But technology will never replace people as coaches because, as the following article explains, athletic outcomes are relatively unpredictable which is why they play the game, as the saying goes.
More importantly, humans are emotional and, let's face it, irrational. JL
Tomas Chamorro-Premuzic reports in The Guardian:
Sports analytics, computer-generated algorithms, and big data can improve human decision-making in sports, but so long as the athletes are human, technology alone will not improve their performance. Data can help us make better predictions, but it will not make people more predictable than they already are.
The 2011 film Moneyball depicts the true story of baseball manager Billy Beane (played by Brad Pitt) who defies all odds by taking an underdog team to the playoffs and winning a record 20 consecutive games despite spending very little money on players. His killer weapon? Data. Indeed, by hiring a geek rather than experienced baseball scouts, and trusting computer-generated algorithms rather than common sense, Pitt’s character demonstrates that sporting success does not depend on common sense or intuition, but robust scientific principles and maths.
Unsurprisingly, the film has spurred a great deal of speculation about the idea that technology may eventually replace sports managers. The underlying logic to this idea is twofold. First, computers are able to gather and process much more data than we do, which enables them to better predict future performance; second, unlike humans, computers are not biased by emotions or subjectivity, so their decisions are bound to be more rational than ours.
Despite the appeal of this sports analytics, it is fairly unlikely that Jose Mourinho or Greg Popovich will be out of work soon. There are three main reasons for this.
Data alone is trivial
Indeed, it is only when combined with expertise, experience and knowledge that data can enhance our ability to make the right decisions. As the German philosopher Immanuel Kant once noted, “theory without data is groundless, but data without theory is just uninterpretable.” The point of data is to refine our intuition, but, at the same time, a great deal of intuition is needed to make sense of any data. Unless you know what to look for, the data will show only numbers. This is why experts are capable of making intuitive decisions that mirror data-driven decisions.
Humans are only partially rational
Because of this, a purely rational approach to managing people does not work. This is why sports athletes need human coaches, who can tune into their emotional states and empathise with them. Of course it may be possible to refine artificial intelligence to mimic human coaches in this task, but a fundamental difference between machines and humans will remain, namely that machines won’t care about the athletes – at best, they will be able to fake feelings for them but they will still seem pretty unbelievable.
Athletes are pre-wired to respond more emphatically to humans than computers. Having your coach watching you creates a strong process of psychological influence, called leadership, which machines will never manage to imitate. Thus, even if data does a good job at diagnosing problems, the intervention – acting on those problems, including making decisions and influencing athletes – is best left in the hands of humans.
If sports outcomes were entirely predictable, there would be no sports at all
As sports spectators, we seek a certain degree of consistency and predictability. This creates an implicit moral code underlying sports whereby, on average, the best teams or players should win. However, it is the remaining degree of unpredictability that makes sports exciting. This is why betting houses make so much money. What sports consumers want, then, is to have the ability to predict the unpredictable; but if they were really able to do this the thrill and pleasure of watching sports would just vanish.
The application of technology and data to sports management mirrors the wider realm of business. Consider the field of talent management, the area of human resources concerned with the selection, motivation, and retention of employees, especially at the top of the organisational hierarchy. Despite substantial technological developments in this area during the past decade, big data and computer-driven algorithms have yet to have a real impact on management practices. Sure, it is now easier, faster, and cheaper to find suitable employees for a job, to quantify their contribution to the company, and to make data-driven decisions regarding rewards, promotions and retention.
However, few organisations have adopted such tools widely, and those who have are not obviously more effective than their counterparts. Besides, there is a high price for the datification of management practices. First, despite the objectivity of such practices they are unlikely to be perceived as fair by the workforce (particularly those classified as poor performers). Second, making these practices transparent increases the probability that individuals play or game the system (just like hotel owners may fake their Tripadvisor ratings, or those of their competitors). Third, when transparency is avoided ethical issues and anonymity concerns emerge. For instance, most companies would learn a great deal about their employees by mining their e-mail data, but who would want to work in a place like that?
In short, sports analytics, computer-generated algorithms, and big data can certainly improve human decision-making in the field of competitive sports, but so long as the athletes are human, technology alone will not improve their performance. Data can help us make better predictions, but it will not make people more predictable than they already are. Finally, most coaches, clubs and managers have access to the same quality and quantity of data, but significant differences between their performances remain because human decision-making still dominates the game.
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