In recent years, The Athletic (now owned by NYT) has become a go-to site for sports fans globally for its very differentiated feature pieces on all things sport than just coverage of the action. Such as this piece on how AI could possibly shape the future of football. AI is becoming ubiquitous across many fields and there is no reason the field of sport should be left behind. Indeed, its use in chess to simulate the myriad possible moves has been around for decades. But the advent of LLM and availability of computing power is helping realise more use cases across other sports. Picture this as illustrated by an AI practitioner in football:
““You could set up to play Manchester City, train the AI agent to understand how they play and what their individual strengths and weaknesses are, and then play the game millions of times to find the most efficient strategies to beat them with the players you have,” says Mooney.
“We train airline pilots in simulators before flying, because we need to know it is safe. This sort of AI could give coaches their own safe playground to take risks and explore a much broader universe of tactics and solutions, before then risking their jobs. Animation can then show the most effective build-up structures to beat their press, how quickly to press, and what structure to recover back into. You’re training technology to understand the game in a virtual space to then inform real-world decisions.”
Specifically, the article talks about how AI can be used by teams in scouting players:
“A scouting data scientist, whose company works with various clubs across Europe, believes the holy grail will be when clubs no longer have to imagine how a potential signing would fit into their team. Instead, they will be able to use AI to translate all the data and video they have on the player’s style and the buying team’s tactics and use that to visualise just how they would perform in certain situations. “It’s not going to be liked, because there might come a time where it puts jobs at risk,” says a senior figure in a Premier League recruitment team. “It’ll be a cost-saving function in the future.”
Clubs already have huge libraries of scouting reports and performance and physical data, but it comes down to humans to interpret it all. AI can expedite that process by cutting out the need for someone to sit and read 100 different reports, instead rapidly picking out the players who best match the criteria the club’s models are looking for. “You could profile players from matches over a period of time and ask AI for the nearest version of a certain player,” says the Premier League recruitment figure.”
But it is not simply about replacing humans. It is the human-AI combination which could turn out to be most powerful as evidenced in other applications too:
““This suggestion that AI will get rid of scouts, I don’t get it at all,” says Mooney. “There are things models can’t see or they have wide confidence bands (a range of uncertainty in the estimate). How a player behaves under pressure, small-space ball manipulation, the timing of scans, the softness of their touch, their vision to see a pass: traits that are easy to see on video but hard, or slow, with data.
“There is going to be a delicate dance as these things evolve. For everything a machine can’t see now, there will be a branch of research closing that gap. It’s going to create a healthy tension between human and machine but also a real co-dependency — quality human input is essential for developing stronger and stronger analytical assets.
“I’ve used every experience I’ve had, working with some top forward-thinking coaches and football people, to make our technologies better, but through doing that I value the human side more. One challenge for people in the AI space is that a lot of those who are leading it can be too detached from the human side. They don’t see themselves as being vulnerable to advancements in technology.””
The article goes on to highlight the use of AI in sports medicine through the instance of FC Barcelona’s investment in an AI startup.
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