Category: voetbalstatistiek

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 65.875.000 euro and earned 56.000.000 euro for a loss of –9.875.000 euro. From 2021 on,players need to be born 2001 or later.

Of the 117 players on the list 77 has increased in valuation (73%), 6 have decreased in valuation (4%) and 34 have no change in valuation mainly due to still being too young. (23%)

PlayedIDDate first in shadow teamValuation at dateTransfer/Currently valuedPositionFBM scoreBorn/playerContract/SoldSold
64612-4-2018126 million transfer to RennesRW8.66Jérémy Doku26M26
1173230-10-20192.7530 million transferAM7.22Odilon Kossounou30M30

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. 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).

The less you need to defend to better your team is

Teams prefer to attack (blue) rather than defend (red). But if your team is up against a stronger team, the chance is that they’ll force you to defend. Transitioning is a lot harder than kick and rush. For that reason the weakest teams have very little effectiveness in transitioning.

Here are the results of all the teams in the Dutch Eredivisie. As you can see Ajax is the most balanced team. It also shows that even though the teams ended very close in the ranking, Ajax is obvious a stronger team.

These numbers are based on the FBM team score, which in turn is based on the attacking, defending and transitioning efficiencies of the individual players in the starting XI. There is a 67% correlation (reversed) between the defensive score and the points a team scored in the league.

Given that these numbers are about effiencies, these percentages are not about the time spent by each team on either attacking, defending or transitioning. Instead these numbers are about what is working for the team.

These numbers show that strong team first focus on transitioning. If that isn’t working out for them, then they go for kick & rush or long balls. If that fails, then all that is left to do is defending.

With FBM statistics we can find players for you club that strengthen the transition or the attack as we can calculate exactly how much a player contributes to the transitioning, attacking and defending of the team.

FC Utrecht
FC Groningen
ADO Den Haag
Willem II
PEC Zwolle
FC Emmen
Fortuna Sittard
De Graafschap
NAC Breda

Dynamical player statistics

Football is a very dynamic game. A lot is happening at the same time. So a static description of a football match or of football players is lacking a crucial element, namely the dynamics of the game.

Here are two video’s that shows how two great players, Frenkie de Jong and Hakim Ziyech, differ in the dynamics of their statistics. We will never know for sure why Frenkie de Jong got a 75 million transfer whereas Hakim Ziyech was unable to find the club of his dreams in the summer of 2018, but we take it that it has a lot to do with the difference in the dynamics of the statistics of both players.

A short interlude of how to read the graphs in both video’s. FBM measures the efficiency of football players. We make a graph of each match analyzed, because it is very important to understand how statistics differ within a single game. That is the only way to discover whether players are able to continu to play well at the end of the match.

The blue line is the overall efficiency. The blue line gives the probability that a player strengthens the team. The red line is the defensive efficiency, the green line the transitioning efficiency and the orange line is the attack efficiency. These lines give the probabilities of the player strengthen the defense, transition and attack. Few player are able to keep their attack efficiency at a high level and even fewer players are able to keep their transition efficiecy at a high level.

Here is the video for Frenkie de Jong. The matches are shown for the season 18/19 up to today.

As you can see Frenkie de Jong is very stable. In the last few weeks his effiency is suddenly dropping. Most commentators link that to his megatransfer. But you also see how quickly Frenkie recovers. The high transfer fee is justified by him consistently having a high transition efficiency.

Here is the video for Hakim Ziyech. The matches are shown for the last 12 months and include his less than successful world championship playing for Morocco.

Here we see that Ziyech struggled to get high effiency. That doesn’t mean that he didn’t do any good on the pitch in that period, but it means that he made even more errors so that his efficiency was quite low at times. It is the one reason why we think he did not get transferred in the summer of 2018.

We can also see that nowadays his efficiency is much higher. Nevertheless, the dynamics still show that he is quite fickle. Given that his efficiencies are so much higher this season than last season, we think he will transfer to a great club for a big, but not huge amount. That club has to realize though that Ziyech has a bit of fickleness.