Scientific data is changing the game for football clubs and their players. But how does the action on the pitch get turned into the data we see off it?
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00:00As the cost of securing football players continues to rise, teams are increasingly turning to
00:06data science to make more informed decisions.
00:09Smaller clubs within Europe use it to try and gain a competitive edge over their big
00:14budget rivals.
00:15So how has the rise of data in football transformed the sport in such a short space of time?
00:26Hello and welcome to Football Now from the La Salle Stadium in Qatar.
00:31When it comes to the way that football clubs analyse matches and scout players, there has
00:35been no greater revolution in the modern game than the collection and application of data.
00:40Nowadays, scouting a player with just your eyes is actually seen as a little bit old
00:44school.
00:45Most teams now employ data scientists to interpret thousands of games logged by data specialists.
00:51So just how does the action we see on the pitch get turned into data off it?
00:56So all those data points are being collected manually.
00:59So per game, we have two operators who are being supervised by a team lead as well.
01:04So there's one for the home team, one for the away team, who collect all that information
01:07manually.
01:08So every shot the player takes, it's been manually input by an operator.
01:12We collect passes.
01:13The computer's going to calculate if this was in fact a long pass or a short pass.
01:17That means that every specific moment in the game, when you have 22 players, you only collect
01:21information about one player.
01:23And if you then think about the fact that when you have a 90-minute match, each player
01:27would have around two minutes.
01:29The ball is in his possession.
01:30So that means you cover actually only a fraction of what this player is doing.
01:33So that's what we call on-the-ball metrics.
01:35The focus is shifting more towards the off-the-ball metrics, that the camera will track players
01:39automatically.
01:40And we have operators who identify the players correctly.
01:42So we know that we monitor the correct players, assign the right data points to the right
01:45players.
01:46And that's a pretty automatic process.
01:48That's what teams want to know.
01:49So what is my team doing?
01:50What is this individual player doing when he has the ball?
01:53Of course, it's also changed the game for those watching, with phrases such as expected
01:58goals or XG now very much part of the vocabulary of an average football fan.
02:03But what is it?
02:04And how is it calculated?
02:06What the expected goal value is telling you is that, okay, given a specific opportunity,
02:10so taking into consideration lots of different factors, such as the distance to the goal,
02:15the type of assists, if it's a header or a shot, if there's players in the way that could
02:18block the shot, the position of the goalkeeper, all these factors make it a very big chance
02:23or rather a small chance.
02:25So from zero to one, or you could also put it in a percentage, like from zero to 100.
02:29So we've been collecting this information, the data around shots for decades already.
02:33So we have a huge database of shots that have been made in the past.
02:37And we use historical data to predict performance that's happening right now.
02:41Let's now take a look at a game that on paper looks equal.
02:45West Ham and Aston Villa drew 1-1 at the London Stadium last season.
02:49Both teams took 13 shots, each with five shots on target.
02:54But the data collected by expected goals tells the full story of the match.
02:59Aston Villa had an XG of 0.66, while West Ham had an XG of 2.02, meaning that they failed
03:06to capitalise on better quality of chances to win the game.
03:10Looking at the data this closely gives us extra context to understand why West Ham will
03:14feel, given the chances they created, why they should have won the game and taken all
03:19three points.
03:20Due to the difference in budget, some smaller teams around Europe are turning to data and
03:26analytics to try and gain an advantage.
03:28FC Midtjylland in Denmark, along with Brentford and Brighton of the Premier League, are just
03:33some of the clubs who've invested heavily in data science to try and gain that competitive
03:37edge, to varying degrees of success.
03:40So, how is data levelling the playing field between the haves and the have-nots?
04:10Not only do we see teams use data to help win matches, but players also rely on information
04:40specific to them to improve their performance and also prolong their careers.
04:45Former USA international Jay DeMeritt spent his twilight years with MLS team Vancouver
04:50Whitecaps and says that data on his physical recovery was imperative to making sure he
04:56was in peak condition on match day.
04:58I remember as I got older, you know, 33, 34, those were kind of the last years of my career.
05:04They used the data to make sure that I'd be fit on the game day.
05:08to go, hey, I think maybe on Thursday, we're going to keep Jay really light because his
05:12recovery based on the data is showing that that hard day, Tuesday, Wednesday, is showing
05:18that Thursday should be a light day for him because he's an older player.
05:21That's where I think the data becomes even more important from a layered approach.
05:25It's not just like, how many duels did you win?
05:27What's your top speed?
05:28What's the distance covered?
05:29It's like your recovery is longer right now on the Thursday, so let's keep him light.
05:34Let's not put him in that hard training session.
05:36As data continues to reshape the way football is played and analysed, will the beautiful
05:41game become something more of an exact science?
05:44Let us know if you think the reliance on science is here to stay using the hashtag FootballNowDataRevolution.
05:51That's all we've got time for from the La Salle Stadium here in Qatar.
05:54We'll see you next time.
05:55Bye for now.