The personality of Sven Mislintat (type 4, SSLC)

Thanks to the insights of David of we determined that Sven Mislintat has a type #4 brain, the SSLC type. GPT agrees with us:

Sven Mislintat exhibits characteristics that are associated with Type 4. He is an idealist who holds strong personal principles, seeks recognition for unique contributions, is passionate about individual expression, socially engaged, outspoken, and willing to challenge authority. This aligns with Mislintat’s reputation as a highly respected and influential figure in the world of football scouting and recruitment. He has a history of being both cooperative and confrontational, which further supports the classification as Type 4.


Here are the reasons why Sven Mislintat scores high on autonomy based on his professional background and public perception:

  1. Professional Independence: Sven Mislintat has gained a reputation for being an independent thinker and decision-maker in the field of football scouting and recruitment. He has demonstrated a willingness to challenge traditional norms and authority figures, suggesting that he values his autonomy and is not easily swayed by others’ opinions or pressures.
  2. Innovative and Creative Thinking: Mislintat is known for his innovative approach to player scouting, often discovering young and talented players before they gain wider recognition. This ability to think outside the box and rely on his own judgment may contribute to a higher autonomy score, as he trusts his instincts and is willing to take calculated risks.
  3. Strong Analytical Skills: Autonomy can be associated with a preference for individual work and a focus on personal interests and ideas. Mislintat’s strong analytical skills and his ability to deeply analyze players’ performances and potential might indicate a higher inclination towards independent work and thought processes.
  4. Desire for Control: Autonomy is often linked to a desire for control over one’s work and environment. Mislintat’s high standards, meticulous approach, and dedication to his craft might stem from a desire to have full control over the scouting and recruitment processes, ensuring that his vision and values are upheld.

Please note that these are speculative reasons based on the limited information provided, and a comprehensive understanding of an individual’s personality requires a more in-depth assessment.


  1. Communication Style: Mislintat has a direct and no-nonsense style, this could be perceived as antagonistic by some, particularly in a field where diplomacy and tact are often required.
  2. Recruitment Decisions: Mislintat is making decisions that aren’t popular with certain factions within a club or the fanbase, this could lead to perceptions of antagonism.
  3. Conflict with Other Figures: As Mislintat is having disagreements or disputes with other figures in football, this could lead to perceptions of antagonism, particularly as those disagreements are public.
  4. Media Narrative: Sometimes, media narratives can shape perceptions of an individual. If there’s a narrative in the media painting Mislintat as antagonistic, this could influence how he’s perceived, regardless of his actual behavior.


  1. Strong Opinions: In his role, Mislintat may need to make and defend strong decisions or opinions about player acquisitions, strategies, or management. This can be perceived as belligerence, especially as he’s passionate and firm in expressing his views.
  2. Conflicts: As Mislintat has been involved in public disputes or conflicts, this could contribute to a perception of belligerence. This is especially the case as these disputes are frequent or high-profile.
  3. Leadership Style: Some people have a more confrontational leadership style, which can be effective in some situations but can also be perceived as belligerence.
  4. Media Perception: The media can significantly influence how individuals are perceived, especially in high-profile fields like football. As the media portrays Mislintat as belligerent, it could contribute to this perception, regardless of his actual behaviour.


  1. Passion for Football: Mislintat’s job involves identifying and recruiting talented players. His enthusiasm could be reflective of his passion for the sport and for discovering new talent.
  2. Job Satisfaction: As Mislintat genuinely enjoys his work and feels fulfilled by it, this would naturally lead to high levels of enthusiasm. His love for the scouting and strategic elements of his role might contribute to his enthusiasm.
  3. Personal Characteristic: Some people are naturally more enthusiastic than others, irrespective of their job or their situation. Mislintat might be one of these people who simply exude energy and passion.
  4. Communicative Style: As Mislintat communicates in an energetic and engaging manner, this could also be perceived as enthusiasm.
  5. Professional Success: As Mislintat has had recent professional successes, such as successful recruitment of high-performing players, this could also boost his enthusiasm level.

Grading players

Grading players comes with a myriad of pitfalls. To begin with: talent is a right skewed distribution like this:

What this means is that in professional football the probability of finding a player scoring a 10 is much smaller than finding a player scoring a 1 or a 2. Yt, if you look into the player database of football clubs you almost never see the above distribution. What this means is that the scouts in that club are overestimating the players they are grading.

There are some mitigating circumstances. For instance, for most clubs it is too much work to grade players with a 1 or a 2. So they simply skip those players to save time. Skipping badly performing players is a risk though, because now it becomes harder to check whether the grades given by the scouts match this distribution. So a club that has the time and budget to also grade badly performing players really ought to do so as it is a check on the quality of their scouts. Because when you see a scout only grading players with a 7 or higher, you know that the judgment of that scout has very little value.

Football Behavior Management (FBM) stats follow the above distribution.

Relative or absolute grading?

So the second pitfall is whether you grade the player relative to his current team or the team you are scouting for, or whether you grade him with a 10 being the best player who has ever played and a 0 being the worst player ever played. Relative grading is much easier than absolute grading. But absolute grading has the advantage of being less time constrained. If you do relative grading then you ought to decide whether you grade the player for his current team (or even match) he is playing in, or whether you grade him for the team you are scouting him for, his potential future team. A player who is currently playing in an easier league with more time and space might score a high grade for his current team, but a good scout might see that he would struggle in a team in a tougher league with less time and space and for that reason give him a lower grade.

If you grade a player as he is playing in his current team or even for the actual match you are seeing, then the problem arises that it becomes much harder to compare this grade to other players graded playing for other teams in other matches. So if a club chooses this kind of grading, then they need to be very watchful of people not generalizing the same grade as meaning the same level of performance. This is very hard for people, even if they are conscious of the issue. Because if player X scores a 8 and player Y scores a 9, it is very hard for our unconsciousness to understand that player X is actually the better player as he scored the 8 in a tougher league than player Y scoring a 9.

So it is much better to grade players for the team you want them to play in in the future. But even then the grade is time constrained as the team you are scouting for might change over time. Players that scored an 8 for the first season, might be a 7 in the season thereafter and even only a 6 two seasons later as the team becomes stronger and stronger. If the club has the right infrastructure to keep track of all the judgments of players, in almost all cases, older grades are never adjusted for a team that has been developed and has become stronger. What this means is that a decision to hire a player might have been the right decision in the first season turned out to be the wrong decision in a later season because the club failed to take into account that the majority of the reports for that player were made for a time period where the team was weaker.

To avoid all of these pitfalls, FBM stats are absolute.

One number to rule them all

Modern football requires high quality decision making at high speed. In order to speed up decision making, a single grade helps a lot. Yet, a single grade can lower the quality of decision making. For a single grade a club first needs to decide how to deal with the difference between specialists and generalists. Most players are specialists. If a single grade covers every aspect of playing football, a single grade has a strong bias in favor of the few generalists. This doesn’t have to be a problem, as long as the club is aware of this bias or prefers generalists over specialists.

One can use a single grade to cover specialists. For instance if the grade is simply only for that aspect where the player excels in. For most defenders (but not all!) their single grade would reflect their ability to defend. For most strikers (but not all!) their single grade would reflect their ability to score goals. Yet, even if this is setup correctly, it will still lead to a lot of confusion not in the least for the unconscious mind of the decision maker. Again, if player X scores a 8 and player Y scores a 9, most decision makers will assume, consciously or unconsciously, that player Y is the better player, whereas in reality player Y might be an excellent defender and player X a very good striker, with the striker being the better player as scoring goals is much harder than defending.

So the best solution is to have a single grade for the player to speed up decision making and to have this single grade cover every aspect of playing football. Yet, to also have subgrades that tells the decision maker how the player scores in various aspects of football. There really ought to be only a very small set of subgrades, because counterintuitively more data means less information. The more data you have, the more you can prove. But the more you prove, the less value your proof has and the higher chance that what you think is valuable information in reality is nothing but confirmation bias.

Always translate grades into probabilities

Because speedy decision making is so important in modern football, grades are often used to steer the club into action. Many clubs use a grading from A to E, whereas an A grade means try to immediately sign the player and an E grade means to never hire the player. The pitfall here is that scouts find it hard to see the difference between an A grade and a B grade. Or between a B grade and a C grade. Everyone sees the difference between an A graded player and an E graded player, but in reality a club never has to make that decision.

Another issue is that coming up with an A to E grade, or even a grade from 1 to 10, makes it hard to measure how well the scouts and decision makers at the club are at predicting the future development of the player. For these reasons, it is important as a club to know how to translate grades into probabilities. For instance in the following way:

  1. An A grade means that you find it highly likely that this player will be a success at the club. The probability of success for this player in your estimation lies between 80% and 99%. Even if all the other scouts within the club are against hiring this player, you still think that the club ought to hire this player directly.
  2. A B grade means that you find it likely that this player will be a success at the club. The probability of success for this player in your estimation lies between 60% and 79%. But you would only recommend the club to hire this player if there is no A graded player as an alternative AND the majority of the other scouts of the club have given this player an A or a B grade.
  3. A C grade means that you find it unlikely that this player will be a success at the club. The probability of success for this player in your estimation lies between 40% and 59%. Even if most of the other scouts of the club are in favor of hiring this player, you still have your doubts and would see hiring this player as an emergency measure.
  4. A D grade means that you find it highly likely that this player will be a success at the club. The probability of success for this player in your estimation lies between 20% and 39%. You feel that hiring this player is a mistake even as an emergency measure.
  5. A E grade means that you find it highly likely that this player will be a success at the club. The probability of success for this player in your estimation lies between 1% and 19%. No matter what other scouts think about this player, for you it is clear: never hire this player.

When you use a system like this, this makes it easier for the scouts to give players the right grade. More importantly, this enables the club to measure how good the scouts are in predicting the future development of the player. For a club it is very important to know who the best predictors of the future development path of players are within the club. Once that is known it becomes possible to create a risk management model that uses the success at predicting the future development of players as weights so the club can actually calculate the risks and opportunities that a specific player has.

Potential or progression?

Grading players in terms of their potential is a pitfall. “Potential” means that something is possible. Statistically speaking everything you can think of has a chance of becoming real. In that sense every player has a potential of a 10. Much better than looking for future potential is looking at realized progression in the past and extrapolate that progression into the future. Besides grading players, also predict how their development path and career will continue from the moment of reporting on.

Did you watch that player?

Fans, if they disagree with FBM stats, often ask me: “Did you actually watch this player?” The short answer is: “No!” That is not my job. My job is to make an assessment of how likely it is that a player is going to contribute to the team. Yet, if you are not a good pro-scout chances are that while you think you watch a player, in reality you fall into one or more of the following pitfalls:

  • You see potential, whereas a good pro-scout sees progress and extrapolates that progress. “Potential” just means that there is a possibility of this or that. Everything you can think of can happen potentially. Even I could, potentially, be the next superstar striker of the Dutch national team. The probability of that happening is extremely low, but there is a chance. Progress is something that you can actually see. An extrapolation of progress in the form of a concrete prediction can be measured in terms of whether the prediction was correct or not. Seeing potential is meaningless.
  • Confirmation bias. The world contains much more data than we take in through the senses. This is called the bottleneck problem. The most likely solution is that for the most part we see what we expect to see. Most people see what they expect to see when they look at a player. This goes for you and me, but also for top executives of some of the biggest clubs I worked for in my experience.
  • You see a great pass. But did you see what kind of pass that was. Did you see whether that pass was the right decision? Was the decision made in time? Was the decision made with less space and time? If you see a player under pressure, you are not looking at the player as a good pro-scout. Are you at the same time capable of seeing that player make the same decision, execute the decision and get the same result, in a different team with different team mates?
  • Are you looking through the good to see the bad when the player plays really well? Are you looking through the bad to see the good when the player plays really badly?
  • Can you see whether the mistakes a player makes are due to a lack of technique or a lack of game intelligence?
  • Do you look at how the player drinks his water bottle during injury time?
  • Do you know that a video scout watches quite differently than a live scout?

To sum up, no I don’t watch players, but chances are neither are you. Unless you are a good pro-scout. That is the reason why we teach the Football Behavior Management course with the participation of live and video scouts.

Always combine live, video and data scouting

Having said that, I am all in favor of always using every available source to determine how probable it is whether a player is going to be a success at the club. In fact I have developed a risk management model that measures the predictions of every scout and executive to see who is the best at predicting success. After one season this model uses the results to weigh the opinion of each source, so that the overall conclusion is more and more influenced by the best predictors.

Often that is data, but not always. I have met and worked with some amazing pro-scouts who were able to predict the careers of players and see them be a success at a different club playing in a different role. It’s wonderful to watch that process in action. Yet, I have also seen many pro-scouts who did not meet this level of mastery. At the same time, I also see some amazing work being done by fans on Twitter. Yet, the chance of seeing a master at work on Twitter is a lot smaller than within a club. And in a club that is already quite rare.

So no, I didn’t watch this player you are upset about. I would not know what to watch for. In all probability: neither do you unless you are a good pro-scout. In that case I am happy to learn from you. My job is to make sure that unintelligent football statistics is turned into a prediction that is on par with the kind of predictions good pro-scouts make unconsciously: is this player going to be a success or not.

Team dynamics based on brain types

Some teams are friends teams while other teams are fighters. There are many reasons for this difference, but in large part it has to do with the personality of the players in the team. Cybernetic Big Five Theory (CB5T) is the neuroscientific way to map this. When you do so, you find that there are basically nine different brain types. In the same way that players have body types, they also have brain types. Their brain type determines:

  1. Their personality.
  2. How they deal with emotions.
  3. How they learn.
  4. How they motivate themselves.
  5. How they typically behave.

Brains have three different modes of operation: stress, relaxation and neither stress nor relaxed. Players behave differently if they are stressed than when they are relaxed. In short: when players relax they behave in a positive way, but if they stress they behave in a negative way. This doesn’t mean that relaxation is the best way to get players to reach peak performance. Although relaxation helps some players reach peak performance, in reality it depends on the brain type and their role & position on the pitch. Yet, when players know their brain type, it helps them perform better. A good example of this is Oscar Fraulo:

Team dynamics

When you combine the brain types of the players of the team you get a dynamic interaction that is quite predictable and explains why the performance of teams can differ widely week to week. Win against a better team the first week (being praised by the pundits for their fighting spirit) only to lose to a much weaker team the next week (and being scowled by the pundits for lacking a fighting spirit).

Stress and relaxation play an important role here because of how the brain triggers very different behavioral patterns in times of stress than in times of relaxation. A winning streak can make a team too relaxed in the same way as losing too many matches can make a team too stressed.

One needs to combine this with the amount of time and space a team gets from the opponent. An opponent that pressures the team high, can create just enough stress in a team that is already too relaxed so that the team still performs well, whereas the same team might do much worse if the next team gives them plenty of time and space.

Cybernetic Big Five Theory

At the highest level CB5T behavioral patterns fall in four different areas:

  1. Social behavior.
  2. Efficient behavior.
  3. Creative behavior.
  4. Predictive behavior which in football is called vision.

Here is an example of how a friends team looks depending on how stressed or relaxed they are:

If the team is dysfunctional, i.e. the team structurally fails to achieve its goals, then this chart looks like this:

At the level of the Big Five, this team looks like this (healthy version):

At the lowest level of detail, this friends team looks like this:

As you can see, this team is nog a fighting team (a low score on Belligerent), but instead this is a team of friends who care for each other and are polite. Such a team often does really well.

When you want to understand what kind of team your club has, contact us so we can give you a detailed presentation of how CB5T and brain type work and how you can use this model to perform better and find players who fit the team better.

FBM Finishing Points

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

How FBM Finishing Points work

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

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

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

How to make use of FBM Finishing Points

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

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

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

How does FBM replacement values compare to TransferMarkt?

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

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

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

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

Comparing FBM replacement values to TransferMarkt

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

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

Validation of the FBM player statistics

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

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

The FBM Bayesian Transfer Model

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

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

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

Basic principles of the FBM transfer model

The basic principles of the FBM transfer model are:

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

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

Factors in the FBM transfer model

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

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

What the FBM transfer model does

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

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

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

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


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

Predicted successPredicted failure
Actual success154
Actual failure11

There are two caveats:

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

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

The FBM Creativity stat

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

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

What is creativity?

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

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

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

Two kinds of creative players

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

Éverton Ribeiro is an example of a Type II creative player

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

Complexity and predictability

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

II) Create a Viable Systems Model of your club

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

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

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

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

III) Hire one player less

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

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

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

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

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How to read a FBM chart

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

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

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

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

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

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

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

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

The distribution of probability

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

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

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

How to read these distributions

There is a very simple rule to reading these charts:

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

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

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

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

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

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