What is intelligence and what is its relationship to the beautiful game? Should stars such as Zidane or Maradona rightfully be called “geniuses”? What is it that allows some players to consistently make better decisions than others? The trouble with questions like these is the fact that they contain assumptions within them. Intelligence, in particular, is a word that engenders tremendous confusion and controversy. I’ve heard terms like “soccer IQ” or “game intelligence” thrown around enough that I figured it’d be worth looking into.
It’s easy to see why an idea like soccer IQ would develop. Simple observation of players that any coach has access to will show that some players consistently have more success solving the problems the game presents. The confusion isn’t in the differing levels of success – this is a given in sport – it’s the explanation of that success that’s problematic. In the explanation of success we reveal how we think about the problems players solve, and this is where many of our cultural assumptions sneak in.
Since Socrates and Plato, Western philosophy has assumed that we operate by universal rules. Greek philosophy may seem irrelevant but fast-forward to the present day and the computer I’m working on takes inputs, applies rules or algorithms to them (hence computing), and transforms them into outputs. Even modern forms of therapy like CBT are essentially attempts to uncover the hidden rules (cognitive biases) which we’re operating by.
So let’s see how we bring this paradigm onto the field. From this point of view, information from the field is the input into the players brain where a set of rules/algorithms are applied to that information until it is transformed into a mental solution which can then be sent to the muscles for output. If this were the case, then the player who optimizes that middle step between input and output (computing) would rightfully be considered more intelligent. Perhaps they have found a better set of rules to apply to the incoming information. Perhaps they apply those rules more quickly like a more powerful computer. Once you have this paradigm in place it’s easy to explain the game through it. Some players just appear more aware and switched-on (note the computer metaphor) than their sluggish competitors.
The only problem is this paradigm is dead wrong. It wasn’t until the mid-twentieth century that thinkers like James Gibson and Martin Heidegger began to seriously pick apart the idea that we operate based on unconscious rules and computation, but modern research has come down decidedly in their favor. The classic experiment was done with baseball outfielders catching fly balls. If the outfielders received information and performed a calculation that predicted where they needed to go to catch the ball we would be able to see that in the path they take to get there. What we actually see is that the outfielders directly couple their perception and action without the middle computational stage. This gets a bit technical, so here’s the research for those who want to know more. Also, consider that your dog can catch a ball without (presumably) calculating the laws of physics.
So if not computation, what differentiates between players who are able to solve the problems the game presents and those who aren’t? The difference isn’t in the performer. It’s in the relationship between the performer, their environment, and the task or goal. Let’s bring this model onto the pitch. First you have the performer. Let’s say it’s an attacker in a 1v1 situation. Second, we have the environment which is the field, defender, other players and so on. Last we have the task or goal. The attacker wants to dribble by the defender. The thing is, all three of these constrain and influence each other in a reciprocal fashion. The movement of the defender depends on the movement of the attacker. The movement of the attacker depends on the movement of the defender. The goal of the attacker to dribble by the defender is constantly being modified by the position of the attacker in relation to the defender. Maybe the attacker decides they should turn and keep the ball rather than going forward. Maybe the defender decides to hold the attacker up rather than challenge for the ball. A computational approach just won’t do for the fluid, ill-defined problems that players face on the pitch.
To get to the answer, we have to understand that the performer, environment, and task/goal functions as a system. None of the parts make sense outside of that context. Ecological dynamics gives us a way of talking about these complex interconnected systems. Now that we’ve framed the question, we can dive into the soccer intelligence issue.
Through self-organization, the performer-environment-goal system will converge onto stable states of organization. The shift between stable states is referred to as a bifurcation or phase transition. If a system is unable to shift between states in the face of perturbations it is referred to as mono-stable. By contrast, ‘multi-stability is defined as the “ability to transit between multiple states of organization under given constraints.” (Seifert, Button, Davids, 2013). Additionally, meta-stability is the “ability to exploit coexisting coordination tendencies in a transition or unstable region); and ‘variability’ (exploiting critical fluctuations to enable adaptive behavioural transitions).” (Seifert, Button, Davids, 2013).
Ok that’s a mouthful. Let’s break it down. Better players are more flexible because they have access to more states of organization. This allows them to flexibly adapt to the situation while their opponent has settled onto only one solution (state of organization). It’s easy to dribble around novice players because they typically exhibit the mono-stable solution of invariably swinging their right leg at the ball with a sweeping motion. The more experienced player retains movement variability and has access to any number of adaptive solutions to the problem of the swinging leg tackle.
So are multi and meta-stability the same thing as soccer intelligence? Yes and no. There is no single factor that underlies a player’s ability to solve movement problems, so if your concept of intelligence is the standard g-factor model of cognitive psychology applied to sport then this is decidedly not that. If by intelligence you mean the fluid creativity that great players employ to solve a broad range of unique movement problems then the ecological approach is a good start towards understanding that.
As coaches we should take a minute to think through the terms we use. What value do they add? Are there hidden assumptions in them? Personally, I feel that the term soccer IQ lacks a meaningful definition and carries too much baggage from cognitive psychology and information processing to be of much use. I do believe, however, that adaptive skilled behavior displayed on the pitch is certainly a form of intelligence. The fact that a maestro like Iniesta or Zidane isn’t calculating their decisions doesn’t make their play any less brilliant.
Mcbeath, M., Shaffer, D., & Kaiser, M. (1995). How baseball outfielders determine where to run to catch fly balls. Science,268(5210), 569-573. doi:10.1126/science.7725104
Seifert, L., Button, C., & Davids, K. (2012). Key Properties of Expert Movement Systems in Sport. Sports Medicine, 43(3), 167-178. doi:10.1007/s40279-012-0011-z