If a team loses it is only natural to look for the cause of the defeat. But cause and effect do not really exist. They are abstract concepts that need a justification to be used. In football no such justification can be found. Instead in football we have to work with the probability of a certain sequence repeating in the future. That might sound quite a lot like cause and effect, but it isn’t.

As the following tweet attest, more and more people are becoming aware that cause and effect might not be very useful in football. So let me explain what philosophical issue there are with cause and effect.

Criticism of cause and effect starts in the eighteen century with David Hume. He defines cause and effect as follows: if every time we experience an event B, there has been an event A just before it, the A is the cause of B and B is the effect of A. If Pepe tackles Messi and Messi is injured then Pepe is the cause of Messi’s injury.

At least that is what seems to follow from Hume’s definition. But Hume warns us against such a conclusion for he states that a cause if only a cause if the effect is always preceded by the cause. As not every time Pepe tackles Messi he is injured, we can doubt whether Pepe’s tackle is the real cause of Messi’s injury. Also, Pepe’s tackle is not the only thing that has preceded Messi’s injury. Maybe Messi has been tackled by other players earlier in the game. Or maybe he already started the match injured. The problem with causes is that we can always find more causes than we want and we can always go further back to look at the cause of the cause. Maybe Messi was injured by Pepe’s tackle because Pep Guardiola pushed him to take more risk attacking. But maybe Pep only did so because Barcelona was behind. And maybe Barcelona was behind because Messi missed a big opportunity. So maybe Messi is the cause of Messi’s injury.

In the nineteenth century cause and effect is criticized by Friedrich Nietzsche. Nietzsche shows that there is an inherent mistake in the concept of cause and effect. As the effect has to happen after the cause, cause and effect can’t be simultaneously. For if cause and effect would be simultaneously we would be unable to tell what the cause is and what the effect is. As Louis van Gaal liked to say: “Are we so good or are they so bad?”. Is Messi scoring so easy because Messi is very good or is Messi scoring so easy because the defense is so bad? But if cause and effect can’t be simultaneously, then there has to be time in between cause and effect. What happens in the time between cause and effect? If you don’t know how are you able to connect the cause to the effect. But if you do know what happens in between, then you are coming up with a new cause and effect pair. And then we ask the same question again: what happens in the time between the new pair of cause and effect. In football terms: Messi is able to score because he overtakes Pepe. But what happens in the time between overtaking Pepe and scoring? No matter the answer, we can zoom in to an even smaller time scale. Nowadays we register 2.500 actions during a match, but with more precise measurement we might be able to increase this to 25.000 actions. The smaller the time scale the more irrelevant the cause and effect pairs become. But at a larger time scale we run into Nietzsche’s argument about the time in between.

In the twentieth century Russell expands Nietzsche’s argument. Influenced by the newly discovered quantum mechanics and relativity theory, he states that even if take cause and effect to be connected in time, then both cause and effect as an event on it’s own take time. Messi’s overtaking Pepe take some time. Russell notices that the first half of the cause is ineffective in the same way as the second half of the effect. So we can subtract those ineffective halves of the cause and the effect. But we can keep doing this until there is nothing left of the cause and effect

Causation implies absolute truth. If we don’t know the cause absolutely, we cannot speak of cause and effect because the effect might not always be preceded by the cause if things are uncertain. But quantum mechanics show that everything has a measure of uncertainty. The same goes for football: there is so much uncertainty within football that it is overly simple to think in terms of cause and effect. Every time you indicate that you know the cause of something, you are actually proposing a theory. That theory is always uncertain due to the underdetermination of the theory by the data.

The probability of a sequence repeating in the future

It is very hard to live and work without cause and effect. Cause and effect talk is very much ingrained in our daily lives, inside and outside of football. In fact, our brain is hardwired to see cause and effect. Research has shown that whatever is the most salient detail at the same time as we experience the effect, is taken by the brain to be the cause of the effect. We see Pepe tackle Messi and we see Messi falling, so our brain concludes that Pepe’s tackle is the cause of Messi’s fall. But we have already seen that cause and effect can’t be simultaneously. So our brain assigns cause and effect incorrectly. Evolutionary this is a great strategy. To survive you don’t have to be correct, as long as you are safe when you are wrong. Our brain likes finding cause and effect so much that whenever you find a cause you are rewarded with a dopamine rush that is also used to reward you whenever you have achieved one of your goals. So cause and effect talk is ineffective because (a) the concept is incorrect and (b) our brain is biased to interpret the wrong things as if they were causes.

Nevertheless, almost everyone wants to talk in terms of cause and effect. Fortunately, that want can be satisfied by a healthy alternative for cause and effect talk. Instead of talking about how A causes B, one can talk about the probability of B following A. We experience sequences of events multiple times. The more we experience these sequences, and the more other people also talk about the same sequences happening even though you personally did not experience them, the more probable it becomes that these sequences repeat themselves in the future. This is actually how the brain learns game intelligence through associative learning.

So while people feel the need for cause and effect talk, it is much better to stop talking about cause and effect and replace all that talk with talking about the probability of a certain sequence repeating itself. In terms of football: stop talking about the causes of the loss if you lost a match, but instead look for patterns of events that preceded the loss and calculate how probable it is that those patterns repeat themselves in the future. 

Our brains are Bayesian brains. They are best described as biocomputers that use Bayesian statistics to calculate how probable patterns repeat themselves. The more we are able to train players to think in terms of the probability of a specific pattern repeating itself, the easier we make it for the player to increase his game intelligence. For example: the next time he looks at where the opposing players are currently in the match, the easier he can spot different scenarios of how the game will evolve and what probability each scenario has. That is much better than trying to find a single cause. First of all because causes don’t exist. Secondly because football is way too complex to be described in a simple cause and effect. And thirdly because our brain tends to pick up with wrong events as causes. 


There are two modes of communication (Skinner, Verbal Behavior 1957). Either you communicate to clarify or you communicate to influence. The basic principle of communicating to clarify is to be as specific as possible. To clarify, you need to make sure that you do not delete, distort or generalize relevant details. Because clarification entails a lot of details, people don’t like this mode of communication. Details are often antecedents for punishments. That is why people feel uncomfortable when you clarify matters. They fear that all these details either proof that they made mistakes or that they need to reproduce these details as if they were still in high school. Of course there are techniques like rapport building techniques that lessen this negative attitude towards details. Yet the need to use such techniques underlines the basic negative attitude to details. So as a coach you often want to clarify a lot to your players. So you need to take into account that your players are not too happy on details.

People love influence as a mode of communication. This is ironic, because communicating to clarify can be seen as a much more pure and honest mode of communication. Whereas communicating to influence people is much more manipulative. Nevertheless, people’s attitude towards communication to influence them is often very positive. The main reason for this is that when you communicate to influence people you communicate in a way that seems specific, but is not specific. By deleting, distorting and generalizing relevant details, you create a lot of room in the way the listener understands you to fill in the blanks based on their own experiences. That way it seems as if you really connect to their world and their world view, even if you don’t. Besides deleting, distorting and generalizing relevant details, there are also a number of hypnotic language patterns that increase the level of influence your communication have when you use them.

So as a coach you need to (a) clarify details so your players know what to do and (b) influence them so they go out and do it. Those two objectives require two different methods of communication. Often, people master only one of those two and think that the other one isn’t needed. Either a coach thinks that if his players understand in detail what he wants them to do, then that understanding will make his players do the correct thing. Unfortunately, that is not the case. These coaches often talk too much. The other case is where a coach is a great influencer, but forgets to actually clarify the relevant details to his players. Now his players are all pumped up, but don’t have a clue what to do.

In many clubs these two objectives are achieved by having an assistant communicate all the details and then have the manager influence the players to go out and do whatever the assistant has explained to them. Yet, ideally a coach is a master in both modes of communication. As talking is for a large part an unconscious activity, if a coach doesn’t use these two modes of communication naturally, he needs training to make sure that his unconsciousness is able to switch between these two modes of communication fluently.


Complexity is a measure of how much variation there is in a system. Variation is a measure of how many variables there are and how many different values these variables can have. The more variables you have, especially when many of these variables can have many different values, the more complex the system is. A different way to explain complexity, is noting that the longer it takes to describe all the different options a system has, the more complex the system is. (Source: An Introduction to Complex Systems Science and its Applications by Alexander F. Siegenfeld and Yaneer Bar-Yam 2019)

Given how much you need to write about football before you have said it all (if that is even possible), football is a game with a very high level of complexity!

Our interest in complexity lies in our desire to deal with complexity. Cybernetics is about communication and control in man and machine (which is actually also the subtitle of the first book on Cybernetics by professor Wiener). As said, football is a very complex game and the question is how do we control this complexity. The first step to take control is to define the system and the environment. We are completely free to define anything in football as a system. As we have very little control over the opposing team, it is best to define our own team as the system and let the opposing team be part of the environment. The question then becomes how can the manager regulate the way the team (or the system) deals with complexity.

Simplifying complexity

Obvious if you take both teams as the system, there are more variables than if you only look at your own team. More variables, means a higher level of complexity. But because we can’t control the opposing team directly, we chose to look only at our own team. In fact it is even easier to start with a team of only one player.

So let’s start with a single player. Imagine a match with only a single player. What can he do? Basically he can shoot the ball and run after it. Or he can dribble with the ball. There is not much else to do. So the complexity is relatively low. But as soon as we add another player, the complexity increases. Now they can pass to each other. One can give an assist as the other player scores. So team members increase complexity. Team members are variation amplifiers.

The more players we add to the team, the more complexity rises. In fact it rises exponential as all the possible different relations between the players add to the level of variation. A single team of eleven players without an opponent has the highest level of complexity. That sounds counter-intuitive. Doesn’t the opponent make the game more complex? The answer is: only if you make them part of the whole system. If the opponent is part of the environment, then the opponent is limiting the options of your team (the system in focus) and thus complexity decreases.

The opponent makes it more difficult to score. Obviously, It is much easier to score if there is no opponent on the field. Without an opponent it is very easy to score. But writing down all the options a team has, if there is no opponent, has become a much bigger task, than writing down all the options a team has when there is an actual opponent. Your opponent will diminish the options your players have. As such opposing team members are variation attenuators.  Your opponent only increases the complexity if it is part of the system. But because we have excluded the opposing team from the system, we only look at what the opposing team means for the options our team has.

Now let’s add an opposing player to our experiment. Immediately, it becomes clear that in this situation the team has less options to score. For one, they can’t run the ball through the opposing player as that would be a foul. The team has to play around the opposing player. In fact, by adding a single opposing player we proof that opposing players are indeed variation attenuators. They diminish complexity. The more opposing players there are on the field, the less options our team has. The more our team is forced to play in certain predictable patterns. And the more predictable our team becomes, the more difficult it becomes to score. The reason why it is difficult to score in football is not because the game is too complex, the reason is that the game is not complex enough.

If you are not yet convinced than reverse our little thought experiment. Now we start we only one player for ourside against eleven opponents. Scoring becomes almost impossible as the eleven opposing players limit the option of our one player to next to nothing. Again, we can increase our odds by either adding players to our own side as they are variation amplifiers. With more players our team gets more options, complexity rises and our chance to score increases as well. Or we could decrease the number of opponents. With less variation attenuators our options increase and again complexity rises together with our chance to score.

Real variables

Let’s see if we can calculate the level of complexity for a single player on an empty pitch. We will consider the following five variables (even though there are probably many more to consider):

  • Position
  • Direction
  • Speed
  • Timing
  • With or without the ball

To calculate the complexity we have to determine how many different possible combinations of these five variables there. To do that we first determine how many possible settings each variable itself has. “With or without the ball” is easy as it is a binary variable with only two options: yes or no. For position we have to create a grid on the pitch. Let’s go for the smallest pitch allowed which is 100 by 64 meters. If we build a grid out of a single m2 then we have 100*64 = 6400 different positions on the field. For direction we have 360 degrees around us. But it is probably fine grained enough to use sections of 30 degrees so we can work with 12 directions as if we were to use a clock to determine our directions. Timing goes really fast in football so it is probably best to use a time scale of seconds. So in a football match we have 90 * 60 = 5400 seconds at least. Finally we have speed. Let’s go with 11 different speeds going from 0 m/s to 10m/s which is very fast.

So this gives the following number of options for a single player with or without the ball on an empty pitch. The formula is:

Options = position * direction * Speed * Timing * Ball

Which gives us:

6400 * 12 * 11 * 5400 * 2 = 9.123.840.000 options for a single player on an empty pitch. Over nine billion options is way to many options for people to handle conscious or unconsciously. That is why we need to decrease this number of options to make an interesting game that can actually be played by human beings.

Now if we add a single opponent, you can see the number of options for the first player drop only because there is 1 less position where he can be. Now our original player has the following number of options:

6399 * 12 * 11 * 5400 * 2 = 9.122.414.400 So now we see that by adding 1 extra inactive opponent, our original players has over a million option less. Now let’s add the other 20 players completely inactive with each player only taking up 1 space. The formula then becomes:

6379 * 12 * 11 * 5400 * 2 = 9.093.902.400 So that 29 million less options for our original player. Here you can see mathematically how adding more opponents actually decreases complexity. And this does not even take into account rules like off side where the opposing players can actively make large parts of the pitch inaccessible. Nor does it take into account all the seconds that the opposing team has the ball and the “with or without the ball” variable drops from 2 options to 1 option immediately limiting all available options for our original player for all those seconds by half.

With every second ticking away in a match, the number of options available to players decreases. One of the reasons why a formation helps players perform better is that a formation limits the number of positions where players can be, thus decreasing complexity.

Complexity and space

So far we have considered the whole pitch as the boundaries of the system. But we can actually look at smaller parts of the pitch. What goes for the whole pitch, goes for every part of the pitch: the less opponents there are in any given space, the more options you get and the more complex the game becomes. And the more complex the game becomes, the more chances you get to score. So once you have divided the pitch into zones, it becomes important to make sure that your team has more players in the important zones than your opponent. The better the ratio is between your players and the opposing players, the more options your players have. Complexity rises, but so do the chances to score.

This only works of course if your players are able to handle the increased complexity. A well known phenomena in football is that if a player gets too much space and time, he starts to overthink the situation and blunder and lose the ball. The problem here is that the brain and the unconscious mind are suddenly confronted with more options than it can handle. The brain and the unconscious mind need consciousness to help out with the increase in complexity. Unfortunately, for some players their conscious mind is not trained to solve this level of complexity and a blunder happens.

If your team is not in possession of the ball, you want to close down the space of your opponent. The less space your opponent gets, the less options he has, the less complex the system becomes and the more predictable the behavior of your opponent becomes. Which, in the end, lowers his chance to score. And makes it easier to recover the ball.

Yet, the moment your team captures the ball, space works the other way around. Then you want less opponents in your space so you get more options. That is why Ten Hag, for instance, wants all players to spread out when the ball is lost, except for the few players who are needed to recover the ball. That way, once the ball is recovered, the team has way more options. The game has become a lot more complex for the opposing team and your team has an increased chance to score.

Then, once you run into a packed defense you want as many of your own team mates closeby and into the same space that the player with the ball occupies. This makes the ratio between your players and opposing player more even and increases complexity.  Because if in the limited space where the ball is, the opposing team has more defenders than your team has attackers, these defenders diminish your options quickly, making your play more predictable and decreases your chances to score. The whole idea of parking the bus is to always have more defenders so to decrease complexity, make the game more predictable and decrease the chances of conceding a goal.  At the same time, you don’t want your players to be so close together that they start to limit each others options. Players still need room to manoeuvre and run.

Here is an example of six attackers flooding a relative small space to increase complexity, making it harder to defend and easier to score:

Complexity also explains why sometimes it looks as if a team playing with ten players, because a player was sent off the pitch, is easier than playing with eleven. Sometimes, the manager even comments on this after the match by saying that he ought to play more often with ten players as it seems as if the team was playing better with ten players than eleven. In fact, this not only seems to be the case, but often this really is the case. Complexity explains it. The team has a lot less options with ten players against eleven. So the complexity has decreased for the team. If the team was struggling with the level of complexity when they were still with eleven players, it could well be that now with the decreased level of complexity they can manage it.

To be clear: for the opposing team, who still play with eleven players, the complexity has risen. This is why it is easier to score against a team of ten players than against a team of eleven players. But only if the players of this team are able to cope with the increase in complexity. Sometimes this is not the case.

Complexity and risk

Ten Hag’s approach of attacking by making the space even smaller and adding more attackers into that small space, is one way to break through a defense by increasing complexity. Another example is to add one or more creative players to the lineup. 

Why is a player called creative? Because he has more diversity in his play. He is less predictable than other players. The reason is that he has more variation. Adding a creative player to the team increases the complexity of the system. That is the reason why a creative player increases the chances to score. 

But the more options you have, the more that can go wrong. Increasing complexity, also increases your chances to score. But it also increases the risks you take. For not only are there more ways in which things can go wrong, often the additional options creative players have, are also harder to execute. So the creative player needs more game intelligence and more technique than the average player. For all players, it is important to check to see whether they have a positive error ratio, but for creative players even more so. For a creative player might also increase the risk the team runs to an unacceptable high level.

Ten Hag’s strategy of spreading out on ball loss and concentrating attacks, does increase the chance of his team winning. It also increases the risks as the higher level of complexity might bite him. That is why you see him sometimes lose a game out of nowhere.

The difference between complexity and difficulty

One can argue that scoring in a match without an opponent is very easy. And they would be correct. But the fact that scoring is easy with no opponent, doesn’t mean that it is not complex. For there is a difference between complexity and difficulty. Activities can be both complex and difficult. Or they can both be without much complexity and difficulty. Or they can be either complex and easy or having less complexity and yet be difficult to execute.

Complexity has to do with how many options you have. Not whether those options are easy or difficult to perform. In football there is a difference between decision making and executing decisions. The more complex the situation, the more options you have, the harder it becomes to make a decision. The less complex the situation is, the less options you have the easier it becomes to choose. In the example of a match against no opponent, even though most complex, it is easy to choose, because although there are so many options, a few options present themselves as most attractive because they are the easiest to perform. For instance, dribbling to the goal and score. But this is only the case if you think about the match as a single action. As soon as you understand that the players of your team have to enjoy themselves for 90 minutes, it becomes clear that they will entertain and probably do a lot of the other, less easy, options in that situation.

The difference between difficulty and complexity can also be seen in the following example:

Let’s do another experiment. This time taking a penalty. But before we take the penalty, we are going to strongly limit the number of options as to where to shoot by boarding up the goal with wooden planks. We will only leave a small hole in the middle of the goal just big enough to let the ball through. This is a real life example of a system with a very low complexity (as there is only one option to score), yet where the chosen action is very difficult to execute.

Now we can expand this example by creating a second hole in one of the corners of the goal. We make the second hole a bit bigger than the first hole so that the difficulty of the exercise stays the same. Although the difficulty is the same as in our previous experiment, the complexity has risen as now there are two options. And this proves that there is a difference between the difficulty of the execution of the decision and the complexity of the decision itself.

Of course we can continue to create more holes in the goal to increase complexity. Or we can remove all the planks and put a goalkeeper in the goal instead. Suddenly, a whole lot more options are available, complexity has risen, but it has also become easier to score. Some players will turn out to be better at handling the complexity of taking a penalty in the same way that other players will turn out to be better at executing the penalty. Ideally, you have a player in your team who is both good at dealing with the complexity of a penalty and at the execution of a penalty.

Example of complexity in football

Here is another great tweet that explains complexity:

What is so great about it is that it explains that if you give Messi space, he also gets time to think and he becomes very dangerous. But why does he become so dangerous if he has time to think? It is because he now has more options (more variation) and time to go through these options. So the complexity for Messi rises. Lesser players might be overwhelmed by this increase in complexity. But Messi is such a good player that he uses the increased complexity to become a lot more dangerous. So this is another great example of why more space and time increase complexity.


A concept is an abstraction that we use because it makes it easier to communicate. Although it seems that the concept of “concept” is easy to grasp, there are quite a few pitfalls with concepts. Naively there is the risk of taking the concept to be the same as a real thing as both are nouns. Consciously it is easy to understand that a concept is an abstraction. Nevertheless, the unconsciousness finds it much more difficult to understand that concepts are abstractions and not real things.

Examples of concepts that play an important role in football are:

Whether a concept is useful in football depends on how helpful it is for instance in training players, understanding opponents play or increasing hiring the right players, in relation to how much the use of the concept leads to misunderstanding, over- or underestimation of opponents or hiring the wrong players.


Convergence is the number of people who come to the same judgement. A high convergence means that a lot of people agree. A low convergence means that there is a lot of disagreement. See objectivity for a more extensive explanation of convergence.


How do you proof that you analysis is correct? Well, first of all a proof might be too strong a demand. Maybe, it is enough to have an analysis that is probable. For normally a proof would need a double blind random test which is not feasible within football. A good alternative would be Bayesian statistics where you calculate how probable your analysis is. The difference is that when you want to prove something you calculate how strong the evidence is in light of your theory.. But when you calculate how probable your theory is in light of the evidence.

Your analysis is a kind of theory. You want to make a point with your analysis. If there is no point in your analysis then it is probably a description of the match. Yet, as soon as you draw conclusions from your analysis you have a theory and you claim that your theory is supported by the data, i.e. the evidence. In football, unlike when you try to prove something, the data is not in question. Sometimes there is a discussion on whether someone has counted for example  the correct number of passes. But most of the time, there is no discussion about the data.

But there is a lot of discussion about what the data tells us. This is exactly what is covered by the Bayesian philosophy: you want to show how probable your theory is given the data (the evidence).

One of the ways in which you could do this, is by showing that your analysis is a true reflection of what really happened on the pitch. This is tricky though. First of all, a reflection pretty much sounds like a description of the match rather than an analysis. But what is worse, is that it would make you a realist. Now, being called a realist sounds like a compliment. And a lot of philosophers call themselves realists. But we are talking here about the philosophical theory called realism. Realism claims that our true knowledge mirrors nature. The problem with realism is that it has many faults. As it turns out it is quite difficult to make sense of the idea of truth. Truth is absolute and we can’t find anything that absolute except within mathematics and formal logic. Everything else has a measure of uncertainty and that uncertainty makes it impossible to find absolute truth. Yet, as soon as you no longer have absolute truth, realisme also becomes impossible as it is no longer possible to proof that your ideas are realistic due to the fact they have a measure of uncertainty.

Another problem with realism is that any useful form of realism makes use of atomism while our language and meaning is holistic. So it is impossible to express isolated atomic parts of reality in our language. That makes atomism also doubtful.

All of this is highly relevant for football because most football analyses treat football as something that can be analysed in smaller parts. This idea that you can understand individual atoms of reality, so that in that way you can understand football, is highly unlikely. In football everything is connected to everything else. In that sense like our language and meaning, football is also holistic.

Fortunately, there are alternatives to realism. These alternatives are called anti-realism. The most important anti-realistic theory is pragmatism. Again, pragmatism sounds more pragmatic that it is. We are talking here about philosophical pragmatism. Philosophical pragmatism is defined as any theory that also cares about other values that the truth. Pragmatist don’t want to figure out how things really are, because they find it more interesting, for example, to figure out what works best. They are interested to find out how things hang together, rather than how things really are. In terms of football: pragmatist are looking for useful holistic patterns of play that help them achieve their goals rather than want to know the truth about the game.

The only measure of success of correctness for pragmatists is whether they achieve their goals. So any football analysis that leads to the team winning is correct. Any football analysis that leads to winning bets is correct. Any football analyses that leads to players getting better efficiencies are correct.


Most people have heard that correlation is not causation. Yet, almost no-one has heard that correlation is not correlation. Technically, correlation only establishes a measure of how much two lines are similar to each other. This measure of similarity is not even undisputed as it uses the least square method of the regression of the two lines involved.

Here is issue one as described by Francis Anscombe, a famous statistician. The following four graphs all have the same regression line even though the data points are wildly different:

As you can see only the regression of the first graph (the blue line) seems right to use intuitively. Even worse, all four graphs have a 100% correlation with each other. That is what is meant by the statement: correlation is not correlation.

To make matters even worse: the regressions in the above figures all presume that the “real” line (again the blue line) can be calculated by using the horizontal axis.  The least square method basically calculates which line would involve the least squares to capture all the data points. Here “the least” is calculated as orientated towards the horizontal axis. Yet, this is completely arbitrary. If one uses the vertical axis the line would be the opposite as shown below. The red line is the normal regression, but there is simply no mathematical argument why the black line is not correct.

Of course in the example above, it looks weird. But the reason is that the red line follows the dots really close. If you have two of those lines you get a very high correlation. So, even though there is no sound argument for it, if the correlation is very high, one can still use it.

So what is a very high correlation? As a rule of thumb, any correlation below 80% is suspect. And yes, we haven’t found that many correlations above 80%. So most correlations are spurious.

Underdetermination also plays a role with correlation. Even if you get a high correlation (>80%), even then due to the underdetermination of theory by data, there are many more theories possible besides your one theory.

Correlations in football

Does this have any real world application in the world of football? The answer is yes! Most correlations in football are less than 80% and should be regarded with a pinch of salt. Furthermore, correlation can be gamed.

Let’s look at any correlation involving a team statistics like xG or Xa. If one finds, for example, a correlation of 50% between team xG and the number of goals scored in the next season, how can that correlation be gamed? Easy! For once, the correlation between the xG of defenders (for instance 0.1) and future goals is very high, because the xG of defenders is very low and they will only score a few goals next season. But the correlation between the xG of strikers and future goals is quite low. We looked at the topscorer for each team in the Dutch Eredivisie and the Belgium Jupiler League and found only a 27% correlation between the xG of the striker before the season and the goals scored during the season.

But if we would combine the high correlation of the defenders in our example with the low correlation of the strikers we found, then one gets about a correlation of 50%, which most of the time is considered a good correlation by people who are less strict than we are.

Most importantly: decision made based on these kinds of correlations have a bigger risk of being the wrong decision than decisions based on higher correlation and less combinations of underlying correlations. Especially, when it comes to recruiting players, basing your decision on the wrong kind of correlations can end up in quite a costly debacle.

No tail information

As Nassim Taleb makes clear almost all correlations lack information about the tails of distributions. Correlations, if useful at all, only tell you something about average players. Yet, football clubs and scouts are looking for exceptional players. It is highly unlikely that you will find exceptional players using correlations as exceptional players are located in the tail of a distribution as they outperform average players.

Even in an 50% correlation there really is very little information as can be seen from this graph:


Cybernetic Big Five Theory

Cybernetic Big Five Theory is the most scientific way of looking at personality as it is based on neuroscience rather than psychology. Cybernetic Big Five Theory is of course a form of cybernetics. The first lesson of Cybernetic Big Five Theory is that there isn’t such a thing as personality. Thinking in terms of traits turned out to be the wrong approach. Instead there are evolutionary behavioral patterns that our brain produced due to the biological structure of our brain. Although it is called Cybernetic Big Five Theory, in reality there are different levels of abstraction as to describe these evolutionary behavioral patterns. At the highest level of abstraction there is the Big Two, followed by the Big Five and the Big Ten.

Cybernetic Big Five Theory is extremely important for football. For knowing which evolutionary behavioral pattern your players have, makes the difference between understanding them and knowing how to deal with them or not. For more than twenty years I have developed The Neurogram® model which is based on Cybernetic Big Five Theory, but provides a dynamic version of Cybernetic Big Five Theory that shows how people evolutionary behavioral patterns change depending on whether they are stressed or relaxed or neither. The Neurogram® also allows you to determine people’s typical set of evolutionary behavioral patterns without the need of an fMRI scanner.

Here is the hierarchical heuristic scheme of Cybernetic Big Five Theory:

Evolutionary Behavioral Pattern Cybernetic Function Opposite Pole
Big Two
Stability (defending) Protection of goals, interpretations, and strategies from disruption by impulses Creativity (attacking)
Exploration (scanning) Creation of new goals, interpretations, and strategies Exploitation (passing)
Big Five
Extraversion  Behavioral exploration and engagement with specific rewards (i.e., goals to approach) Detached
Openness to experience (tactics) Cognitive exploration and engagement with information Openness to reasoning (strategy)
Conscientiousness (discipline) Protection of nonimmediate or abstract goals and strategies from disruption Disinhibition (undisciplined)
Agreeableness  Altruism and cooperation; coordination of goals, interpretations, and strategies with those of others Antagonistic
Unflappable Defensive responses to uncertainty, threat, and punishment Neuroticism 
Big Ten
Assertiveness  Incentive reward sensitivity: drive toward goals Letting others go first
Enthusiasm  Hedonic reward sensitivity: enjoyment of actual or imagined goal attainment Unenthusiastic
Even tempered Active defense to avoid or eliminate threats  Volatility
Self-assured Passive avoidance: inhibition of goals, interpretations, and strategies in response to uncertainty or error Withdrawal (anxiety, depression)
Intellect  Detection of logical or causal patterns in abstract and semantic information Unintellectual
Openness to experience Detection of spatial and temporal correlational patterns in sensory and perceptual information Imperceptive
Industriousness Prioritization of nonimmediate goals  Undisciplined
Orderliness  Avoidance of entropy by following rules set by self or others Disorganized
Compassion  Emotional attachment to and concern for others Callous
Politeness  Suppression and avoidance of aggressive or norm-violating impulses and strategies Belligerent

(Source: DeYoung, C. G., & Weisberg, Y. J. (2019). Cybernetic approaches to personality and social behavior. In M. Snyder & K. Deaux (Eds). Oxford Handbook of Personality and Social Psychology, Second Edition (pp. 387–414). New York: Oxford University Press)

Cybernetic Big Five Theory and football

In my own research I am working with a circumplex model of Cybernetic Big Five Theory. DeYoung himself is also looking into circumplex models for his work. What is still speculative is whether defending as Stability, attacking as Autonomy / Creativity, opportunity as in seeing chances and exploiting them through passing as Exploitation and prediction (also known as vision or game intelligence in football) as Exploration also correlate with specific sets of these evolutionary behavioral patterns. If they do, the circumplex model looks like this:

Cybernetic Big Five Theory for Football
Cybernetic Big Five Theory for Football