The search to find the next Wayne Rooney is on – and data analysts are leading the charge.
Although talk of the Manchester United player鈥檚 demise may be premature, there鈥檚 no denying that the quest to unearth the next Rooney, Lionel Messi or Cristiano Ronaldo has never been greater, given the vast sums of money in the game.
As well as traditional scouting structures and football academies, clubs are increasingly relying on data and technology to flag up the potential stars of the future.
That鈥檚 the message from Sam Gregory of the world鈥檚 leading sports data provider Opta, who spoke at 老九品茶Cloud鈥檚 ‘Technology in sport’ breakfast, held in conjunction with Pro-Manchester, this week.
Opta compiles data from a range of sports, including rugby union, rugby league, cricket and most prominently in football. It scrutinises more than 50 leagues around the world: in the last 12 months, for example,聽it has聽analysed 139,297 goals.
Canadian-born football data scientist Gregory gave an insight into the work the analysts do with Sky Sports, the BBC and many top Premier League teams.
鈥淲e have a lot of clients. Often a club comes looking at a 拢30million signing, and they just want a tick to make sure what their scouts are saying matches up with our data,” he said.
鈥淭hat鈥檚 a common-enough request, for us to essentially do the due diligence and make sure this player is the right fit.
鈥淒ata鈥檚 a really good way to look at the bigger picture from a聽 bunch of games across different leagues.
鈥淸You can] approach the problem at a much higher level than just a coach鈥檚 intuition, watching the game through his eyes.
鈥淲e鈥檙e not going to know how to take a group of players and turn them into a team that鈥檚 going to win matches. But in terms of just helping the manager get there, I think that鈥檚 where we step in.鈥�
The majority of big teams now have a huge network of tech-savvy scouts scouring Europe for its brightest talents.
Manchester City, for example, have at least 11. In 2012, Arsenal bought US-based football data analytics company StatDNA for 拢2.165m.
Opta鈥檚 Gregory stressed that聽only a handful of sides have millions to splash out on the next John Stones or Luis Suarez, and this is where his company鈥檚 gigantic database can prove invaluable.
He said: 鈥淚f you鈥檙e looking at a new centre-back, and you say we don鈥檛 have the money to look at every single centre-back in the world, or don鈥檛 have that scouting money, we鈥檒l ask what type of player you鈥檙e looking for and find the statistical profile of players that match up to it exactly.
鈥淎nd that鈥檚 what鈥檚 most common, to use that at the lower level. If you鈥檙e signing a player for 拢500,000, then these are the players that we would suggest you look at.
鈥淚t鈥檚 quite common to have a specific player in mind, a specific style in mind that managers want.
鈥淣ot just a good centre-back, they want some guy who fits exactly, with a lot of characteristics and qualities.
鈥淔or example, for a John Stones, we look at ball-playing centre-backs and try finding a good fit for that specific player and that specific manager.鈥�
Chasing data exploded into the minds of the public with the statistics-driven approach of cash-strapped Californian baseball side Oakland Athletics and their general manager, Billy Beane.
Their record-breaking 20-game winning streak of 2002 inspired the book Moneyball, and later the Brad Pitt film of the same name.
Gregory says the UK is still playing catch-up with US sports like baseball or basketball when it comes to data farming 鈥� but won鈥檛 be for long.
鈥淏aseball has a statistics database that goes back to the early 1900s. They have stats from every player, every game of the modern era.
“At Opta we have data going back in the Premier League to about 2003, or 2004.
鈥淚t鈥檚 something that is still developing, but because there鈥檚 so much money in football it鈥檚 developing quite quickly.
“In baseball, things move slowly over a long period of time. We don鈥檛 quite have the headstart they did, but we can move a lot quicker as there is so much money in the game right now, and there鈥檚 that drive towards high quality data.
鈥淏ut from our perspective, it is becoming really good, because now we鈥檙e seeing the career paths of what footballers like Wayne Rooney looked like when he was 18, versus today.
鈥淪o we can look for who are the players today that look like Wayne Rooney at that time.”
Gregory admitted that聽teams may already know who the 鈥榥ext Rooney鈥� is, and may be best recruiting Opta in searches a little further from home.
“The next Wayne Rooney, most of the scouts and managers in this county will have seen.
鈥淲ho looks like Wayne Rooney? A lot of that will come from people who have watched players from when they were 10 years old.
鈥淏ut one thing we might be able to do is for teams from this country asking who the next Luis Suarez is.
鈥淔or example at Ajax, they have this young kid named Kasper Dolberg right now who鈥檚 had a great season – it鈥檚 looking for players like that, who you might not have watched from 10 years old, but who line up statistically on the same path.鈥�