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Super James Milner? The Impact of Big Data on Football

England stalwart James Milner has been announced as the fifth best player among Euro 2016 teams – ahead of the likes of Champions League winners Gareth Bale, Toni Kroos and Sergio Ramos, according to the results of a recent UEFA rankings tool.

The success of Big Data to make decisions in the world of sport has been widely debated for many years.

The Moneyball story of Oakland A’s General Manager Billy Beane building a team based purely on statistical analysis is probably the best known example. Beane’s team of unfashionable bargain buys went on to win 20 consecutive games on their way to being crowned champions of the American League West in 2002.

Championship side Brentford’s Danish owners also insist on mathematic modelling as a key component to their tactical approach, albeit with less success. Their other club, FC Midtjylland, and German club TSG Hoffenheim are also big advocates.

The idea of old school gaffers such as a Big Sam or a Harry Redknapp relying on anything other than intuition, experience and the player being a terrific lad might seem like a foreign concept, but Big Data is now an essential part of football management.

Training and Match Performance

Electronic performance tracking devices are fitted to almost every top level player both on the pitch and the training ground, as well as cameras following their every move.

Ex Manchester United boss Louis Van Gaal famously installed cameras throughout the club's training complex to ensure he could analyse every aspect of behaviour and performance.

A team of performance analysts are then on hand to scrutinize and report almost every aspect of the player’s game - from the distance they travelled and number of sprints, to how hard they are striking the ball.

These data trends have a tremendous effect on player coaching. Is a player’s work rate poor? Does their movement or performance dip after 80 minutes? Training programmes can be created and inspirational substitutions can be made.

Germany’s national team are certainly advocates, using Adidas’ miCoach elite team system for training sessions during their winning 2014 World Cup campaign.


Looking for hidden gems such as the next Riyad Mahrez or N’Golo Kante?

With colossal sums of money switching hands during the transfer window, Big Data is playing a huge part in ensuring the gamble on a new player pays off.

A host of companies such as Opta, Prozone, and British platform Scout7 track and record performance data of players from across the world. This data is then used by scouts and managers to identify players who will fit the profile that their team is looking for.


Think of it like a real life version of Football Manager, where players are rated based on a vast range of qualities.

Gone are the days of agents sending clubs DVDs of their clients' greatest hits. Scouts can now get a full report of a player’s performance over a season and beyond through Big Data analysis.

The England Euro 2016 Selection

Roy has picked his team for Euro 2016, with plenty of debate on which players should be on the plane based on form, experience and reputation.

The main debate has been the inclusion of players such as Jack Wilshere and Jordan Henderson in the squad ahead of players like Danny Drinkwater and Mark Noble, whose stats for passing, goals and assists are far superior.

Using Big Data, football statisticians released their list of 23 players who would travel to France if Roy had picked the Three Lions purely on stats during the Premier League season. It’s a very different squad indeed, particularly the absence of Captain Wayne Rooney!


  • Fraser Forster
  • Joe Hart
  • Ben Foster


  • Danny Rose
  • Kyle Walker
  • Chris Smalling
  • Phil Jagielka
  • Simon Francis
  • Eric Dier
  • Scott Dann


  • Dele Alli
  • Ross Barkley
  • Michail Antonio
  • Danny Drinkwater
  • James Milner
  • Mark Noble
  • Andros Townsend
  • Adam Lallana


  • Jamie Vardy
  • Harry Kane
  • Troy Deeney
  • Daniel Sturridge

Source: Sky Sports