing and passing game probabilities and so they overlap turning the graph into some greenish blueish color to indicate that both the green and the blue chart overlap.

If we compare two players in a chart the overlapping area is quite important. Because then the overlapping area is actually the chance that the lesser player will do as good as the better player or even better! So if the graphs of two players overlap a lot, the lesser player has a decent chance of outperforming the better player in the future. Nevertheless, the better player still has the biggest chance of outperforming the lesser player.

When you want to see how one of your favorite players looks in FBM stats, please let us know on Twitter through a Tweet or a DM. Or fill in the form below to request a free sample report:

We respect your email privacy

What is FBM replacement value?

With FBM replacement value we calculate what a club is to be expected to pay minimally to replace one of their players. This calculation is based on the following stats:

  1. The FBM players stats. There are four FBM players stats: 
    1. The probability to be able to contribute to the team overall.
    2. The probability to be able to contribute to the finishing of the team.
    3. The probability to be able to contribute to the passing game of the team.
    4. The probability to be able to contribute to the defense of the team.
  2. The historical transfer fees actually paid for the position of the player in the current league.
  3. The rank of the team in the table.
  4. The player’s age.
  5. The player’s length.
  6. The player’s international status.

Replacement value calculates the amount of money a club probably needs to spend to get a replacement player coming to the club. That means that replacement value is more about the new player coming to the club then the player leaving the club. So replacement value is not the most likely transfer fee, but can be used to determine a fair transfer fee by the selling or buying club, or both. That is the reason why replacement fees at some clubs are so much lower than expected transfer fees. These are smart clubs that hire new players for low transfer fees and let them go for high transfer fees. So for example Jurrien Timber of Ajax is expected to leave the club for a transfer fee of thirty million euro. Yet, his replacement value is “only” ten million euro. Yet, this is the amount Ajax needs to spend to get a player back who will produce the same stats as Jurrien Timber.

The way the calculation works is that we start with the perfect player. The FBM player stats are created by using Bayesian statistics. The highest possible probability to be able to contribute is 100%. That means that the perfect player has 100% probability to be able to contribute to the team overall, 100% probability to be able to contribute the finishing of the team, 100% probability to be able to contribute to the passing game of the team, 100% probability to be able to contribute to the defense of the team. Furthermore, the perfect player also has the perfect age and perfect length based on the length and age that have historically gotten the highest transfer fees. The perfect player also plays for the #1 ranked team in the league and he plays for the national team.

Such a player has never yet existed. Even Messi, who scores 100% probability to contribute to the team overall, in finishing and in passing game for most of his career, had a low probability of being able to contribute to the defense of the team.

Nevertheless, even though the perfect player has never existed, we take it that if he did exist, a club should have paid the all time highest transfer fee for that particular position in that particular league for him. In other words, the all time transfer fee actually being paid is to be taken to be paid for the perfect player even if in reality it was not. We don’t want to extrapolate based on the highest fee ever paid, because that fee might be a market top. 

The next step is to check how long ago this top transfer fee was paid and what has happened to the transfer fees paid for that particular position in that particular league. We do this because the market might have topped and we have to take into account that transfer fees in the future go down. This gives us the top transfer fee for the perfect player in the current season.

Finally we calculate how different the actual player is from the perfect player and this gives us the ratio between the actual player and the perfect player. The replacement value is calculated using this ratio and the top transfer fee for the perfect player for that particular position in that particular league.

How replacement value works for clubs and agents

Replacement value is not the most likely transfer fee. The transfer fee is whatever clubs and agents can get away with. Replacement value helps clubs and agents in their negotiations though. Because if club A pays club B a transfer fee that is above the replacement value of the player for club A, then club A is being weakened as the overpay and now have less money available for other transfers. The same goes the other way: if club B sells a player for less than the replacement value of the player for club B, club B is being weakened because they got too little money for the player and have less money left to spend on a replacement or for other players.

Fortunately, this means that it is possible that a transfer is a win/win for both clubs. This is due to the fact that replacement values are specific for each and every club. Player valuations like those on TransferMarkt suggest that the player has an intrinsic worth. This is a statistical mistake. The player only has a value to a specific club. If no club wants a certain player, no matter how high that player is valued, his future transfer fee is zero. So player valuation always needs to be made in the light of the club he is currently playing for and the club he is going to play for, including possible differences in leagues and the rank in the league of both clubs. That means that it is possible, even quite common, that the transfer fee is higher than the replacement value of the player for the club selling the player and at the same time lower than the replacement value of the player for the club buying the player. Both clubs are strengthened by the deal and it is a win/win situation.

Of course, in more cases one club profits at the expense of another club. In that case in very real terms (i.e. money) one club is getting stronger and the other club is getting weaker. So it is very important for clubs to keep checking whether their deals are favorable or not, in the light of the replacement value of the player.

To give an example of how this worked out in practice, we advised one of the consultants working for Willem II in regard to the transfer of Fran Sol to Dinamo Kiev. According to TransferMarkt the value of Fran Sol was 6 million euro at the time. Willem II was trying to get this amount from Dinamo Kiev, but Dinamo Kiev was unwilling to pay this amount. In our calculations Fran Sol had a replacement value of only 2 million for Willem II and 4 million for Dinamo Kiev. So any amount between 2 million and 4 million would be a win/win for both clubs. The final deal was for 3.5 million euro.

Given that the player’s age is a factor in the calculation of the replacement value, one can easily calculate the replacement value of a player in the future. This way clubs and agents can see whether a potential transfer has the chance of being profitable if the player leaves his new club after one or two seasons. That is how we were able to predict that Dalmau, who came to Heracles as a free agent, would have a replacement value of 1.75 million euro one season later. Indeed, after one season playing for Heracles he was transferred to FC Utrecht for 700.000 euro plus the transfer of Dessers to Heracles. Dessers at the time was valued at 1 million euro, bringing the value of the complete deal to 1.7 million euro, very close to the replacement value of 1.75 million euro we predicted for Heracles. Dessers left Heracles also after playing there for just one season for a 4 million euro transfer fee.

Why there, sometimes, is a big gap between FBM replacement value and transfer fees valuations

Take the interesting case of Noussair Mazraoui

His replacement value is 6.5 million euro playing for Ajax in the Eredivisie. Yet, his valuation at TransferMarkt is 20 million euro. Noussair Mazraoui is leaving as a free agent, so we will never know what the transfer fee would have been, but it doesn’t seem that Ajax has gotten a very attractive offer for Noussair Mazraoui. 

Nevertheless, there is a big gap between 6.5 million and 20 million. This gap is a great example of why replacement value is not the same as transfer fees. The FBM replacement value is a measure for Ajax to limit their spending on a replacement for Noussair Mazraoui to 6.5 million euro. We might be mistaken, but we think it is highly unlikely that Ajax would spend more than 6.5 million euro. Cases like these validate the replacement value model.

Some superstar players have extremely high valuation on sites like TransferMarkt. Mainly because they play in one of the best leagues for some of the best clubs, often getting very far in the Champions League. These high valuation are more a token of appreciation than a likely transfer fee as most of these superstar players will stay with their current club until (almost) the end of their career. Without the prospect of a transfer, the real value of those players is much closer to zero than the extremely high number quoted everywhere. So once more clubs find more use in the replacement value of those players as at some point they have to replace their retiring superstars with other players.

Even though FBM replacement values are not meant to predict future transfer fees, in 2 out of 3 cases FBM replacement values are closer to the actual transfer fees than the valuation at TransferMarkt.

Undervalued players

Even though replacement value is really about the next player coming in to replace the current player, if the replacement value is much higher than the actual valuation, then one still can say that they player is undervalued. The reason being that if the club were to sell their current player for the valuation fee and then had to hire a new player for the much higher replacement value, they would lose a lot of money. Hence players whose replacement value is much higher than their public valuation are undervalued. Here is a list of some of the undervalued players we have found.

Loyalty, recruitment, brain types and the ABC-model

Player agents often complain about the lack of loyalty of the players they have signed. They assume that loyalty is an inherent trait some players have and others don’t. Of course, it is painful to see one of your biggest talents sign with a different agency just before their big breakthrough. In most cases leaving the agency has little to do with loyalty and more to do with the player’s brain type and the ABC-model. In this article I will describe what an agent can do to breed loyalty into his players.

First of all, the whole idea of people having traits is a backward idea. In reality people acquire knowledge through associative learning and skills through instrumental learning. In terms of football: associative learning gives you game intelligence and instrumental learning gives you technique. How do we know whether a player has game intelligence or technique? We see that in how the player behaves. For we cannot look into the soul of the player.

The behavioral patterns of a player are, for the most part, acquired through instrumental learning. Through instrumental learning the brain creates probabilistic relationships between the behavior and what this behavior gets you. The brain of the star player has learned in extreme detail how to shoot the ball in order to get the result the player wants: a goal. Instrumental learning works according to the ABC-model. In this model A stands for Antecedent which is everything that happens before the behavior or is necessary to make the behavior possible. B stands for Behavior, the desired or undesired behavior you are targeting. C stands for Consequence which is everything that happens after the behavior. There is overwhelming evidence that Consequences have a much, much bigger influence on our future behavior than Antecedents. Nevertheless, in most cases we continue to try to influence people through Antecedents rather than through Consequences.

‘So when it comes to loyalty, there isn’t an inborn trait that some players have and others don’t. Instead, there is the history of all the Consequences that the agent has given in response to the behavior of the player. To understand this you first have to specify the desired behavior. To do this we have to take MARCO into account. Behavior is only behavior if it is:

  • Measurable. If you can’t measure it, it ain’t behavior.
  • Active. If a dead person can do it, it ain’t behavior.
  • Reliable. If you can’t measure it reliably, i.e. different people come up with completely different measurements, it ain’t behavior.
  • Controlled. If it is not under the control of the actor, it ain’t behavior.
  • Observed. If it is impossible to observe, it ain’t behavior.

As you can see: loyalty ain’t behavior. Loyalty can’t be measured, a dead person can be loyal, if you can’t measure it, you can’t measure it reliably, loyalty is not under the control of the actor and you can’t observe it directly. So we have to specify the behavior that makes us think that a player is loyal. Most agents would specify that behavior as the player not signing up with another agent. But here, the first mistake is made. Dead persons never sign contracts with other agents. So not signing a contract, ain’t behavior either. Instead the right specification is to honor the contract the player signed. Dead persons can’t honor contracts. You can measure how long the player is honoring the contract and you can measure this reliably. Honoring the contract is completely under the control of the player. And we can easily observe the player honoring the contract.

So the desired behavior is honoring the contract and the undesired behavior is signing with another agent. The ABC-model teaches us that players do more of what has been rewarded with positive consequences in the past; in the same way players do less of what has been punished or penalized in the past. A player breaking his contract and signing with another agency, doesn’t do so out of disloyalty, but because honoring the contract has not given him enough positive consequences. On top of that honoring the contract with the agency always has at least one negative consequence. For the fee that the player pays the agent, is experienced in the brain as a penalty. Players want money, so spending money is a negative consequence. It is the task of the agent to compensate for this negative consequence, by more positive consequences. At first the agent does this by promising the player more positive consequences. But these promises are Antecedents and have little impact on the future behavior of the player.

Only when the player really does get what he wants as a result of him honoring the contract, only then the player gets a positive consequence. So the ability to get the player signed with a big club for a high salary, is the most important job of the agent. Yet, this happens only every few years. That means that after the first signing the agent made possible, it will take a long time before the next big positive consequence will be there to reinforce the player’s brain to honor the contract. Furthermore, this future positive consequence is also uncertain. The player might get an injury that ends his career. Or it may turn out that he is less talented than thought before. Or just a case of bad luck. Research clearly shows that long term uncertain positive consequences have way less impact on the behavior of a player than short term certain consequences. Therefore the agent has to make sure that the player is rewarded short term with a high degree of certainty for honoring his contract. If an agent does this then the player will continue to honor his contract and everybody will think that he is a very loyal player. Whereas in fact it is the behavior of the agent rather than the player that makes the player appear to be loyal.

What kind of short term positive consequences are there for the agent to give to the player? In short the agent can choose between the following categories:

  1. Material rewards:
    1. Direct material rewards: food or drinks.
    2. Indirect material rewards: money or valuables.
  2. Social rewards:
    1. Attention. It is important that the agent regularly checks in with the player to ask how he is doing.
    2. Compliments. If the player achieves something on the pitch during a match, make sure he is complimented for it as soon as possible after the match.
    3. Status. An agent can create different classes of players within the agency so a player feels he is promoted within the agency as he develops. Just make sure that you set-up the program in such a way that there are only winners.
    4. Information. Many players love to have access to the statistics of how they did or video’s of their best actions.
    5. Opportunities to develop one self. Players not only want to become better at football, they also want to develop themselves mentally.
    6. Keep their social media up to date. Keeping their social media up to date has negative consequences for players as it takes time and energy. So often they love it if the agent takes care of it. Updating their social media accounts as soon as possible so the player sees his fans rewarded as soon as he comes off the pitch, is a positive consequence for most players. Also because this enhances their status.

As players most of the time get plenty of material rewards, the best choice for agents is to go for social rewards. The easiest way to discover what kind of rewards the player is looking for is by asking the player himself. This may seem obvious, yet it is the second mistake most people who use the ABC-model make. They fall into the pit called the Perception Error and assume they know what is a positive reward for the player. So ask your players, how they can be rewarded on top of everything they already get from the club. 

Brain types

The third mistake is disregarding brain types. In the same way that there are different body types, we also have different brain types. Your brain type determines your evolutionary behavioral patterns. These behavioral patterns determine:

  1. How you are motivated.
  2. How you deal with your emotions.
  3. How you learn.

Brain types determine in a large part how the Dopamine reward system in your brain works. Therefore, if you know someone’s brain type you can predict with a high probability how you can reward him with positive consequences. Here is the list of positive consequences for each brain type:

Type #1, the Perfectionist can be rewarded with control.

Type #2, the Helper can be rewarded with love and attention.

Type #3, the Successful Worker can be rewarded with material rewards and hopeless projects where he has a small chance of becoming the project’s hero.

Type #4, the Romantic can be rewarded with justice served.

Type #5, the Analyst can be rewarded with autonomy, personal freedom and being left alone.

Type #6, the Loyalist can be rewarded with safety.

Type #7, the Hedonist can be rewarded with new things to do and variation.

Type #8, the Boss can be rewarded with power.

Type #9, the Mediator can be rewarded with harmony.

As loyalty also is an evolutionary behavioral pattern, some brain types have special issues concerning loyalty as can be seen from this list:

Type #1, the Perfectionist has no special issues with loyalty. Yet, as Perfectionists feel that they must act in accord to the morals of the group, it helps if you make honoring your contract one of high principles endorsed by the whole group.

Type #2, the Helper has no special issues with loyalty. Yet their craving for love and attention is so high that if the agent fails to make compliments, give little presents and keep in touch with them, the agent risks being put in the so-called out group and that will lead to a parting of the ways.

Type #3, the Successful Worker has an issue with loyalty. Successful Workers are very loyal when they are relaxed. If they are stressed, they seek social stability. In both cases it is unlikely that they would break their contract. Unfortunately, when neither stressed, nor relaxed, they become reckless, antisocial and highly sensitive to material rewards and promises of material rewards. In that state, they can be easily poached by other agents.

Type #4, the Romantic has no issues with loyalty. In fact, if breaking the contract is seen as an injustice it is unlikely that the Romantic will break the contract. On the other hand, if the agent’s actions appear to be unjust toward the player, other players, clubs or people in general, they might very well break their contract even if it means a worse outcome for themselves.

Type #5, the Analyst has no issues with loyalty. If it is clear for the Analyst that he has lots of autonomy, personal freedom and is left alone, he will not risk losing this by signing with another agent.

Type #6, the Loyalist has issues with loyalty as the name implies. It will take quite some time and thorough research by the Loyalist before the Loyalist signs with an agent. Nevertheless, once they sign, they honor their contract. Not so much out of loyalty, but because they see too much risk in breaking the contract. Unfortunately, Loyalists are probably underrepresented in football as the game and the culture are not their thing.

Type #7, the Hedonist has no issues with loyalty. The one thing to watch out for is that if the Hedonist stresses he becomes quite sensitive to material rewards. Furthermore, there is the risk that he lacks a clear sense of morality. Meaning that if stressed, the Hedonist can easily be bribed, even illegally, to sign with another agent.

Type #8, the Boss has no issues with loyalty. In fact, the Boss likes to receive a clear manual from a higher power he respects. He then blindly follows the rules in the manual and will in fact enforce these rules with other players. So if there is a rule in the manual that states that you always will honor your contract he will do so and he will try to forcefully make other players within the agency comply with that rule as well.

Type #9, the Mediator has no issues with loyalty. In fact, the Mediator is likely to become dependent on the agent and would find it emotionally difficult to leave the agency. As most players have a type #9 brain, this is the common experience of agents. They mistake these dependency issues for loyalty and then complain that the other players lack these issues. 

So besides using the ABC-model to positively reinforce honoring the contract, it is also smart to take into consideration the brain type of each player.

Davy Klaassen to Ajax

Based on the Wyscout data for the 43 matches Klaassen played for Werder Bremen in season 19/20 & 20/21, he has:

  • 86% probability that he is able to contribute to the Werder Bremen overall,
  • 5% probability that he is able to contribute to the attack of Werder Bremen,
  • 97% probability that he is able to contribute to the defense of Werder Bremen,
  • 83% probability that he is able to contribute to the build up and transitioning of Werder Bremen.

Based on the Wyscout data for the 19/20 season Werder Bremen as a team had:

  • 31% probability that the team will win or draw a match,
  • 30% probability that the attack will score,
  • 43% probability that the defense will concede a goal (lower is better),
  • 44% probability that the build up and transitioning will create an opportunity.

Based on the Wyscout data for the 19/20 season the Bundesliga has a FBM League Strength score of 123 points. (91% correlation)

Based on the Wyscout data for the 19/20 season the Eredivisie has a FBM League Strength score of 114 points. (91% correlation)

Based on the Wyscout data for the 19/20 season Ajax has:

  • 87% probability that the team will win or draw a match,
  • 69% probability that the attack will score,
  • 28% probability that the defense will concede a goal (lower is better),
  • 53% probability that the build up and transitioning will create an opportunity.

Given that the League Strength of the Eredivisie is lower and that the club probabilities of Ajax are higher, it is a realistic idea to see Klaassen play for Ajax.

Based on the above data including minutes played, difference in league strength and difference in team strength, we calculate the following probabilities for Klaassen playing for Ajax.

  • 94% probability that he is able to contribute to Ajax overall,
  • 12% probability that he is able to contribute to the attack of Ajax,
  • 99% probability that he is able to contribute to the defense of Ajax,
  • 92% probability that he is able to contribute to the build up and transitioning of Ajax.

As you can see the performance of Klaassen will be very similar for Ajax as it was in the 19/20 season for Werder Bremen.

If we were to substitute Van de Beek for Klaassen Ajax would get the following probabilities:

  • 92% probability that the team will win a match (+6%),
  • 70% probability that the attack will score (+1%),
  • 26% probability that the defense will concede a goal (lower is better) (-2%),
  • 53% probability that the build up and transitioning will create an opportunity (+0%).

This would result in 5 additional points in the table.

To conclude: Ajax is slightly better off with Klaassen.

Shadow team born this century anonymized

To track how we are doing in finding talent at a relative early age (20 years or younger, from 2022 on players need to be born 2002 or later), we publish our shadow team anonymized and keep track of how these players are doing. As soon as any of these players transfer to another club or become a household name, we update this list and reveal the name. Or if they turn 25 in case the did not break through. Valuation at date is the price where would virtually buy the player. That way we can see how much profit would make virtually.

We paper trade the players as if we bought them for the valuation at data. The we sell the players when they reach the age of 25 or when the make a major transfer. That way you can see how well we do.

So far we have spent 136M euro and earned 56M euro for a loss of -80M euro, but the valuation of these players has gone up to 493M (+262%).

Of the 291 teenagers on the list 162 have increased in valuation (55.6%), 21 have decreased in valuation (7.2%) and 107 have no change in valuation mainly due to still being too young. (37.2%)

PlayedIDDate first in shadow teamValuation at dateTransfer/Currently valuedPositionFBM scoreBorn/playerContract/Sold
563929-5-20190.20.5AM5.9520002022
644322-4-201903.5LW620032023
644713-1-202000.55DM620022023
64612-4-2018126 million transfer to RennesRW8.66Jérémy Doku26M
705916-2-2020112RB5.920002024
73835-9-20200.50.45LW7.8220002023
74062-3-20200.70.3GK6.2420002023
804422-4-20190.50.85CM6.8420022022
804619-4-20190.250.6CM7.3920002022
810727-1-20200.751CF7.2920002025
81303-5-20190.10.6DM7.8920002022
814417-8-201900.3LW6.520012022
814517-8-20190.50.5RB6.8520012022
823021-10-201933LB5.9720002024
87398-6-20190.20.8CB6.7720022023
87418-6-201902.5DM620022023
87528-6-20190.0750.025RW620012023
87588-6-20190.10.15RW720012022
88151-11-20180.37.5RW920012024
902222-4-201908LB620032025
902313-1-202000.5LB720022023
902427-9-20210.20.25DM620022024
90257-9-201902.4CM5.520032022
97778-6-20190.050.3RB820012022
985010-12-20200.12RW6.820002022
986310-12-20200.32.2RB7.620002024
1009328-5-20192.56CB7.420002024
1034012-12-201800.5RW820002023
1035219-8-202006LB6.2520012024
109571-8-20190.40.3AM7.1720002022
110465-3-20200.40.45DM6.8820002022
1112017-8-201900.15CM820012022
1112127-1-202000.9CM6.1320012023
1125817-9-20190.051.5CB6.052002unknown
1127024-12-202021.2CB6.320022024
115137-3-20201.14RB7.0620002026
1165720-12-20190.10.7CB6.4720002023
1173230-10-20192.7530 million transferAM7.22Odilon Kossounou30M
118414-12-2019012LB7.4220012024
1206325-1-20200.10.35CM7.7620002024
1209412-9-20200.050.6CM7.1120002022
121033-3-20200.0255CB7.3120002022
122305-6-20203.55CF8.220022023
1228918-5-202010.6CF5.9920022024
1234326-7-20202.33AM620012025
1241422-6-20200.80.6RW5.620012024
125256-4-202110.9CF620022025
1261415-7-202000.8CB6.1620022023
126652-7-20200.410DM7.520012024
1272121-7-20200.10.15LW6.520002023
1274922-7-20200.40.3RW62001unknown
1279023-7-20202.56LW5.720012025
1279325-7-20200.30.3CF720012023
1279425-7-20200.51.5CF6.520022023
1279525-7-20200.151CB620002022
1279625-7-20200.85CB620012022
1280426-7-20200.10.9CB5.820032022
1281026-7-20200.050.4RW5.720042023
128551-8-20200.20.25CF5.920022022
128562-8-202001CM5.620032022
128695-8-202001.5AM720002022
128745-8-20200.215LW6.320012026
128899-8-20200.91CB620002024
1290210-8-20200.10.15LW6.620032022
1290310-8-20200.152.5LW7.520032025
1291011-8-20200.250.4CM8.220012022
1292212-8-20200.150.1CF7.120012023
1292712-8-20200.21.7CF5.920042022
1292913-8-20200.54.5RW620012023
1297519-8-202003CM5.520012023
1298322-8-20200.720CM6.1220022024
1302526-8-202000AM6.320052023
1304414-11-20210.30.5CM6.320022023
130755-9-20200.30.3DM7.1320002023
130968-9-20200.22CB5.6820002023
1312613-12-20200.51LB620012023
1315915-9-20200.0515CB6.4320022025
1316215-9-20200.43CB7.5620002023
132816-10-20200.050.275CB6.2220002022
1329914-10-20201.26DM7.520012024
1330018-10-202031.6CB5.720032023
1330122-10-20200.21.5CB5.820002025
1334428-10-202032.5LW5.9620012023
133563-11-20200.11.5CM7.520022025
133573-11-20200.10.35CF720022025
1344210-12-20200.20.6CM820002023
134718-12-202014CF5.520022022
134729-12-202008CF6.620012023
1349110-12-20200.30.1CM620012022
135346-1-202100CM62004Unknown
135356-1-202100CM820052022
135366-1-202100.15AM82005Unknown
135376-1-202100CF720052023
135396-1-202100CF62004Unknown
135493-1-20211.81AM620022024
135536-1-20210.7250.625CF620012023
135606-1-202100CF62004Unknown
135616-1-202100.2CF720042022
135626-1-202100CF720042022
135636-1-202100AM72005Unknown
1365211-1-202100GK6.220022022
1367714-2-202100RW7.520022022
1367914-2-202100LW6.920022024
1368417-2-202100CF6.32002Unknown
1369419-2-202101CF6.420042024
1369519-2-202100.3AM6.720042022
1369619-2-202100RW72004Unknown
1369919-2-202100LW5.820042022
1370019-2-202100AM6.92004Unknown
1370119-2-202100AM6.62004Unknown
1370220-2-202100CF6.82003Without club
1370324-2-202100.35RB7.920022026
1370625-2-20210.52.5GK620012024
1370827-2-202117CF5.720032023
137224-3-2021015CM620022025
1373415-3-20210.810RW720022023
1373516-3-20210.71.4CF620012024
1373616-3-202100.35CM7.520032022
1374822-3-202123DM6.520022022
1376924-3-202133.5CB5.920022025
1377125-3-202135CB7.120012024
1380630-3-20210.80.9CB72002Unknown
1381130-3-20210.10.3CB6.820012025
1383331-3-20210.58AM720032022
138428-4-202101.5LB5.820022024
138448-4-202102CM6.720022024
1387016-5-20213.515CM6.320032024
1387117-5-20210.0750.3CB8.520012022
1387217-5-2021214RW720032022
1399719-2-202100CF62004Unknown
1399819-2-202100CF5.720042023
1416824-9-20210.050.55CM5.920022022
1425913-7-202100CM72006Unknown
144543-8-20210.11RW720022025
144583-8-20210.10.3LW720022025
1450018-8-20211.55AM620022024
1450118-8-2021210CB7.520012024
1458314-9-202100.2LB6.620032022
1460116-9-202100.3AM620012022
1462124-9-202100.7CB620042023
1463427-9-202100.25CB5.520032022
1463527-9-20210.30.45LB620032024
1468612-10-202100CM5.620022022
1469413-10-202100.75CB5.82001Unknown
1471019-10-202100CB9.120042023
1471219-10-202100.15CB8.22003Unknown
1471419-10-20210.10.25CM6.520032022
1471619-10-202100.1LW6.320032022
1471819-10-202100CF5.920052023
1471919-10-202100.075GK6.32003Unknown
1472019-10-20210.10.075CB62002Without club
1472119-10-202100.2CM7.22003Unknown
147426-11-202114LW82002Unknown
147466-11-202100.1RB7.52002Unknown
1476411-11-20211.23.5CB620022024
1477011-11-20210.65GK720022024
1477312-11-2021312CM720022024
1478814-11-20210.050.175AM6.820012023
1479014-11-20210.40.35CB6.720012023
1479114-11-20210.20.4RW5.820022024
1479424-11-202100LW62005Unknown
1479524-11-202100.25RB620032023
1479624-11-20210.20.3CB5.520012022
1479724-11-202100CB62005Unknown
1479825-11-20210.750.6CB620022025
1479913-12-202100RB720052023
1480114-12-202100.1CB720042022
1480224-12-202111CB620012026
1480324-12-20210.10.4CB720022023
1480424-12-20210.50.4CB6.520012024
148054-1-202200DM820042023
148064-1-2022210CB920032023
148074-1-202200CB62003Unknown
148085-1-202211.5CB72002Unknown
148095-1-20220.30.35CB620032023
148105-1-20221.31.3CB720032022
148115-1-20220.350.5CB620032025
148125-1-20220.42DM620022024
148135-1-202200.3DM620042022
148146-1-20221.20.8GK620032023
148156-1-20220.80.8GK720022023
148166-1-202202LB820032023
148176-1-202200.025RB720032022
149193-2-20220.50.5CB6.520022023
149288-2-202200AM6.120042023
149298-2-202200CF6.12006Unknown
149449-2-202200RB6.720032024
149539-2-20220.30.3CF6.420042023
149479-2-202200.15RW5.920032022
1480924-2-20220.30.35CB620032023
150122-3-202200.2GK6.920032022
150152-3-202200.1CB62003Unknown
150282-3-202200.8CB5.820032023
150577-3-202200AM620062022
1507825-3-202200CF5.520042025
1507925-3-20220.40.4RW620032023
150867-4-202200CM620052024
1508710-4-202200CM5.520062022
1508810-4-202200LB620052022
1452015-4-20220.54.5RW5.520032025
1509018-4-202200CF82006Unknown
1509128-4-202200DM720062024
151197-5-202200CM620042024
151208-5-20220.51CB5.520032023
151219-5-20220.71.3AM62003Unknown
1512210-5-202212CB620052024
1512310-5-202222CM6.520052024
1512411-5-20220.20.45CB720022026
1481211-5-202222DM620022024
1512512-5-202200.05CM5.520042023
1512614-5-20220.20.4CB720042024
1512715-5-202200LW620052024
1512815-5-202223CF720032025
1512915-5-202200AM720052023
1258316-5-20220.93RW620022023
1513121-5-2022016CF620062024
1513222-5-202200CB620042024
1513323-5-202229AM720052025
1513430-5-20220.10.1CF72004Unknown
1513531-5-20221.21.2CB720022024
151361-6-20220.751AM620052025
151371-6-20222.85CB620032026
151382-6-20220.30.6CB5.520022024
123932-6-20220.2750.75CB62004Unknown
1513911-6-20221.21.2LB620022025
1514012-6-202204AM720042027
1514113-6-202205DM720042023
1514216-6-20220.350.35CM720032023
1514317-6-20220.5750.575CB72003Unknown
1514417-6-202201CF5.520032026
1514517-6-20220.250.25DM5.520032025
1514617-6-20220.51.5RW5.52003Unknown
1514718-6-20220.41AM720022024
1514928-6-20220.350.35CF720022024
87575-7-20220.20.2LW820032026
151505-7-202200LB820052023
151515-7-202200DM62005Unknown
1515211-7-202200CF7.52004Unknown
1515312-7-20220.70.7CF720022024
1515413-7-202200CF620052023
1515513-7-20220.40.4CF620052025
1515715-7-20220.350.35CB5.52002Unknown
1515817-7-20220.0750.075RB62003Unknown
1515919-7-20221.21.2CB620032024
1516019-7-20220.60.6CM820032025
1518023-7-202200.5CB62005Unknown
1518123-7-20220.450.6RB720032023
1522531-7-202222LW720042023
152262-8-202222LW720032023
152273-8-202211CM620022025
152289-8-20220.50.5CF720022022
1522911-8-202211CM920032024
1523012-8-202200CF5.52002Unknown
1523114-8-202200.4CF920062025
1523214-8-20220.750.75CM720052025
1523316-8-20220.30.9AM620032025
1523416-8-202200LW7.520032024
1523518-8-202222RW620032025
1523618-8-20220.40.4LB820032024
1523722-8-202224CB620032025
1523825-8-20220.73LB720032026
1523926-8-202211LW720042024
1524027-8-202200CF720052025
1524127-8-202200.4RW720042024
1524227-8-20220.30.3CB720032027
1524328-7-20220.21.5RB72002Unknown
1524429-8-202200CM920062025
1524529-8-202212CB72002Unknown
152462-9-20221.51.5AM72004Unknown
152472-9-202222CM62003Unknown
152488-9-202200LW720042024
1524911-9-20220.40.4AM720052024
1525012-9-202200DM62004Unknown
1525113-9-20220.050.05CM620042023
1525213-9-20220.250.25AM820042024
1525313-9-20220.10.1CM820022023
1525413-9-20220.050.05LW720022022
1525513-9-20220.1250.125CM720022023
1525613-9-20220.0250.025AM620062024
1525713-9-20220.10.1AM620032024
1525813-9-20220.10.1CB620032024
1525913-9-20220.10.1CB620032022
1526013-9-20220.050.05CF620022023
1526113-9-20220.40.4CF620032023
1526213-9-20220.150.15CB620032023
1526313-9-20220.050.05RB620022023
1526413-9-202200LB720052023
1526514-9-20220.2750.275RW62002Unknown
1526614-9-20220.10.1LW720052025
1526714-9-202200CB620032025

Current value is public valuation of the player by TransferMarkt in million euro. FBM score is our propietary score to rank players. Only players who score 5.5 or higher make it to the list. Players in the right age group who get at any time a FBM score of 5.5 or higher are automatically added to the list. PlayerID is the ID of the player in our FBM database.