“He’s probably not been this excited since FIFA 17 came out on PlayStation. But Ian Cathro is about to find out what football’s like in the real world.”
This was, ex-Scotland Striker, Kris Boyd’s opinion as Ian Cathro was appointed Hearts manager in 2016. Whilst he may have had a point about the difficulty of stepping up from coach to manager (Cathro was sacked in July 2017 after 9 months in charge), Boyd’s quotes at the time highlight the mistrust from a lot of ex-players and pundits when it comes to newer coaches use of technology, and especially data, as part of their job.
This cynicism is clearly not shared by the clubs, as all of Europe’s top flight clubs now have data analysis departments, with analytics driving coaching drills, player recruitment and even, to some extent, tactics. Another sign of the importance clubs are putting on this area is Liverpool’s recent promotion of their Head of Performance and Analysis into the newly created position of Sporting Director. This seems to acknowledge that all of the club’s sporting activities are now informed by data analysis.
Alongside the clubs involvement there is also a large number of amateur analysts using publicly available data to provide interesting insights into matches and teams via blogs and social media.
So what kind of analysis is being carried out? Any regular viewers of football on television will be used to seeing the increasing array of statistics throughout each game and it is, in essence, a deeper dive of these numbers that is being carried out, either to back up more conventional opinions or to spot outliers that could give a team or player a competitive edge. Of course, the clubs themselves very rarely share the data they collect, or how they analyse it, but the independent analysts are more open. With one of the most talked about statistics being ‘Expected Goals’, a measure that seems to be making a move from analysts laptops to your Saturday night TV screens, which further underlines the role that data is playing in the modern game.
The Expected Goals (xG) statistic has been around for a few years but is still a relatively unknown measure to the majority of football fans. It is, in its simplest form, a score (between 0 and 1) assigned to every goal scoring opportunity during a match. This score takes into account where the opportunity occurred and how it happened. So a header at goal from outside of the penalty area is going to be near 0, whereas a shot from inside the six-yard box is will be near 1. These individual scores are then taken by the analysts and used in numerous ways. A detailed explanation of one method, used for predicting the outcomes of whole league competitions, is outlined here by the website 11tegen11.net. Other analyses show how effective a player has been when you compare their actual goals against their total xG across a season.
Finally, there are some great charts created from some of this data, one was created by the professor, author and data analyst David Sumpter (Soccermatics) to give some insight into Leicester City’s remarkable Premier League win in 2016. The chart below shows 2 passing stats from the first 20 games of the 2015-2016 season. It clearly shows that Leicester were different from the rest of the league when it came to moving the ball towards the goal and this likely contributed at least something towards their historic campaign.
There may be some resistance from certain members of the “old guard” but the spread of data analytics in football is here stay.