Profile: Ruud Gullit = brain type #3 the Successfull Worker

Exploitation


In football terms, “exploitation” typically refers to a player’s or a team’s ability to take advantage of the weaknesses or mistakes of the opposing team. This can be done in various ways, such as exploiting gaps in the defense, taking advantage of a goalkeeper’s poor positioning, or capitalizing on a defender’s lack of speed or skill.

Ruud Gullit was an exceptional footballer who was known for his versatility, technical skills, and physical prowess. He played in various positions throughout his career, including as a midfielder, forward, and sweeper. This adaptability allowed him to exploit mismatches against opponents who were less skilled or less physically gifted.

Gullit’s understanding of the game, combined with his technical skills, allowed him to create scoring opportunities for himself and his teammates. He could exploit gaps in the opposing team’s defense and could use his strength and skill to win physical battles against defenders. His ability to read the game also allowed him to anticipate where the ball was going to be and to position himself accordingly, giving him a further advantage over his opponents.

Autonomy

In a football context, “autonomy” could refer to a player’s ability to make independent decisions during a match, to control the game, and to take actions without needing explicit instructions or assistance from their teammates or coaches.

Ruud Gullit was known for his high football IQ, technical skills, and leadership abilities. These traits gave him a high degree of autonomy on the pitch.

As a player, Gullit had the ability to read the game and make decisions quickly. He could adjust his play style based on the situation, such as knowing when to hold onto the ball, when to pass, and when to attempt a shot on goal. He was also known for his ability to take on and beat defenders one-on-one, which is a skill that requires a high level of autonomy.

As a captain and a leader, Gullit often took charge on the pitch. He could direct his teammates, organize the team’s formations and strategies, and influence the flow of the game. His strong leadership skills are another indicator of his high level of autonomy.

Extraversion


“Extraversion” is a trait often associated with outgoing, sociable, and energetic individuals. Extraverts often enjoy being around people, are full of energy, and often quite active. They are likely to enjoy group activities and community participation.

Ruud Gullit, as a footballer and later as a manager, was often seen as an outgoing and charismatic figure. His public persona has often been characterized by a high level of energy and enthusiasm, both on and off the pitch. His ability to communicate effectively with teammates, lead as a captain, and later manage teams also indicates a high level of extraversion. He has also been involved in media work, further demonstrating his comfort in social and public situations.

Assertive

“Assertiveness” in a football context typically refers to a player’s ability to express their ideas and opinions clearly, confidently, and directly. It can also refer to their tendency to take charge in situations, to make their presence felt on the pitch, and to influence the actions of their teammates and opponents.

Ruud Gullit was known for his assertiveness both as a player and as a manager. As a player, Gullit was a commanding presence on the pitch. He was not afraid to take on opponents, to demand the ball from his teammates, or to take shots on goal. He was known for his ability to influence matches, whether by scoring goals himself, setting up his teammates, or disrupting the play of his opponents.

As a captain, Gullit was a vocal and influential leader. He would organize his teammates, direct play, and communicate clearly and effectively with both his team and the match officials. As a manager, Gullit was similarly assertive, making strategic decisions, managing his players, and dealing with the press.

Enthusiasm


Enthusiasm in the context of football generally refers to a player’s passion, energy, and dedication for the game. Enthusiastic players are often seen as being highly motivated, eager to participate, and having a positive attitude both on and off the pitch.

Ruud Gullit, throughout his career as a player and then as a manager, consistently demonstrated high levels of enthusiasm for football. Here are some possible reasons why Gullit might score high on enthusiasm:

  1. Passion for the Game: Gullit always showed a deep love and passion for football, which is a key component of enthusiasm. He was known for his dedication to improving his skills and understanding of the game.
  2. Energetic Play: Gullit was known for his energetic and dynamic style of play. He often took charge in matches, showing a keen desire to influence the game.
  3. Positive Attitude: Gullit often displayed a positive attitude, both in success and in adversity. This is a trait commonly associated with enthusiasm.
  4. Leadership: As a captain and a manager, Gullit showed enthusiasm in leading his teams. He was known for his ability to inspire and motivate his teammates.
  5. Post-playing Career: Even after retiring as a player, Gullit’s enthusiasm for football remained evident. He transitioned into management and punditry, roles that allowed him to stay involved with the sport he loves.

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.

Profile: Pep Guardiola = brain type #1, the Perfectionist

Social Stability

Pep Guardiola, as a football manager, is known for his leadership skills, strategic acumen, and ability to build strong, cohesive teams. These qualities could contribute to a high score in Social Stability, which often refers to a person’s ability to maintain stable, positive relationships with others and contribute to the stability of their social environment.

  1. Leadership: Guardiola has led several top-tier football clubs to success, including FC Barcelona, Bayern Munich, and Manchester City. His leadership style, which emphasizes teamwork, discipline, and strategic play, has been widely praised.
  2. Team Building: Guardiola is known for his ability to build strong, cohesive teams. He often works closely with his players to develop their skills and help them work together more effectively. This ability to foster positive relationships and teamwork could contribute to a high score in Social Stability.
  3. Strategic Acumen: Guardiola’s strategic acumen is another key aspect of his success as a football manager. His ability to analyze the strengths and weaknesses of his own team, as well as those of opposing teams, allows him to develop effective game plans and make strategic decisions during matches. This strategic acumen could also contribute to a high score in Social Stability, as it demonstrates his ability to navigate complex social situations effectively.

Exploitation

In the context of leadership and management, “exploitation” often refers to the ability to make full use and derive benefit from resources. In the case of a football manager like Pep Guardiola, these resources would include the skills and talents of his players, as well as the tactical and strategic options available to him.

  1. Tactical Acumen: Guardiola is renowned for his tactical acumen and his ability to adapt his team’s style of play to exploit the weaknesses of opposing teams. This ability to exploit tactical opportunities could contribute to a high score in Exploitation.
  2. Player Development: Guardiola is also known for his ability to develop and improve players. He often works closely with his players to help them improve their skills and reach their full potential. This ability to exploit the talents of his players could also contribute to a high score in Exploitation.
  3. Resource Management: As a manager, Guardiola is responsible for managing a wide range of resources, including players, coaching staff, and training facilities. His ability to manage these resources effectively and make the most of what he has available could also contribute to a high score in Exploitation.

Conscientiousness

Conscientiousness is a personality trait characterized by organization, responsibility, and a strong sense of duty. It’s associated with a preference for planned rather than spontaneous behavior.

  1. Organization: Guardiola is known for his meticulous attention to detail and his organized approach to football management. He often spends hours studying game footage and devising detailed game plans. This level of organization could contribute to a high score in Conscientiousness.
  2. Responsibility: As a manager, Guardiola is responsible for the performance of his team. He takes this responsibility very seriously and is known for his commitment to his role. This sense of responsibility could also contribute to a high score in Conscientiousness.
  3. Duty: Guardiola often speaks about his duty to his team and to the fans. He is dedicated to his role and is committed to achieving the best possible results. This strong sense of duty could further contribute to a high score in Conscientiousness.

Industriousness

Industriousness is a personality trait characterized by hard work, diligence, and a strong work ethic. It’s associated with a preference for sustained effort and productivity.

  1. Hard Work: Guardiola is known for his hard work and dedication to his role as a football manager. He often spends long hours studying game footage, devising game plans, and working with his players. This level of hard work could contribute to a high score in Industriousness.
  2. Diligence: Guardiola is also known for his diligence. He pays close attention to detail and is committed to doing his job to the best of his ability. This diligence could also contribute to a high score in Industriousness.
  3. Work Ethic: Guardiola has a strong work ethic. He is dedicated to his role and is committed to achieving the best possible results for his team. This strong work ethic could further contribute to a high score in Industriousness.

Orderliness

Orderliness is a personality trait characterized by a preference for organization, structure, and cleanliness. It’s associated with a desire for predictability and control over one’s environment.

  1. Organization: Guardiola is known for his meticulous attention to detail and his organized approach to football management. He often spends hours studying game footage and devising detailed game plans. This level of organization could contribute to a high score in Orderliness.
  2. Structure: Guardiola’s teams are known for their structured style of play. He often implements complex tactical systems and expects his players to adhere to them strictly. This preference for structure could also contribute to a high score in Orderliness.
  3. Control: As a manager, Guardiola is responsible for controlling a wide range of factors, from player selection and tactics to training routines and team morale. His ability to maintain control over these factors could further contribute to a high score in Orderliness.

General introduction to the personality of football players based on Cybernetic Big Five Theory

To start first a quote of one of the football agents that use this system for better understanding and supporting their players:

Just wanna let you know about my first meeting with the player yesterday.
Spend 2 hours talking about #3 successful worker.
He was blown away and could definitely see himself in the things I presented and pointed out.

It was fantastic to have something that concret to talk to a new player about, and it made our relationship strong from the beginning.
I have a very, very good feeling about the player and the things we have decided to work with in the future.

Soon i´ll set up a meeting with the next player.
I have a very strong relationship with him already, and I look forward to knowing about his brain type 🙂

See you soon!

Best from Denmark
René Lundgaard
Footballers Collective

Once you have watched this video, you understand that there are nine sets of evolutionary behavioral patterns. And that these patterns are dynamic. If a player is stressed he behaves in a different way than when he is relaxed or when he is neither stressed or relaxed (what we call “neutral”). What creates the biggest chance for the player to reach peak performance depends on his brain type and his position. Below you can see each of these nine dynamics and there relevant evolutionary behaviors from Cybernetic Big Five Theory:

Davy Klaassen to Ajax

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This would result in 5 additional points in the table.

To conclude: Ajax is slightly better off with Klaassen.

Shadow team born this century anonymized

To track how we are doing in finding talent at a relative early age (20 years or younger, from 2023 on players need to be born 2003 or later), we publish our shadow team anonymized and keep track of how these players are doing. As soon as any of these players transfer to another club or become a household name, we update this list and reveal the name. Or if they turn 25 in case the did not break through. Valuation at date is the price where would virtually buy the player. That way we can see how much profit would make virtually.

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

So far we have spent 171M euro and earned 367M euro for a profit of 196M euro. The valuation of these players has gone up to 1368M (+800%).

Of the 375 teenagers on the list 277 have increased in valuation (73.8%), 31 have decreased in valuation (8.3%) and 66 have no change in valuation mainly due to still being too young. (17.6%)

PlayedIDDate first in shadow teamValuation at dateTransfer/Currently valuedPositionFBM scoreBorn/playerContract/Sold
563929-5-20190.20.55AM5.9520002022
644322-4-201902.5LW620032023
644713-1-202000.5DM620022023
64612-4-2018126RW8.66Jérémy Doku26M
705916-2-2020125RB5.920002024
73835-9-20200.50.375LW7.8220002023
74062-3-20200.70.7GK6.2420002023
804422-4-20190.50.7CM6.8420022022
804619-4-20190.250.5CM7.3920002022
810727-1-20200.752CF7.2920002025
81303-5-20190.10.7DM7.8920002022
814417-8-201900.7LW6.520012022
814517-8-20190.53RB6.8520012022
823021-10-201938LB5.9720002024
87398-6-20190.21.5CB6.7720022023
87418-6-201904DM620022023
87528-6-20190.0750.5RW620012023
87588-6-20190.10.175RW720012022
88151-11-20180.325RW920012024
902222-4-201908LB620032025
902313-1-202000.8LB720022023
902427-9-20210.20.3DM620022024
90257-9-201901.3CM5.520032022
97778-6-20190.050.3RB820012022
985010-12-20200.12RW6.820002022
986310-12-20200.31.5RB7.620002024
1009328-5-20192.510CB7.420002024
1034012-12-201800.6RW820002023
1035219-8-2020010LB6.2520012024
109571-8-20190.40.7AM7.1720002022
110465-3-20200.40.55DM6.8820002022
1112017-8-201900.15CM820012022
1112127-1-202001.2CM6.1320012023
1125817-9-20190.051.5CB6.052002unknown
1127024-12-202020.6CB6.320022024
115137-3-20201.16RB7.0620002026
1165720-12-20190.11CB6.4720002023
1173230-10-20192.7530AM7.22Odilon Kossounou30M
118414-12-2019012LB7.4220012024
1206325-1-20200.10.45CM7.7620002024
1209412-9-20200.050.6CM7.1120002022
121033-3-20200.0255CB7.3120002022
122305-6-20203.54CF8.220022023
1228918-5-202011.5CF5.9920022024
1234326-7-20202.31AM620012025
1241422-6-20200.80.35RW5.620012024
125256-4-202113CF620022025
1261415-7-202001CB6.1620022023
126652-7-20200.435DM7.5Amadou Onana35M
1272121-7-20200.10.3LW6.520002023
1274922-7-20200.40.25RW62001unknown
1279023-7-20202.52.5LW5.720012025
1279325-7-20200.30.35CF720012023
1279425-7-20200.55CF6.520022023
1279525-7-20200.152CB620002022
1279625-7-20200.810CB620012022
1280426-7-20200.11.7CB5.820032022
1281026-7-20200.050.4RW5.720042023
128551-8-20200.20.25CF5.920022022
128562-8-202000.6CM5.620032022
128695-8-2020010AM720002022
128745-8-20200.270LW6.3Mykhaylo Mudryk70M
128899-8-20200.91CB620002024
1290210-8-20200.10.1LW6.620032022
1290310-8-20200.153LW7.520032025
1291011-8-20200.250.3CM8.220012022
1292212-8-20200.150.2CF7.120012023
1292712-8-20200.27CF5.920042022
1292913-8-20200.54RW620012023
1297519-8-202008CM5.5Sergio Arribas8M
1298322-8-20200.718CM6.1220022024
1302526-8-202000AM6.320052023
1304414-11-20210.30.8CM6.320022023
130755-9-20200.30.2DM7.1320002023
130968-9-20200.23CB5.6820002023
1312613-12-20200.56LB6Jayden Oosterwolde6M
1315915-9-20200.0522.7CB6.43Ilya Zabarnyi22.7M
1316215-9-20200.43CB7.5620002023
132816-10-20200.050.1CB6.2220002022
1329914-10-20201.2116DM7.5Moisés Caicedo116M
1330018-10-202032.5CB5.720032023
1330122-10-20200.21CB5.820002025
1334428-10-202031.8LW5.9620012023
133563-11-20200.115CM7.520022025
133573-11-20200.10.5CF720022025
1344210-12-20200.20.8CM820002023
134718-12-202013CF5.520022022
134729-12-202006CF6.620012023
1349110-12-20200.30CM620012022
135346-1-202100.1CM62004Unknown
135356-1-202100CM820052022
135366-1-202100.2AM82005Unknown
135376-1-202100CF720052023
135396-1-202100.15CF62004Unknown
135493-1-20211.80.5AM620022024
135536-1-20210.7250.3CF620012023
135606-1-202100CF62004Unknown
135616-1-202100.4CF720042022
135626-1-202100CF720042022
135636-1-202100.25AM72005Unknown
1365211-1-202100GK6.220022022
1367714-2-202100RW7.520022022
1367914-2-202100LW6.920022024
1368417-2-202100CF6.32002Unknown
1369419-2-202100.75CF6.420042024
1369519-2-202100.8AM6.720042022
1369619-2-202100.075RW72004Unknown
1369919-2-202100.1LW5.820042022
1370019-2-202100AM6.92004Unknown
1370119-2-202100.075AM6.62004Unknown
1370220-2-202100CF6.82003Without club
1370324-2-202100.6RB7.920022026
1370625-2-20210.510GK620012024
1370827-2-202115CF5.720032023
137224-3-2021016.9CM6Pape Matar Sarr16.9M
1373415-3-20210.85RW7Ansgar Knauff5M
1373516-3-20210.71.1CF620012024
1373616-3-202102CM7.520032022
1374822-3-202123DM6.520022022
1376924-3-202135.5CB5.9Radu Drăgușin5.5M
1377125-3-202130CB7.120012024
1380630-3-20210.80.75CB72002Unknown
1381130-3-20210.10.175CB6.820012025
1383331-3-20210.540AM720032022
138428-4-202101.3LB5.820022024
138448-4-202101.2CM6.720022024
1387016-5-20213.513CM6.3Arsen Zakharyan13M
1387117-5-20210.0750.3CB8.520012022
1387217-5-202128RW720032022
1399719-2-202100CF62004Unknown
1399819-2-202100CF5.720042023
1416824-9-20210.050.55CM5.920022022
1425913-7-202100CM72006Unknown
144543-8-20210.11RW720022025
144583-8-20210.10.7LW720022025
1450018-8-20211.53AM620022024
1450118-8-2021213CB7.520012024
1458314-9-202100.3LB6.620032022
1460116-9-202100.25AM620012022
1462124-9-202101.5CB620042023
1463427-9-202100.45CB5.520032022
1463527-9-20210.30.9LB620032024
1468612-10-202100.25CM5.620022022
1469413-10-202100.6CB5.82001Unknown
1471019-10-202100.75CB9.120042023
1471219-10-202100.15CB8.22003Unknown
1471419-10-20210.10.3CM6.520032022
1471619-10-202100.2LW6.320032022
1471819-10-202100CF5.920052023
1471919-10-202100.15GK6.32003Unknown
1472019-10-20210.10CB620022023
1472119-10-202100.15CM7.22003Unknown
147426-11-2021110LW82002Unknown
147466-11-202100.35RB7.52002Unknown
1476411-11-20211.212.85CB6Andrew Omobamidele12.85M
1477011-11-20210.614GK7Gavin Bazunu14M
1477312-11-2021312CM720022024
1478814-11-20210.050.35AM6.820012023
1479014-11-20210.40.25CB6.720012023
1479114-11-20210.20.65RW5.820022024
1479424-11-202100.3LW62005Unknown
1479524-11-202100.7RB620032023
1479624-11-20210.20.3CB5.520012022
1479724-11-202100.3CB62005Unknown
1479825-11-20210.750.4CB620022025
1479913-12-202100.2RB720052023
1480114-12-202100.3CB720042022
1480224-12-202110.7CB620012026
1480324-12-20210.10.5CB720022023
1480424-12-20210.51.2CB6.520012024
148054-1-202200.6DM820042023
148064-1-2022240CB920032023
148074-1-202200.5CB62003Unknown
148085-1-2022112CB72002Unknown
148095-1-20220.31.8CB620032023
148105-1-20221.30.8CB720032022
148115-1-20220.350.5CB620032025
148125-1-20220.47DM620022024
148135-1-202200.8DM620042022
148146-1-20221.20.45GK620032023
148156-1-20220.80.5GK720022023
148166-1-202207LB820032023
148176-1-202200.2RB720032022
149193-2-20220.53CB6.520022023
149288-2-202200AM6.120042023
149298-2-202200CF6.12006Unknown
149449-2-202200.4RB6.720032024
149539-2-20220.30.3CF6.420042023
149479-2-202200.15RW5.920032022
150122-3-202200.2GK6.920032022
150152-3-202200.125CB62003Unknown
150282-3-202200.6CB5.820032023
150577-3-202200AM620062022
1507825-3-202200.5CF5.520042025
1507925-3-20220.40.5RW620032023
150867-4-202200CM620052024
1508710-4-202200.5CM5.520062022
1508810-4-202200LB620052022
1452015-4-20220.510RW5.520032025
1509018-4-202200CF82006Unknown
1509128-4-2022020DM720062024
151197-5-202202CM620042024
151208-5-20220.51.2CB5.520032023
151219-5-20220.72.5AM62003Unknown
1512210-5-2022115CB6El Chadaille Bitshiabu15M
1512310-5-202221CM6.520052024
1512411-5-20220.20.7CB720022026
1512512-5-202200.6CM5.520042023
1512614-5-20220.21CB720042024
1512715-5-202201LW620052024
1512815-5-202222.5CF720032025
1512915-5-202200.5AM720052023
1258316-5-20220.90.8RW620022023
1513121-5-2022035 million transferCF6Endrick35M
1513222-5-202200.25CB620042024
1513323-5-2022220AM7Arda Güler20M
1513430-5-20220.10.8CF72004Unknown
1513531-5-20221.22CB720022024
151361-6-20220.753.5AM620052025
151371-6-20222.89CB6Ahmetcan Kaplan9M
151382-6-20220.30.55CB5.520022024
123932-6-20220.2750.8CB62004Unknown
1513911-6-20221.21.5LB620022025
1514012-6-2022020AM7Matheus França20M
1514113-6-2022015DM720042023
1514216-6-20220.350.5CM720032023
1514317-6-20220.5750.5CB72003Unknown
1514417-6-202201CF5.520032026
1514517-6-20220.250.2DM5.520032025
1514617-6-20220.51.5RW5.52003Unknown
1514718-6-20220.40.75AM720022024
1514928-6-20220.351CF720022024
87575-7-20220.20.2LW820032026
151505-7-202200LB820052023
151515-7-202200.6DM62005Unknown
1515211-7-202200CF7.52004Unknown
1515312-7-20220.712CF7David Datro Fofana12M
1515413-7-202201CF620052023
1515513-7-20220.45CF620052025
1515715-7-20220.350.45CB5.52002Unknown
1515817-7-20220.0750.1RB62003Unknown
1515919-7-20221.22CB620032024
1516019-7-20220.61CM820032025
1518023-7-202200.8CB62005Unknown
1518123-7-20220.450.6RB720032023
1522531-7-2022225LW720042023
152262-8-202224LW720032023
152273-8-202216CM6Sivert Mannsverk6M
152289-8-20220.50.4CF720022022
1522911-8-2022112CM920032024
1523012-8-202200.15CF5.52002Unknown
1523114-8-202201CF920062025
1523214-8-20220.750.75CM720052025
1523316-8-20220.30.75AM620032025
1523416-8-202200.075LW7.520032024
1523518-8-202221.8RW620032025
1523618-8-20220.40.5LB820032024
1523722-8-2022210CB620032025
1523825-8-20220.717.87LB7Milos Kerkez17.87M
1523926-8-202210.5LW720042024
1524027-8-202200CF720052025
1524127-8-202200.5RW720042024
1524227-8-20220.345CB720032027
1524328-7-20220.23RB72002Unknown
1524429-8-202200CM920062025
1524529-8-202215.5CB72002Unknown
152462-9-20221.52AM72004Unknown
152472-9-202223.5CM62003Unknown
152488-9-202200LW720042024
1524911-9-20220.415AM720052024
1525012-9-202200.4DM62004Unknown
1525113-9-20220.050.3CM620042023
1525213-9-20220.250.6AM820042024
1525313-9-20220.10.05CM820022023
1525413-9-20220.050.1LW720022022
1525513-9-20220.1250.15CM720022023
1525613-9-20220.0250.025AM620062024
1525713-9-20220.10.15AM620032024
1525813-9-20220.10.15CB620032024
1525913-9-20220.10CB620032022
1526013-9-20220.050.05CF620022023
1526113-9-20220.40.6CF620032023
1526213-9-20220.150.15CB620032023
1526313-9-20220.050.05RB620022023
1526413-9-202200.15LB720052023
1526514-9-20220.2750.7RW62002Unknown
1526614-9-20220.10.3LW720052025
1526714-9-202200CB620032025
1526816-9-20220.0750.1AM820032026
1526920-9-202200CB820062023
1527021-9-20220.250.75LB720042023
1527121-9-202200.5AM720042023
1527222-9-20220.0750.1CB72003Unknown
1527324-9-20221.21.5LB720022025
1527424-9-202200CB720062024
1527524-9-202200CF620062024
1527624-9-202201.5CM820042024
1527727-9-202200.15RW620042024
1527828-9-202200AM820062023
1527929-9-202200.5CF720042023
152804-10-20220.51.5AM820042023
152814-10-202200CM720042023
152824-10-202213CF620052024
152834-10-202200RW820052024
152844-10-202200.35AM620032023
152854-10-202200.5CF620042023
152864-10-20220.32.5LW5.52003Unknown
152876-10-202200CF620062025
152886-10-20221.55AM720042025
152898-10-20220.11RW720042026
1529010-10-20221.515CM6Hákon Arnar Haraldsson15M
1529111-10-202203.5CF720052023
1529211-10-20220.88AM820062025
1529311-10-202200AM720052023
1529412-10-20220.120CF620022026
1529513-10-2022212CM820032023
1529616-10-20220.810AM6Farès Chaïbi10M
1529717-10-202200.3RB62004Unknown
1529831-10-202203RW82005Unknown
152992-11-20220.11.5DM720042025
1530016-11-20220.30.4CF720042025
1530116-11-202200DM620062025
1530224-11-202200.1AM720032024
1530328-11-20220.30.9LW720042023
1530429-11-202211.5CB620032025
153051-12-20220.2250.4AM620042024
153061-12-202202CM720062023
153074-12-20220.251CF720032023
153084-12-202200.4AM620062025
153094-12-202200CF62006Unknown
153105-12-202200LW720062025
153115-12-202200LW82006Unknown
153125-12-202200.05AM720052024
153135-12-202206CB820072024
153147-12-20220.21CM820022025
153157-12-20221.55AM82005Unknown
1531614-12-20220.86.5RB6Tiago Santos6.5M
1531727-12-20221.32DM820022027
153181-1-20230.55CM620032025
153191-1-20230.450.4AM620042023
153202-1-2023112RB5.520052025
1513410-1-20230.80.8RW72004Unknown
1532112-1-20230.47.5CB6Ousmane Diomande7.5M
1532216-1-20230.1250.9LW72004Unknown
1532316-1-202300CF62005Unknown
1451619-1-20230.350.6LB620042025
1532422-1-2023130CF720042026
1532527-1-20230.41CF620052025
153262-2-202311CF62004Unknown
153271-3-20230.151.5CF620032027
153282-3-202311CF620052023
153292-3-20231.32RW620032023
153302-3-20230.20.3CM720052024
153315-3-20230.30.3CB620042023
153325-3-20230.10.1CB620032023
1533312-5-202313CM6.520042024
1533430-5-20230.1250.125CM720052025
1533531-5-202300.3LW72004Unknown
1533631-5-20230.30.6CF720062025
153371-6-202322.8RW620052024
153384-6-20230.32RW620042024
153395-6-20230.30.3LB820042024
153405-6-20230.80.8DM920042023
153418-6-20230.250.25CB620032025
1534211-6-202300.7RB720032023
1534321-6-20230.150.15CF62004Unknown
1534425-6-20230.10.1LB62003Unknown
1534526-6-20230.20.4CF62003Unknown
153466-7-202333CB720032024
1534712-7-20230.50.5CB620032024
1534816-7-20231.21.2CM720032026
1534920-7-20230.251LB720032025
1535025-7-20230.70.7LB620032024
1535128-7-202300.3LW820052025

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.

Relative strength MLS

Here is the list of the relative strength of teams in the MLS based on their performance in the previous season according to Wyscout data. If it were a simple competition than this would also be our prediction for the league table. Previously predictions like these have had a 80% correlation with the actual league table a year later. So will see how it goes for the MLS:

RankFBM Wyscout score
1Atlanta73
2Los Angeles72
3Salt Lake71
4Toronto70
5Dallas67
6New York City65
7Columbus63
8Seattle62
9Portland61
10Chigago57
11Minnesota56
12San Jose56
13Colorado54
14New England54
15LA Galaxy52
16Montreal51
17Philadelphia50
18Kansas City48
19DC45
20Orlando42
21New York RB40
22Houston37
23Vancouver36
24Nashville35
25Cincinnati30
26Miami19

Showcase: Niklas Dorsch

Niklas Dorsch is a defensive midfielder of Heidenheim, playing in 2. Bundesliga. Dorsch has been on our radar for the last couple of years and with only one year left at Heidenheim, he is an interesting player to follow. He plays for Germany U21. We think he would do well in 1. Bundesliga. For that reason we show here that he would be a good addition to Eintracht Frankfurt.

Dorsch at Heidenheim

Here is Dorsch’ most recent FBM contribution chart:

Although Heidenheim lost and Dorsch did not play his best match, especially in the first half as can be seen from his contribution chart, he is still an exceptional player according to his FBM stats:

Yet, these are his stats for playing in the 2. Bundesliga. How would he do at Eintracht Frankfurt? We think that Dorsch is a good replacement for Hasebe at Eintracht Frankfurt. Hasebe’s most recent contribution chart shows he is not playing well at the moment:

Also his FBM stats are less than those of Dorsch:

Dorsch is slightly better than Hasebe at their highest performance, but Dorsch beats Hasebe on average performance, current performance and worst performance. Yet, Hasebe plays on a higher level. So we have to take that into account.

Dorsch playing for Eintracht Frankfurt

Taking into account minutes played, difference between both clubs and both competitions, we get the following results for Dorsch playing at Eintracht Frankfurt:

What you see in the first row, is the performance level of Eintracht Frankfurt in the 1. Bundesliga. In the second row we subtract Hasebe’s contribution to the performance of Eintracht Frankfurt. That is only a small difference because Hasebe is not contributing that much on average. In the third row we add Dorsch to the expected performance of Eintracht Frankfurt. Finally, we can see how Eintracht Frankfurt’s performance would increase or decrease in row 4. Overall performance of Eintracht Frankfurt would rise as would attack and transitioning. Defensive performance would suffer slightly.

Eintracht Frankfurt’s FBM Team Score would increase to 115 points up from 102 points. There is an 80% correlation between FBM Team Score and future ranking in the league. If other teams would not improve Eintracht Frankfurt would rise to rank 10 in the league table if they played with Dorsch rather than Hasebe.

What is Dorsch worth?

Our model takes into account, position, highest transfer fee in the current season, record transfer fee, difference in competition, club, player age and length, international status and FBM stats. Due to the Corona crisis, it is much more uncertain how future transfer fees will develop. Our model is still based on the pre-Corona circumstances. 

When we calculate what we call the replacement fee for players. This is the amount of money the current employer of the player can expect to spend on a replacement who is as good as their current player. In short: clubs should not transfer players for less than the replacement fee, nor buy players for more than the replacement fee. As the replacement fee differs from club to club, there is room for negotiations. We also calculate what the player would be worth one year later if he is able to transfer to an even better club. All assuming his FBM stats remain the same.

Here are the replacement values for Dorsch:

Replacement value for Heidenheim£2,592,419
Replacement value for Eintracht Frankfurt£3,861,435
Replacement value for Schalke 04£6,672,353

TransferMarkt currently values Dorsch at £4.05m. We think that Dorsch is slightly overvalued on TransferMarkt. If Eintracht Frankfurt were able to buy Dorsch for less than £3,861,435, they would have a good deal. A deal that might make them almost £2m a year later, if they would transfer Dorsch to the next club and Dorsch would perform at the level we expect him to do. As a reminder, we predicted that Dalmau would be worth 1.75m euro to Heracles and they transferred him a year later for 1.7m euro.

For us the most important thing about FBM stats, is that we calculate the probabilities that a player is able to contribute to a specific team. Here are the probabilities that Dorsch is able to contribute to Eintracht Frankfurt:

Probability that Dorsch contributes to Frankfurt63
Probability that Dorsch contributes to the attack of Frankfurt72
Probability that Dorsch contributes to the defense of Frankfurt25
Probability that Dorsch contributes to the transitioning & build up of Frankfurt46

Case study: Watford

Many scouts wonder why their advice is being ignored by the higher ups. The reason is that whatever scouting report they have drawn up, their report fails to answer the most important question:

What is the probability that player X is able to contribute to the team?

The answer is a number between 0% and 100%. This answer is never given in any of the reports or presentation scouts give. That means that the decision makers have to calculate this answer based on the report the scout has provided. Of course, they never do this consciously. Yet, our brain makes these kinds of estimations unconsciously all the time. If a scout does NOT explicitly answer this question, the brain of the decision maker is going to make the probability estimation all by himself. In almost all cases, this estimation will be lower than the players the decision maker prefers himself. That is the reason why even the most successful scouts only have contributed to a handful of transfers. Most transfers happen for other reasons than provided by the scouting team.

It really doesn’t matter whether we are talking about data, video or live scouting. If the final report fails to answer the question about the probability that a player is able to contribute to the team, the decision maker is going to answer that question and probably in a less favorable way.

So let’s look at an example. If you are using Wyscout data as a data scout, how can you then answer this most important question: 

What is the probability that player X is able to contribute to the team?

First you need to build a model that transforms Wyscout data into probabilities. Bayesian networks are most suitable for this job, but there are other ways. We prefer to use Bayesian networks. Second step is to validate your model. For validation we have created a Bayesian network to transform Wyscout team data into team probabilities. We calculate the following four probabilities:

  1. What is the probability that a team is going to perform well?
  2. What is the probability that the attack of the team is going to perform well?
  3. What is the probability that the defense of the team is going to perform well?
  4. What is the probability that the passing game of the team is going to perform well?

Here are the results for the Premier League and Watford:

Validation comes from the 89% correlation (R2=80%) between the probability to perform well and the rank of the team. This is in line with this correlation in other competitions. So to be clear: 

  1. The probability of Watford to perform well is 38%
  2. The probability of Watford to attack well is 37%
  3. The probability of Watford to defend well is 54%
  4. The probability of Watford to pass well is 47%

The next step is to look at the individual players of Watford. Normally we would look at all the players (except the keeper), but for this exercise we only look at the most recent starting XI:

Again, these stats answer the following four questions:

  1. What is the probability that a player is able to contribute to the team?
  2. What is the probability that a player is able to the attack of the team?
  3. What is the probability that a player is able to the defense of the team?
  4. What is the probability that a player is able to contribute to the passing game of the team?

As long as a player has at least one of these four probabilities quite high, he is an asset to the team. Of course, if it is only one category, he is a specialist rather than a generalist, unless that category is the overall category.

Taking into account minutes played we can then calculate the contribution each player has made to the team probabilities of Watford:

The contribution of these ten players is:

Here one can see that although Sarr has quite weak data in Wyscout, his contribution to the attack of Watford is on par to what is expected of him.

One can also immediately see that Pereya is the weakest link. So let’s look at a replacement for Pereya. As this is an example only, I am going to use a replacement who obviously would be better suited than Pereya. The player I am going to use is Liverpool’s Mané.

Here we use our transfer model. This gives the following results:

Let me explain this. First we start with the probabilities of Watford and Pereya we have already seen. Taking into account minutes played, we subtract Pereya from the probabilities of Watford. What this means for Watford is that the probability to perform well remains unchanged, but the probabilities to attack, defend and pass well drop a bit. 

Then we look at the probabilities of Mané playing at Liverpool. As you can see, for all but defense, these probabilities are much higher than Pereya’s probabilities. But in part, Mané is playing well at Liverpool because he is playing together with other great players. That won’t be the case if he transfers to Watford. So we have to take into account that his performance will drop a bit. But how much? Fortunately, we have a Bayesian model to calculate precisely that by taking into account the relative strength of both teams and minutes played. To make it explicit:

  1. The probability that Mané is able to contribute to Watford is 87%.
  2. The probability that Mané is able to contribute to the attack of Watford is 98%.
  3. The probability that Mané is able to contribute to the defense Watford is 5%.
  4. The probability that Mané is able to contribute to the passing game of Watford is 52%.

What this would mean for Watford is that their probabilities also go up when we add Mané with his Watford probabilities to Watford as is shown in the final row. With Mané playing for Watford the new probabilities for Watford are:

  1. The probability of Watford to perform well is 45%
  2. The probability of Watford to attack well is 43%
  3. The probability of Watford to defend well is 46%
  4. The probability of Watford to pass well is 55%

GIven the correlation between overall team performance probability and rank, we can also see that Watford would rise to somewhere between rank 10 and rank 15 in the competition once Mané is playing for Watford. 
Rational decision makers use these kinds of models to calculate for every player they are seriously considering hiring what the probability is that the player is able to contribute to the team and what this means for the team. Once you have ranked all players according to their probability to be able to contribute to the team, you try to hire the best player available. That is how we were able to transfer Dalmau to Heracles for instance.

This is the kind of work that we are going to teach at the Football Behavior Management summer school at the VU-university in Amsterdam in juli 2020. Due to the current circumstances this will be an online course.

Wyscout data to Bayesian team ranking

Without live matches I found time to work on my third iteration of my Bayesian model to turn Wyscout data into Football Behavior Management (FBM) data. To be clear: we only accept correlation above 80% and R2 above 60%. So far all 7 competitions checked have a correlation of at least 80% and sometimes it goes up till an astonishing 95%!

The Wyscout team data we use are:

  • Average goals scored
  • Average goals conceded
  • Shots off Target
  • Shots on Target
  • Passes inaccurate
  • Passes accurate
  • Recoveries (low, medium, high)
  • Losses (low, medium, high)
  • Challenges failed
  • Challenges won

Als please note that we use team data for these correlations that can NOT be traced back to individual players. Unlike the correlations we get with FBM probabilities that are based on stats of individual players and that can be traced back to these players.

Premier League

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Liverpool19984707454
ManCity28193728353
ManUnited36677636651
Chelsea46676547451
Leicester56275606652
Tottenham65964546249
Wolves75961545850
Arsenal85664527947
Sheffield95452535450
Burnley105441444850
Southampton115244445149
Everton124945425747
Newcastle134433365147
Crystal144336365447
Brighton154149406248
West Ham163944435448
Aston Villa173533365246
Bournemouth183433355346
Watford193431335147
Norwich202125235944
Correlation with rank 93% (R2=88%) and points 90% (R2=81%)

Bundesliga

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Bayern16193738054
Dortmund25789697852
Leipzig35484706852
Leverkusen45380607350
Gladbach55269606149
Wolfsburg63951485450
Freiburg73750455749
Schalke83745415847
Hoffenheim93651408047
Koln103444435448
Hertha113443435546
Augsburg123033414646
Berlin133033384848
Frankfurt142846435450
Mainz152730345145
Dusseldorf162431315547
Bremen172131305744
Paderborn181831345444
Correlation rank = 91% (R2=82%) and points = 96% (R2=93%)

Bundesliga 2

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Bielefeld15380656751
HSV24675606849
Stuttgart34575547350
Heidenheim44456515649
Darmstadt53952495549
Aue63846495147
Kiel73855486048
Greuther83751505447
Hannover93545387650
Regensburg103434444447
St. Pauli113348465547
Bochum123246445647
Osnabruck133042435347
Sandhausen143042415250
Nurnberg153040385448
Karlsruher162833394648
Wiesbaden172833394748
Dresden182430404447
Correlation rank = 88% (R2=78%) and points = 93% (R2=87%)

Bundesliga 3

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Duisburg15164546150
Mannheim25065566150
Bayern II34851436445
Braunschweig44850525049
Unterhaching54757545450
Wurzburger Kickers64751515249
Ingolstadt74654535151
1860 Munchen84652515349
Hansa Rostock94555517351
Uerdingen104445445548
Meppen114351525248
Kaiserslautern124149485349
Viktoria Koln133852446346
Magdeburg143756515452
Chemnitzer153747485248
Zwickau163636484247
Hallescher173349445749
Munster183338435046
Sonnenhof192524284847
Jena201825295046
Correlation rank 79% (R2=62%) and points 85% (R2=73%)

Eredivisie

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Ajax15687697253
AZ25688707651
Feyenoord35075606850
PSV44972576650
Willem II54450505448
Urecht64172606449
Vitesse74160535949
Heracles83656535648
Groningen93554477452
Heerenveen103354466148
Sparta113343465147
Emmen123247396247
VVV132826305046
Twente142747406246
Zwolle152643386046
Sittard162636385446
ADO171926315046
RKC181532296045
Correlation with rank 91% (R2=83%) and correlation with points 92% (R2=85%)

Jupiler Pro League

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Club17074616353
Gent25574606651
Charleroi35463645051
Antwerp45356555350
Standard54959535849
Mechelen64448485448
Genk74452446147
Anderlecht84374596849
Zulte93647447546
Mouscron103349455748
Kortrijk113345455448
STVV123343376245
Eupen133033335447
Cercle142331315446
Oostende152224324645
Waasland162019235144
89% correlation with rank (R2=80%), 89% correlation with points (R2=79%)

Dutch Eerste Divisie

RankPointsFBM Wyscout scoreFBM Wyscout AttackFBM Wyscout PassingFBM Wyscout Defense
Cambuur16681716252
Graafschap26274645953
Volendam35567556449
Jong Ajax45473596847
NAC55064555951
Go Ahead64851535247
Excelsior74749525247
NEC84561565849
Almere94450497151
Telstar104448455846
Den Bosch113860526247
Jong Utrecht123845505046
Eindhoven133440415446
Jong AZ142848406544
MVV152730384646
Top Oss162533325646
Roda JC172234395046
Jong PSV182245376544
Dordrecht192031325445
Helmond Sport201720254845
91% correlation with rank (R2=83%) and 88% with points (R2=78%)