Ian McHale is a professor of sports analytics at the University of Liverpool. His research interests include statistics in sport, and the analysis of gambling markets and issues relating to gambling. In 2005, he created the EA SPORTS Player Performance Indicator – the official player ratings systems of the Premier League.
Ian appeared on the University of Liverpool Podcast to discuss the growing influence of analytics in professional sports around the world, and whether football is able, or willing, to embrace advanced analytics – or if the complexity of analytics will continue to restrict its role in the world’s most popular sport.
5 tactical insights about football analytics
- Sports analytics isn’t new
Made famous by the film Moneyball, which depicts the general manager of the Oakland Athletics baseball team (played by Brad Pitt) rebuilding a team that would bring about their 2002 winning streak, analytics has become increasingly prominent in sport.
The broad theory is easy to understand. Ian explains: “What the Oakland Athletics did is they said: ‘Let’s ignore all the traditional statistics of how good a player looks, how tall he is, how strong he is, and how fast he can run. Let’s look at the statistics that impact the results of the team.’”
- It is incredibly complex – whatever the sport
Different analytics models are used for different sports, but they each have a common trait: their complexity. In ice hockey and basketball, for example, plus-minus player ratings use complex calculations to assess a player’s worth not on traditional metrics, such as goals, runs or dribbles, but on the likelihood of a team winning with that player involved.
“All it’s interested in is what’s the team performance without the player,” explains Ian.
Where it gets complicated, however, is in weighting for factors such as the strength of a player’s team-mates and opposition. “You need things like advanced statistical modelling to be able to estimate the performance, or the change in performance of the team, with or without the player.”
- But it is even more complicated in football
Compared to some of the US sports where analytics has been embraced, football has a range of factors that add to the confusion – and several the data cannot yet see.
How do you measure the value of a player making a run just to pull an opposition player out of position, for example? Or the impact of a player’s concentration dropping for ten minutes after an unexpected mistake?
“In baseball,” says Ian, “you’ve got a pitcher, a batter, and an outcome. So you’ve got an input, an output, and you can measure the outcome very easily – whereas in football, what’s your input? What’s your output?... For any one player, it’s not obvious what that is.”
- Analytics regularly goes against the expert’s expectations
When Barcelona, one of the world’s biggest football clubs, spent £142m on Liverpool midfielder Philippe Coutinho in January 2018, they did so in the belief they were buying one of the best players on the planet. But the analytics suggest that verdict may be wrong.
Ian reveals: “He does very nice things on the ball, but when you calculate his plus-minus rating, he’s actuallly a pretty average player in that Liverpool’s results with or without him are about the same.”
- Clubs are fooling themselves into thinking they’re using analytics
Ian insists that even in the Premier League, the world’s richest domestic competition, most clubs are confusing counting for analytics. Rather than focus on what matters, he says, they monitor things like a player’s pass completion percentage.
“That’s not actually what I mean by ‘analytics’,” he says. “When you try to explain this to people at football clubs, they then revert back to the sort of Doubting Thomas that you see at the start of Moneyball, (saying) ‘we actually don’t need that much help. I’ve been watching this game for 40 years now, so I can spot a player when I need to.’”
He adds: “The reality is that there are very few football teams that are actually adopting an analytical approach.”
About this podcast
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