Soccer's top teams tap into data



Half-time soccer team talks used to be based around eating oranges. Now they can analyze real-time data and react to presentations.

Soccer has become a numbers game.

There is still a huge debate in the game about the use of goal-line technology, but for everything behind the scenes it seems like that battle has already been fought and technology won.

It is no longer just 2-0, 1-3 or - if you are a Barcelona supporter - 4-0 but all about a near-infinite number of figures.

Possession, territory, passes completed, the list goes on and on and on.

It's only a very dedicated soccer fan who would know that Bayern Munich midfielder Javi Martinez won an average of 2.6 balls in the air during each Uefa Champions League game so far this season. But processing this kind of data gives teams a great advantage, both in training and matches.

For goalkeepers, the guess of where a penalty might go is now a mathematical algorithm rather than an instinct.
The goalkeeper will know where the taker has placed previous penalties. If a player has hit 95% of his penalties to one side of the goal, which way do you think the goalkeeper is going to dive?

What this means is that the millions of pieces of data created must be analyzed by teams. Manchester City, for example, have 10 analysts working full-time on analyzing data, four just for the first team, so that no stat that counts - or at least worth counting - is left un-analyzed.

Pick geeks first

City defender Vincent Kompany has initiated a meeting with fellow defenders and the analysts once a week to discuss the data. The club have even experimented with making all this information public and, because of how popular it has been, are expected to resume this next season.

There was a time, not too long ago, when teams in school playgrounds were picked and the ones left towards the end of selection were often those more at home in front of a computer screen than in front of goal. Now Premier League clubs are picking them first.

"We've got a record for every shot across the top few leagues for the last five years," says Sam Green, advanced data analyst at sports data company Opta. "Beyond penalties, the tendency for people to shoot under certain circumstances and where that shot was from." "A lot of things can be done with the data. Some of the things we use it for is to look at how teams react to corners or other set pieces. How likely are they to concede a shot on a counter-attack afterwards?"

But there are limits to how far data can take you, particularly with the often distracted or influenced mind of a soccer player.

Intent

"I don't think any club is at a stage where they can use data comprehensively across every aspect of the game," says Mr Green. "There are things that we're not confident in predicting - which way they would turn, for example. "There's still an issue of intent. The shot went in the top left-hand corner but we don't know necessarily that it's where the striker was trying to put the ball."

What is often forgotten about the current data "revolution" - as some people call it - is that recording the information of soccer matches goes back a long way.

RAF pioneer

"Soccer is the original hotbed of analytics," Prof Chris Anderson, of Cornell University, and author of The Numbers Game, said. "There was an accountant and RAF wing commander by the name of Charles Reep who starting notating soccer games in 1950. "He develops a coding system, sits down at matches and does it for 60 years."

The games must still be recorded manually as automation is difficult in such a complex game. "His work has informed how [data] has reverberated in clubs through the years. "He notated with his own system all the events on the pitch and collected reams of data that he put into practice working with clubs in the 1960s, 70s and 80s. He was the original soccer analyst."

Mr Reep, through his findings, has been cited as one of the main architects of the long-ball game.
Another man with a reputation as a long-ball manager, Sam Allardyce, is seen as one of the main advocates of modern data use.

Mystery

Beginning in 2000 with a change in approach at Bolton, a company called Prozone was enlisted to provide detailed statistics that Allardyce has quoted and used time and time again. He believes his use of modern techniques improves performance in a way that can be measured. But even his data use is a mystery to some managers.

One, seen as a modern, progressive coach, is critical of the reliance of statistics at all. In a similar way to soccer broadcasts, video is now a key part of feedback from coaches. "I have never used Prozone. I don't use it because I don't believe [in it]," Tottenham Hotspur manager Andre Villas-Boas said in a press conference. "You always have to be very, very careful with statistics. It doesn't mean that we negate them completely - we just don't use them to the extent that people might think. "We have a scientific department that deals with that but we don't prepare our training or players based on the physical data we get from matches.

'Different approaches'

"The mind and how the player feels is much more important for us, rather than statistical data.

"For me it's useless but it varies from coach to coach. We all have different approaches." For a manager seen as one of the world's new breed of technically astute managers, it seems like the numbers do not add up.

If that's the case, he probably does not even know how many balls Javi Martinez has won in the air.

PITCH IN WITH THOSE FACTS
While some data is great for training staff and coaches, other stats are just interesting to drop into conversation

169 - David James has the most clean sheets in Premier League history, 169 in 572 games
126 - Morgan Schneiderlin has made the most interceptions in the Premier League this season
123 - Arsenal have seen the most opponents caught offside this season
88 - Stephane Sessegnon has been fouled more often than any other Premier League player this season
44 - Ashley Williams has blocked the most opposition shots in this season's Premier League
7 - No Premier League team has conceded more penalties than Aston Villa this season. Manchester United are the only side yet to concede one.

Stats courtesy of Opta. Correct as of 27 April 2013

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How to lead a Data team

How To Lead A Data Team

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Battle Royale: Computational Biologists Vs Machine Learning Engineers

Battle Royale: Computational Biologists vs Machine Learning Engineers

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Here’s a good place to start if you’d like to take a deeper dive into the differences between the two or read this article about mindsets and misconceptions. Office Politics in the Tech Space Circling back to the concern between Computational Biologists and Data Scientists with a focus on Machine Learning. The latest around the water cooler within the tech space is that those with a biological background who understand Machine Learning are looked upon as dangerous to the status quo.  But, as many of our candidates know, it’s important to stay on the cutting edge and if that means, upskilling in Machine Learning so you have both the human element as well as the mathematical, robotic components, then that is more marketable than just having one or the other. 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In coming back to speak with the Hiring Manager, it became clear the animosity is even more prevalent than first imagined. So, it’s important to get input from within the company and develop a unified story, a unified message across departments, and especially within the Life Science Analytics and Data Science teams as well. In other words, “keep people in the loop.” If it’s happening to this company, it seems other companies may be facing this same issue. However, it’s not going away and is creating a kind of competition between the old guard and the up-and-coming startups. For example, any new company is going to want to integrate AI and will be asking the question how best to integrate it into their structure. They might also ask how best to optimize the ads coming through AI. This is just another way of how companies are catching up, but also how people are catching up to the companies.  Technology is coming whether you like it or not. 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Data Engineers: The Workers Behind the Curtain

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A Data Engineer is a Unique Blend of Data Professional

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