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I blame Frederik Winslow Taylor and the educational system that we believe in universal recipes for doing things.
The engineer Taylor had the dream of improving the productivity of factories. Among others, one of the tools to achieve this was the school. He standardized behavior in the industry so that the factories were more efficient. Surrounded by complicated systems like machines —not complex ones like people— he reduced and calculated everything in order to efficiently organize them. He explained it in The Principles of Scientific Management. The worker —a person, a complex system— was reduced to the condition of a specialized machine —a complicated system. Spoiler: It goes wrong.
Taylorism is the radical tendency to organize every aspect under quantitative measures and strict planning. Anyone who has worked in a fast food restaurant, has been a sport player or student in a public school knows it. These followed —and still do— Taylorist principles of standardization, routinization, and simplification. Centralize, divide, reduce, over-control... everything. The worker only had to execute in a standardized way the plan decided by the manager. Taylor proclaimed that “in the past man has been first; in the future the system must be the first.” Forget your potential. Fuck your individuality. Obey the plan that the manager tells you. This is why the current educational model in America emerged.
Taylor needed to know where each student’s ideal place was in the factory. School emerged to classify the students who were managers and the ones who were workers. The development of schools and Taylorism went hand in hand. The ring bell controlled the movement. They wanted you to listen to it for 20 years in the school so that you would be speechless during the 40 years you were treated like a machine in the factory. The students arrived at the factory classified —not educated— to behave among machines, not to live in a complex world that lives and reacts, with their boss Taylor’s beliefs well rooted.
A grade does not define you but they used it as a label to condition all your life. Their love for standardization and quantification was applied in the classroom with tests: equal and quantifiable for everyone. With measurable outputs the manager could better control the workers, I mean students. Anyone who had standard results was one of the lot and was labeled as a worker —a simple machine. If your marks deviated —above, never below— you were labeled as someone with more potential: a manager, the brain of the factory that controlled the hands. The purpose has never been to educate, but to classify students to put them in the “right society place”. Education does not simply change what students know —it changes what they want to know. Have you come to consider whether it can be the same with coaching?
In the factory, as in schools and in training, the student was excluded from participation in the planning, organization, and direction of the process. You have to obey the boss’ orders. He “thinks”; the students are workers who “do”. The manager, the coach and the teacher know the best plan and prescribe the solutions. The workers, students or players are only responsible for executing it. The learning outcomes are not discussed or individualized but are imposed form the top by the politicians and the curricula. The player has no say or vote in the game model... unless you are coached by James Vaughan. Goodbye creative abilities and unique potential of each student. The school will not encourage them because the factory does not need them. Taylorism creates monkeys: “That’s how it has always been done.” There is no space for workers, students or players to come up with new ways of doing things. The manager doesn’t care about your potential, he needs his machines to be efficient. It will improve productivity but destroy your life. The coach and the player, despite being in a different context, have the same relationship as the boss and the worker in the factory. Thank you, Taylorism.
We have left the factories with a lot of inertia, school has not developed the critical spirit that makes us question things... and we find ourselves in the middle of the 21st century with a mentality —treating teams as if we were mechanics— that has survived despite the absence of the justification for this choice. Unconsciously, Taylorism has been established in our way of doing things. We all learn by example. Maybe Taylor created a model that worked between machines. He screwed it up the day he considered taking it out of the factory.
The engineer, lived trapped inside a factory, and only dealt with machines. Improving factory’s production is about complicated systems, training is about complex systems. In a complicated system —a car, a refrigerator— you know how each component of the engine is related, it’s linear. The relationships, the processes... are closed and can be studied. You can find an efficient way and standarize it. You can conceive the ideal as something closed that you can calculate and achieve. The behaviour can be easily predicted. Surrounded by machines that do not change, evolve, have no emotions or individuality... I also find “one best —and simple— way” to tell others what to do. Surrounded by players, it is much more difficult for me to find the “universal recipe” to coach, to predict their behaviour.
Among machines the context is not relevant as it is outside the factory. There is no influencing environment, no path dependence, no context, no time or individual influencing it... as it happens when we deal with players. In something as complex as the Flow of Life, a basketball team or the human body... you can’t even anticipate the result, in an exact and concrete way, of how all its processes and the components interact... because they are related in a non-linear way.
There is a lot of evidence that we are very bad at anticipating non-linear processes: the construction of Denver International Airport (16 months late, costs 300% greater than forecast), the development of the Eurofighter jet (5 years late, $25 billion above predicted cost), the Scottish Parliament building (3 years late, with projected cost of £35million escalating to £414 million), the Sydney Opera House a scaled-down version completed 10 years late, with estimated costs of $7 million eventually amounting to $102 million). Despite the meticulous study of meteorology, despite all the technical advances and efforts of scientists and space agencies… we don’t have complete predictive capability of the weather. We cannot measure to a fine enough scale, because small measurement errors can rapidly upset any prediction and because the various component elements of weather systems interact in complex and unpredictable ways. The more you believe in your planning skills, the more you miss.
“Everyone has a plan, until they get punched in the face.”
—Mike Tyson
In a linear world —the factory—, you know what throttle pressure will cause what speed. Life is non-linear: imagine that for the same pressure applied to the car accelerator in the morning it reacts like a Formula 1 and at night you don’t even notice the increase in speed. You apply the same input, its impact is very different. It is a fictitious example, but it is the same effect that an exercise can have: for one player it will multiply, for another it will have no effect or harm him. An exact internal load —input or perturbation— causes different external loads —outputs, results or consequences.
I wish there was an optimal path to success in sport that would last over time and not change. I wish there was “one best way” to grow a plant. I wish there was a universal recipe to teach a player. Between machines, by applying certain inputs, you know what outputs you will cause. Between people, the same inputs have very different outputs —impacts. The previous history, the mood, the environment... and many more contextual variables condition it. Machines don’t know what I’m talking about, you do. That you have had hamstring injuries determines the training program that I will apply to you; an exercise will affect you differently than a player who has not had injuries. The machines, if they break down, are repaired and quickly forget that they have had an “injury”... they continue to perform the same.
The context in which you find yourself —not the manager— tells you the best result, it determines the “one best way” at that moment. There are as many “best ways” as different contexts in which they are applied. John Kiely defined it perfectly when I asked him: “Which belief every coach should eliminate to improve his methods?” He said: “The belief that we can anticipate the effects of training. If I do this type of training, this thing will happen.”
The training paradigm revolves around the belief that we can control a player or a team as we do with machines. It is assumed that the body is an engine and adaptation to future training is largely predictable and follows a determinable pattern. We are not aware of how many interdependencies shape our lives, about how much is hidden. We see what happened in the past but it isn’t so easy to know why. When the magician makes the rabbit disappear… you know it goes away, you don’t know why. It is assumed that we can know and anticipate non-linear adaptations highly influenced by context. They are conditioned by an infinite number of stressors —cellular, muscular, emotional, organismic, from the environment— but we keep thinking that if I do X, I will achieve Y. It doesn’t matter the time, nor the place, nor the person. It served us with machines, it makes no sense with people.
Used to the closed certainty of machines, variability has a negative connotation. If there is “one best way“, why is it necessary to change? The truth is that it is necessary for successful training development. The variability in the task, in the session and in the design of the periodization makes you antifragile. By removing it you create your own cage which does not let you out. Fluctuations help you adapt. Monotony causes overtraining, problems, injuries. Differences, variability… provide information for the system to improve, so that it can adapt to more situations and overcome more challenges. An identical movement repeated constantly —like a machine— does not bring any new information to the system. It makes the player rigid —a robot—, it doesn’t help to become more flexible and adaptive for new situations. If a rigid machine, with a single function, you change its place in the factory, you mess it up. Complicated systems do not know how to adapt. If the player repeats and repeats the same thing, you will change the context and mess it up: you will have destroyed his ability to adapt.
The reality was deciphered for me by Manu Sola: “We control the stimuli, but not the adapation.” The interactions between all the components of the player or team —a complex system— shape the response to a perturbation in unknonw and difficult to anticipate ways. The genetic inheritance, mood, fatigue, sleep quality —which the machine neither has nor knows— affect the adaptations of the team and each player. If the environment is stressful for the player, it will generate a response different than if it generates trust for her. If I press a key on the machine I will cause the same effect throughout its life. For a player, the same key pressed —3x12 repetitions— will impact in different ways in different moments of the season. We wrongly assume that we can control everything, so we wrongly anticipate outputs when we apply certain inputs. We constrain to act on what we want, on the solutions we think are the best; we don’t constrain to afford.
“An actor in a complex system controls almost nothing, but influences almost everything.”
—Scott E. Page
The factories conditioned our mentality at the sports halls, at the football fields. The path influenced our mechanistic mindset. We still seek success by reducing movements or performance to small units. Reducing machines to single components and simple processes gave us power to find optimal paths. We tried the same in training and failed. We need to integrate. If not, we miss the magic. If we separate hydrogen and oxygen and apply them to fire, we give strength to it; but if we integrate them, water emerges and we can turn it off. Now you train technique, now tactics, when you finish you go to the gym to work the physical part and after training you go to meet alone with the psychologist. If I coached like this, I would spend the day praying that all the processes that have been carried out separately interact functionally together in the competition. It never ceases to amaze me that professional padel players who compete together continue to train, separately, in different cities, with different coaches.
We feel comfortable among machines. They are not dependent on the environment nor their history and that give us a pleasant feeling of predictability and control. We love simplicity and explanatory closure, calculating and quantifying everything, summarizing it with a simple number even if it makes us more blind. Would you buy next year’s shoes for your 9-year-old child based on the average shoe size for 10-year-olds? It doesn’t give us better information but it makes us feel safe. It’s a trap. The complexity of this world and the non-linear effects of perturbations keep saying to us that we must accept uncertainty and ambiguity. If you don’t feel like it, go work in the factory. The love for simplicity doesn’t match with the world’s complex reality. Taylorism was applied in engineering contexts... but a man is not a machine. Either they were too dumb, or they didn’t care. Thinking that the map was the territory made Taylor think that the garden of life worked like a fucking machine factory. The truth is that each terrain presents a unique navigational challenge that requires a unique route map to optimally reach the destination. Taylor, by doing more harm than good, impacted the current educational model, many ways we live, train... and see the modern world. Send Taylorism back to the factory and stop treating the flowers of your garden, your players, the team... thinking that they work like a machine.
Martí Cañellas | Fosbury Flop
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