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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:

• Basic principles
• Formation
• Motivation
• Discipline
• Flow
• System
• Personality
• Feedback

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.

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 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:

(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:

What is the best way to develop youth players? To answer this question you first have to split this question into two questions:

1. How to develop youth players before puberty?
2. How to develop youth players during and after puberty?

The reason why these are two different questions lies in the fact that the brain works different in kids before puberty than during and after puberty. Neuroscience has shown that the brain creates a surplus of synaptic connections in the embryo, but then continues to add synaptic connections until mid to late childhood. Basically what the brain does, is create a lot of connections so that the brain has the best circumstances to learn what it can learn from the environment through associative and instrumental learning.

At the same time, the brain has a strategy of pruning unused or underused connections. Pruning basically means that unused or underused connections are weakened, while used connections are strengthened. With that strategy the brain ends up with a network of connections that is highly likely quite good at dealing with the environment that the person is in and the behaviors that the person is actually going to use. As the cut-off point between these two different phases of brain development is around puberty, one can easily answer the two questions above:

1. Before puberty learn as many different things as possible. Different sports, but also different hobbies like playing a musical instrument, paint or hiking. And different environments like playing on the street, taking different holidays or visit museums. The reason that this is the best strategy before puberty, is that it stimulates the brain to create as many connections as possible.
2. After puberty specialize the kid in whatever you want him to excel at. So if you want him to be a pro football player, make sure he only plays football as soon as puberty starts. The reason why this strategy works, is that the brain now starts to prune all the unused and underused connections that the kid doesn’t need to play football. So the connections that are strengthened in the brain are primarily involved with football.

Here’s why this twofold strategy is best. Once puberty starts, pruning will happen a lot more than synaptic connection creation. A lot of connections are weakened in the pruning process as they turn out to be irrelevant for the behaviors that the person is actually using in life. Yet, it matters a lot with how many synaptic connections you started out with before you start pruning them. For example, say that on average the brain prunes 30% of the synaptic connections in the brain. If you start out with 1000 connections you end up with 700 strengthened connections, but if you started out with 10.000 connections you end up with 7.000 connections. To be clear: the brain has around 150 billion brain cells and each brain cell has many synaptic connections creating a network with literally trillions of connections. Nevertheless, the more connections you start with, the more you keep after pruning.

Besides this strategy for youth player development, the other important thing to do, is to check a kids personality type. We can already test kids at the age of eight to see what kind of brain type he has with Cybernetic Big Five Theory. This is important because once you know what kind of brain type a kid has, you can create a development program that fits his brain best.

Brain types differ from synaptic connections in an important way: brain types have nothing to do with the number of synaptic connections. Brain types depend on the biological hardware structure of the brain cells themselves. Not only do we have 150 billion brain cells, we also have thousands of different brain cells. Some brain cells have more Dopamine receptors than others. Some brain cells have more Oxytocin receptors than others. Some brain cells have more Serotonin receptors than others. Depending on the kind of brain cells your brain has grown the most, your brain type differs from other people.

If someone has the same brain type as you, he can still have less (or more) synaptic connections. These are two mostly independent ways the brain is structured. Brain types are important as the come with a typical set of evolutionary behavioral patterns that can interfere with the development of young football players. For instance, certain brain types have issues with discipline. Discipline is probably the most important skill to develop when it comes to becoming a pro player. Other brain types have issues with impulse control. Less of an issue for becoming a pro player, but more of an issue once a player is on the pitch. For all of these issues we have specific strategies to counter the negative side effects and promote the positive use of these evolutionary behavioral patterns.

The error ratio is the most overlooked statistics in football. In fact almost no data provider does report it directly. Instead they think that you need to derive it yourself using the data of the opponent. The reason is that almost all data providers measure what players do, but do not measure what players fail to do. Nevertheless, what players fail to do, is as important as what players do.

Failing to do something differs from not correctly executing what you intended to do. Of course that ratio is measured by almost all data providers. That is, for example, the ratio between all passes and all successful passes. What I mean with the error ratio is different.

First of all, I prefer to look at actual results on the field rather than action because players can perform an action correctly, but actually achieving a negative result for the team. So the first thing to do is to make a division between positive results for the team and negative results for the team. Examples of positive results are: goals, assists and getting the ball significantly closer to the opposing goal. Examples of negative results are: goals against, loss of ball control and having the opponent getting the ball significantly closer to team’s goal. 

The next step is to calculate the percentage of the contribution of each player on the pitch to both the positive results, but – and this is important – also the negative results. Once you have these, you also have the ratio between positive contributions and negative contributions. The ratio between positive contributions and negative contributions, is what I call the error ratio. So a player who has a pass accuracy of 81% – which sounds good – might at the same time have more negative contributions than positive contribution once you take into account what he fails to do.