Data in football needs context to be useful

WILL football one day be so “real time” that coaches will make in-game decisions based on data compiled during the 90 minutes? Surely this has to be on the agenda in the future as football becomes ever more technical?

Soccerex Connected included a session that included various professionals from the world of data. It has been coming, we were warned many years ago about the rise of “Big data” that everyone thought would just affect banking, trading, consumer experience and salesmen. Then came sport, with all its analytical potential. 

Sometimes, you have to wonder if we will all disappear up our own algorithmic orifices, not really understanding the heat maps, graphs, pie charts and figures at our disposal and guiding our lives. Certainly, while we look at heat maps and nod like wise old sages, do we comprehend and do we really need to? 

Football’s broad, universal appeal (there are probably little green people on Mars kicking a ball around) was its simplicity and accessibility. Now, clubs employ data scientists, analysts and other chin-stroking individuals who bring along corporate speak of the type you might hear in a management consultancy firm. Let’s be clear, using phrases like “user experience”, “configure”, “percolate” and “metrics” is not the language of the terrace or the cheap seats, which is why people like Gary Neville drawing on a screen hits the spot far better with the game’s demographic than any statistic.

So let’s assume this sort of dialogue is really for those that know. Jay Cooney of Major League Soccer club Philadelphia Union hit the nail on the head when he told the Soccerex audience, “all data needs context” and this is where so many people – over fascinated by the data rather than how best to use it – fall down.

Cooney pointed to other factors that affect players and their performance and how this is often ignored. For example, if a game is played in incessant heat or dodging thunder storms (which stop games these days), performance is undoubtedly impacted or compromised.

We have seen how taking the data often guides transfer target decision-making. Since Moneyball, any nerd in a dark room thinks he or she can successfully build a football team through statistical analysis. It can work, but it also fails – remember how Fulham bought a new squad based on data-driven processes and flopped miserably? If it was that easy, we could all make a fortune predicting football results. Fortunately, football depends on human fallibility, gut feelings, suspect temperament, euphoria and other emotional factors.

Human beings have faults, so until Manchester City buy a team of robots (that day may come!), then the game will never be perfect.

We are seeing the day of the data analyst at the moment, but it is a day that has only really just started. It’s still the coach, who has to deal with the raw material, the human resources, that carries the can. Do data specialists get the sack when all the number crunching and heat maps prove ineffective?

Photo: ALAMY

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