Category: Statistics


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:

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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.

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.

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:

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What is FBM replacement value?

With FBM replacement value we calculate what a club is to be expected to pay 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.

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), 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 88.875.000 euro and earned 56.000.000 euro for a loss of -32.875.000 euro. From 2022 on,players need to be born 2002 or later.

Of the 184 teenagers on the list 116 have increased in valuation (63%), 12 have decreased in valuation (6.5%) and 56 have no change in valuation mainly due to still being too young. (30.5%)

PlayedIDDate first in shadow teamValuation at dateTransfer/Currently valuedPositionFBM scoreBorn/playerContract/Sold
64612-4-2018126 million transfer to RennesRW8.66Jérémy Doku26M
1173230-10-20192.7530 million transferAM7.22Odilon Kossounou30M

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.

Showcase Sheffield: Sander Berge or John Lundstram

What we do with FBM contribution statistics is calculate what the probability is that a player is able to contribute to a specific team. For a player can strengthen team A, but weaken team B. We do this for four scenarios: 

  1. The best case.
  2. The most likely case.
  3. The worst case.
  4. The current form case, based on the current form of the players involved.

To calculate the probability that a player is able to contribute to a new team we look at:

  1. Difference between competitions.
  2. Difference between teams.
  3. Minutes played.
  4. FBM contribution stats.

We always look at how team A would do if they replace player X with player Y. In this showcase we look at how Sheffield United would do when they replace John Lundstram with Sander Berge.

The FBM contribution stats for John Lundstram are:

John Lundstram

The FBM contribution stats for Sander Berge are:

Sander Berge

At first sight one can see that Berge scores higher in almost every category. Only the probability that Berge is able to contribute to the attack in the most likely scenario (average) is significantly lower than that of Lundstram. Yet, it is important to take into account that the probabilities for Berge are with Genk playing in the Belgium competition and Lundstram’s probabilities are with Sheffield playing in the Premier League. So we have to adapt these values.

Calculating Sander Berge’s expected contribution to Sheffield

Because probabilities don’t point to a single event happening for sure, but for multiple possible events happening with a certain probability, we always look at the four different scenarios mentioned earlier. What we do in each scenario is subtract the actual contribution of Lundstram from the FBM stats for Sheffield and then add the expected contribution of Sander Berge to Sheffield. Sander Berge’s expected contribution is based on his actual contribution to Genk deflated or inflated to compensate for the differences between the competitions, teams and minutes played.

The most likely scenario

In the most likely scenario Sheffield will have a very similar overall performance. The attack of Sheffield will be a bit less strong with Berge rather than Lundstram. Defense will be the same. Transitioning & build up will be much better with Berge. The reliability of the team will remain the same. A 3 points higher FBM team score translates into one additional point in the competition for Sheffield.

The best scenario

In the best scenario Sheffield will not improve much. Sheffield will trade a slightly better performance overall and in attack to an even less functioning transitioning & build up. As you can see the best scenario is worse than the most likely scenario … worse for Sheffield. But with the best scenario, we look at the best performance of both players and compare those.

The worst scenario

As you can see in the worst case, Sheffield is better off in almost every category except defense. The reason is that Sander Berge, even after compensation for a different team and a different competition, still has a higher floor in his performance than John Lundstram.

The current form scenario

The current form scenario shows that impact that Berge can make on the play of Sheffield if he is able to keep his current form at Sheffield. Our FBM contribution stats predict that he probably performs a bit less than this as per the most likely scenario. But there is a decent chance that Berge will perform like this at Sheffield, netting Sheffield 2 extra points at the end of the season when they start with Berge instead of Lundstram.


In all scenarios Sheffield is better off with Berge than with Lundstram except in the best case scenario. When both players play at their best, it makes little difference to Sheffield who is in the starting XI. Yet, Berge is currently performing close to his top performance, while Lundstram is currently performing below his median performance. This means that in the short term, Sheffield’s performance is most likely to improve when they start with Sander Berge.

Probability that Sander Berge contributes to Sheffield84%
Probability that Sander Berge contributes to the attack of Sheffield75%
Probability that Sander Berge contributes to the defense of Sheffield84%
Probability that Sander Berge contributes to the transitioning & build up of Sheffield22%

What happened to the players of Sudamericano U15 2017?

In 2017 we analyzed all youth players of the Sudamericano U15. Now, two years later, it is interesting to see what happened to them and how that relates to their FBM contribution statistics. We look at all youth players who played at least 3 matches. Normally, we want at least ten matches for the most probable estimation of the worth of a player. Fortunately, one of the strongest features of Bayesian statistics is that even with a few data points Bayesian statistics is still able to draw valid conclusions. That means that Bayesian statistics is ideal for estimating which youth players have the best chance of making it.

In 2017 we analyzed 193 players born in 2002 or 2003. They played between 3 and 7 matches. All youth players get the following probabilities assigned:

ScoreOverallAttackDefenseTransition & buildupReliabilityNumber of games analyzed
C. de Oliveira Costa – KakàCF6.2999.1796.8375.4996.0891.775

Score is a summation of the other five probabilities. These five probabilities are:

  1. Overall is the probability that a player is able to contribute to the team in general.
  2. Attack is the probability that a player is able to contribute to the attack.
  3. Defense is the probability that a player is able to contribute to the defense.
  4. Transition & build up is the probability that a player is able to contribute to transitioning & building up.
  5. Reliability is the probability that the overall, attack, defense and transitioning & buildup probabilities remain the same in the next match. Yet, it also is an indication of how reliable the player is.

Score is a number running from 0 to 10 with players approaching 10 will be the best players in the toughest competitions. So the score of 6.29 for Kakà in the Sudamericano U15 is quite good. We use a score of 2.5 to distinguish between players who are more likely to make it in pro football. Players not able to score 2.5 points are less likely to make it.

In the Sudamericano U15 of 2017 there were 93 youth players who scored 2.5 points or more. There were 100 youth players who scored less than 2.5 points. Two years later we looked at whether they played at all in 2019 and if so how many minutes they played and how valuable the team is that they play for. Here are the results (we have also included the data for the top 30 youth players):

CriteriumTop 30 youth players according to FBM player scoreYouth players who scored 2.5 or higherYouth players who scored less than 2.5
Players still playing86.67%77.42%63%
Average value of the team the player plays for7.7 million3.6 million euro3 million euro
Average minutes played in 2019601512356

As you can see youth players who score well in FBM contribution statistics have a higher chance of still playing two years later. They play for more highly valued teams and they play more minutes.

The above results were achieved by only looking at the FBM player score. Basically, this means letting the computer decide who is the better player. When we actively evaluate these youth players ourselves, we look more closely at the FBM contribution statistics. We remove the attackers who scored 2.5 points or more, but who also have an attack probability of less than 50%. And we remove the defenders who scored 2.5 points or more, but also have an defense probability of less than 50%. 

When we evaluate players as described we only keep 78 of the 93 youth players who scored 2.5 points or more. Their results are as follows:

CriteriumTop 30 youth players according to FBM player scoreYouth players who scored 2.5 or higherYouth players who scored less than 2.5
Players still playing90%83.33%63%
Average value of the team the player plays for7.7 million4.3 million euro3 million euro
Average minutes played in 2019608542356

When you compare this second table with the first table, you can see that by not steering blindly on FBM player score, but actually looking at the underlying probabilities, allows you to select the most promising youth players even more accurately. What this means for clubs is that they can have their youth players analyzed and use FBM contribution statistics to determine which players are best to continue working with.

Match preparation PSV vs FC Basel 23-7-2019

The FBM tool makes it easy to predict how upcoming matches will unfold. If we use the most recent starting XI we get the following. As soon as the actual starting XI we’ll update our FBM tool to see what the prediction is based on the actual lineup.

The most important numbers are:

  1. PSV will dominate 43% of the match and Basel 8%.
  2. PSV will have 12% of the chances and Basel 88%.
  3. The most likely outcome is 1-3, but this happens only 9% of the time.

This is the raw data. So the second step is to interpret these numbers. The first thing to notice is that while PSV has the most domination, Basel has the most chances. That means that the chance of a draw is increased. 

The second thing to notice is that although PSV has the most domination, for 49% of the time no team has any domination. This often reflects chaotic periods where no team is able to dominate or control the ball for long. If the no domination percentage is higher than the domination percentage of either team, then more often than not, the team with the highest percentage in chances will disappoint.

For that reason our FBM tool predicts the following (the text is computer generated):

Most likely winner: PSV 1
PSV 1 wins 66% of the time based on 35 matches.
FC Basel 1 wins 34% of the time based on 35 matches.
Most likely outcome: 1 – 3 (happens 9% of the time) 
This result is based on pattern Brown: Both teams are only able to dominate small parts of the game and there is the risk that the underdog becomes overconfident in which case the favorite has a bigger than average chance of winning. (Brown 5)

Most valuable players

With our FBM tool one can look of course at all players of both teams, but for this match preparation we will only look at the most valuable players of PSV and Basel. The idea is that if you are able to neutralize these players, you increase your chance to win a lot.

For PSV the most valuable player is Donyell Malen as can be seen from his most recent FBM contribution chart:

As can be seen Malen is contributing a lot to PSV’s attack (yellow), defense (red) and overall play (blue). Transitioning (green) improved, but against an easier opponent than Basel, so we don’t expect Malen to contribute in that regard that much in this match.

For Basel the most valuable player is Jahlil Okafor as can be seen from his most recent FBM contribution chart:

Okafor has a very similar FBM contribution chart as Malen. The differences are a slightly lesser defensive contribution (red), but a considerable higher transitioning contribution (green).

Weakest defender

While the most valuable player is the number one target to neutralize, the weakest defender is the best area of defense to target for the attack.

For PSV the weakest defender is Luckassen. So attacking through the center is most likely to result in a goal for Basel.

For Basel the weakest defender is Widmer. So for PSV attacking Basel’s right flank would give the best chance to score.

We’ll update this article as soon as the actual starting XI are known.

Update with actual lineup:

PSV comes up with a very surprising starting XI and formation. A 3-5-2 formation according to the UEFA (displayed in our tool as a 5-3-2 formation as that is what happens most of the time when the wings are really wing backs, but in this case the wings are really wingers):

This new formation is good and bad news for PSV both at the same time. The good news is that both domination and chances have increased. Also due that Basel is not playing in their strongest formation according to our data as Basel is not starting with Okafor.

The bad news is that with this formation Basel is probably going to risk less and defend more. It has become unlikely that Basel becomes overconfident. That this means for PSV is that PSV now actually lesss chance to win and the most likely outcome is a draw. There is also less risk for PSV to lose the match. So in that sense, even though the new formation is quite innovative, it is still playing on safe. The same goes for Basel, but then by using a conservative approach to the match.

So the new numbers look like this:

  1. PSV will dominate 77% of the match and Basel 11%.
  2. PSV will have 20% of the chances and Basel 80%.
  3. The most likely outcome is 1-2, but this happens only 9% of the time.

And the computer generated description of the match reads as follows:

Most likely outcome: draw
PSV 1 wins 42% of the time based on 155 matches.
FC Basel 1 wins 28% of the time based on 155 matches.
Most likely outcome: 1 – 2 (happens 9% of the time) 
This result is based on pattern White: The favorite dominates most of the game, but the underdog has most of the chances (mostly through countering), so more than average it becomes a draw. (White 4)

Post match update

Although PSV won with a 3-2 result, the match unfolded pretty much as we predicted. Orginially we thought that PSV would win 66% of these kind of games. With the actual lineup we brought this percentage down to 42%. A big difference with the 67% win chance that the sports betting industry thought it would be. Given that Basel was leading 1-2 5 minutes before the end of the match, we think that 67% – although ultimately correct – was estimating PSV too strongly.

Our final estimation of a draw also turned out to be wrong, but very reasonable. A 1-2 result would be too much for Basel, but PSV was also very lucky in the dying moments of the match. So a draw was the most reasonable estimate before the match.

The weakest defenders were also correctly predicted with both defenders (Luckassen and Widmer) failing to prevent the opening goals. Looking at Widmer’s FBM contribution chart one can see that his performance in the match was the same as what we expected before the match:

Widmer in PSV vs FC Basel 23-7-2019

Basel did have a lot less chances than we predicted, although we correctly predicted the number of goals Basel scored. PSV had a bit more chancees than expected, but scored a lot more than we predicted. So all players will be updated with their performance in this match and the match that the teams play the coming week. Then we will create a new prediction for the return.

Why Răzvan Marin is a decent replacement for Frenkie de Jong

One of the questions that many people have when it comes to the upcoming 19/20 Eredivisie season is whether Răzvan Marin is a good replacement for Frenkie de Jong. In this article I want to show you how to answer that question using FBM statistics. Our approach consists of three steps:

  1. Subtract De Jong from Ajax.
  2. Add Marin to Ajax.
  3. Compare Ajax with De Jong to Ajax with Marin.

With FBM statistics you can literally subtract players from a team as we have an FBM team score which is the same kind of data as an FBM players score. Ajax with De Jong has the following FBM team score:

ClubOverallAttackDefenseTransitionSurpriseFBM team score

These numbers are the average FBM players scores of the starting XI of Ajax in the 18/19 season. Besides the absolute numbers, which indicate the strength of the team for all football clubs, one also has to look at the ratio of these numbers as that gives more insight in how well balanced the team is. For Ajax with Frenkie de Jong the balance of the team looks as follows:

(The less defense the better, the more transition and attack the better.)

The score for Frenkie de Jong looks almost the same:

Frenkie de Jong94197916

Yet, we have to divide these numbers by 11 and normalize for the percentage of the minutes that De Jong actually played. Then we can subtract those numbers of Ajax’ FBM team score to see how Ajax looks without De Jong. These numbers are:

ClubOverallAttackDefenseTransitionSurpriseFBM team score
Ajax with de Jong7338594815203
Ajax minus de Jong (with 10 players)6638524115182

You can immediately see that De Jong had very little impact on Ajax’ attack, but as only one player in a team of eleven players (9.09%), he had a major impact on defending (11.86%) and transitioning (14.58%).

Next we take Marin’s numbers at Standard Liege:


Those numbers look a lot less than Frenkie de Jong’s numbers. But we also have to compensate for the difference in leagues (Eredivisie vs Jupiler Pro League) and quality of the team and the team members (Ajax vs Standard Liege). When we use our Bayesian model to compensate for these matters, Marin’s most probable numbers for his play at Ajax are:


These numbers still look less than the numbers of Frenkie de Jong. But let’s see what happens when we add these numbers to Ajax. Again, in our model we normalize for played minutes and differences between the team. We then get:

ClubOverallAttackDefenseTransitionSurpriseFBM team score
Ajax with de Jong7338594815203
Ajax with Marin7341574115197

If we only look at the FBM team score, you can see that the scouts of Ajax did a great job as with Marin, Ajax is only 3% weaker (197 vs 203). If we look at the more detailed numbers, we can see that this is mainly due to the fact that Marin more strongly supports Ajax’ attack (41 vs 38). At the same time, transitioning will be less effective with Marin instead of De Jong (41 vs 48). To most observers that would be obvious. Nevertheless, we are always happy when our model comes up with obviousness. It is an interesting trade-off. Yet, given how hard it is to find players that support transitioning, it is also very understandable that Ajax has made this trade-off.

The most interesting part though is the balance of the team:

The slight increase in defense reflects that Ajax has become a little bit weaker with Marin instead of De Jong. Nevertheless, this is compensated by having transitioning and attacking more in balance. That’s why we think that Marin is a decent replacement for Frenkie de Jong.

How probable is it that Marin is able to contribute?

As we always stress that football data is meaningless, unless you can answer the question: “What is the probability that a player is able to contribute?”, let me make this more explicit in the case of Marin. As Marin’s numbers above are his probabilities. 


So let’s answer the following questions:

  1. What is the probability that Marin is able to contribute to Ajax overall? Answer: 84%
  2. What is the probability that Marin is able to contribute to Ajax’s attacking? Answer: 39%
  3. What is the probability that Marin is able to contribute to Ajax’ defending? Answer: 68%
  4. What is the probability that Marin is able to contribute to Ajax transitioning? Answer: 3%

These numbers have a plus or minus 6% points room to deviate (the surprisal rate).