Even managers aren’t totally immune to automation… (Source: Getty)
If you pay attention to the countless academic studies devoted to the subject or the dire prophecies from Elon Musk or Stephen Hawking, an artificial intelligence (AI) age is rapidly approaching that promises to make most of our jobs redundant.
A McKinsey and Company report published earlier this year estimated that more than half of the world’s activities could be automated within 40 years.
Even football will not be free of its effects. Jobs requiring repetitive tasks or data collection are most at risk but the robotic takeover does not begin and end with ultra-efficient document checking; in the two decades since IBM’s Deep Blue computer beat grandmaster Garry Kasparov at chess, artificially intelligent machines have learnt to make art and compose symphonies.
Who is to say that AI could not one day park a bus better than Jose Mourinho, spot an opposition’s tactical weakness quicker than Pep Guardiola or scout the next N’Golo Kante before anyone else?
Expected goals 2.0
This season’s introduction of the expected goals (xG) metric — which aims to measure how effective a team’s attacking play is — to the mainstream via its use on Match of the Day demonstrates the increasing sophistication of statistics in the modern game.
Every top-flight club has its own analysis departments — Arsenal even bought an entire company specialising in the field — while modern players double as endless data providers, spending their days hooked up to various GPS trackers and biometric devices.
Yet with the sheer wealth of granular data threatening to exceed our ability to interpret it, some companies, such as Dutch start-up SciSports, are using machine learning — systems that can adapt to tasks they’re not explicitly programmed for — to ingest and interpret numbers at scale.
“Expected goals is too limited,” founder and chief executive Giels Brouwer told City A.M.
“It doesn’t take into account the context of a situation. So we built a system that calculates the game state of every action — determining what the chance of scoring is before a pass takes place and after a pass takes place. Then we can add an x value to every thing that happens on the pitch.”
Super-intelligent scouting
While scouting now routinely takes into account a player’s statistics, those numbers only provide a clearer picture of how they have already performed. Working out how a player may develop in the future is still somewhat reliant on sage-like intuition.
SciSports is using machine learning to change that with a product called Insight.
“It can look at how a player’s game is improving and then look for players who have demonstrated similar growth,” says 26-year-old Brouwer. “It’s worked out how good those older players were at their peak level, and then give a potential rating to the younger player.
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Source: CityA.M.