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The Proliferation of Data-Driven Analysis in Football (Part Two: The Coaches)

Big data.

It’s the flavor of the moment, whether it’s used in conjunction with politics, business or sports, and given the growing ease with which organizations and people can collect data, it looks as if big data is here to stay.

And when the BBC Technology section runs an article on how data analytics is influencing football, you know it’s a hot, hot topic.

In part one of this series looking at data-driven analysis in football, we discussed the growing popularity and importance of statistics to football scouts, which extends to how football clubs run themselves as organizations and businesses.

A key player in the stats arena that we looked at was Manchester City, so it’s no surprise that the BBC article just cited starts with City as a club to look at: They, after all, employ 10 full-time data analysts just for the first team (and this was the picture, at the time of writing, almost six months ago), and club captain Vincent Kompany has realized the value of in-depth analysis, such that he’s reportedly started meeting with his fellow defenders and the data analysts to discuss their findings.

We started off our last piece looking at the role that pre-match preparation played in Simon Mignolet’s exciting penalty save from Stoke City’s Jonathan Walters on the goalkeeper’s debut for Liverpool—the importance of data analysis and the simple of collection of statistics did the job there, and will continue to do this job.

We will now discuss the coach’s role and use of statistics in part two of our four-part series on Business of Soccer, in which we’ll look at how big data and related technologies and trends have influenced and augmented the beautiful game. Parts three and four will look at the sports scientists and, finally, the fans.

 

Photo courtesy of The Telegraph.

Photo courtesy of The Telegraph.

Club Information

Let’s start with a fascinating Sports Illustrated article from Jen Chang, who talks about the use of performance analytics by Premier League club Everton and how it influenced ex-manager David Moyes’ preparation work.

And there are major repercussions on the tactics side of the game. Steve Brown, Everton’s First Team Performance Analyst, performs this exact role, where he analyzes information provided by Prozone (more on the data providers later) to develop game plans. Where are opposing full-backs usually positioned? What positional traits do opposing wingers exhibit? How can Everton prepare their team shape to take advantage of any habitual practices of next week’s opponent? As Brown says in the article, American forward Landon Donovan was often eager to solicit more information from Everton’s analysts during his time on loan at Goodison Park.

We can thus see the importance of opposition scouting in terms of tactical approach and how teams can prepare their own players to negate formations and systems, as well as take advantage of any possible habitual holes that are magnified. Add this tactical and positional information provided by data analytics onto detailed observations and reports prepared by specialist opposition scouts (such as this quite brilliant analysis done by former Chelsea scout Andre Villas-Boas via the Telegraph), and it could make for a comprehensive picture and extensive preparation.

The implications of this method quite naturally also extend and have applications beyond opposition scouting. By studying a club’s own players, managers can get a feel for how they can better train and mold them into all-rounded stars with fewer glaring holes in their games—and this not only means they can do tactical and positional work, but also fitness work.

We’ll look more in depth at the science of sports fitness in part three of this series (particularly a high-profile example at Liverpool), but we’ll also refer to one of many interesting applications of GPS technology: to track player movement, position and fitness.

Arsenal, with their aesthetically pleasing attacking movement, self-sufficient financial structure and new world-class stadium, are known for their modern approach to the game, and their use of GPS to monitor their own players won’t come as a surprise, and in the case of midfield starlet Jack Wilshere, according to a Guardian report, it was this technology that persuaded him to miss the 2011 Euro U21 tournament.

 

Independent Information

So all is well and good with regards to data analytics and statistical analysis employed by football clubs, but where does all of this information come from?

Well, there are a number of big players in the sports analytics scene, and Prozone and Opta are the two biggest names around, mostly because their scope and coverage extend far beyond just a single team.

To that end, Prozone has struck up a number of high-profile partnerships with the likes of Arsenal, Manchester United, Manchester City, Stoke City, Fulham and Wigan Athletic, and this self-styled performance analysis firm provides the information that helps the team preparation process of many other clubs around the world.

In the US, where as we covered last time Major League Soccer have been pioneers in the technological and analytical front, both DC United and Chicago Fire have struck up agreements with Prozone to provide technical and tactical analysis, while the US Soccer Federation itself also employs such data to aid its national team and referees association. Their work also helps the German Football Association, who does a similar thing for the German national football team.

Opta, whose succinct Tweets from a variety of accounts looking at different leagues complement a viewer watching football on TV, are probably as famous among fans for their one-word conclusions as they are among clubs with their information, but it is interesting that they seem to be more of a statistical analysis firm as opposed to “performance” per se.

What do we mean by this? This OptaPro blog will shine more light onto what exactly Opta does with its data. Opta is much more of an independent data provider, in that its information is gathered and supplied in a more third-party role, looking at league-wide trends and analysis, as the blog entry does for Premier League goal-scorers.

Opta is, as well, the official media partner of the Premier League, the Football League and the Scottish Premier and Football Leagues, and its focus is much more on the fan engagement level—official Premier League partners such as Barclays and EA Sports will be able to access use live Opta data, while other popular sites like EPLIndex.com and Squawka are built entirely on data provided by Opta.

But it is another kind of analytics project that Opta has done recently that really captures the imagination and the potential of such analysis.

Opta’s project with adidas on “The Engine,” in which a mathematical equation-based algorithm has searched out specific types of box-to-box, stamina-heavy players and will continue to do so over the course of the season. This collaboration looks on the surface to be an ambitious feature aimed at fans interested in player analysis and comparison, but in reality there could be big implications on the world of football scouting and coaching.

Will there be a day that third-party data analysts—not in-house analysts at clubs—take over all the information analysis functions of football clubs, much like generic call-centers and hardware manufacturers support different companies in the same facility?

Could Prozone come up with a standard set of coaching manuals and training regimes, based on their vast databases of performance-related data, that they could then sell onto clubs, academies and leagues as best practices as a “performance consultancy”?

Could Opta unearth players using specially designed algorithms to recommend to clubs as players they should be looking at?

How would clubs be able to turn down such offerings if both fans and they themselves knew that they are the organizations that have access to the widest range of data and as such should be the most reliable in their recommendations?

 

League-Wide Information

As we ponder the future roles of data providers like Prozone and Opta, we should also keep an eye on the present, where sports firms like adidas are expanding quickly and aggressively into the coaching analytics field.

We discussed above adidas’ “The Engine” project; in 2013, its miCoach Elite system will be implemented across MLS to provide real-time data available to both coaches and fans, who will be able to track performance levels to the minutest detail (if they wish) while the match is going on.

In part two of this four-part series on the proliferation of data-driven analysis in football, we’ve looked at the role data is now playing in the coaching arena. Most of the analysis is done pre- and post-match to prepare and debrief players and managers on what to do next, but when real-time data becomes widely available, everyone comes under heavier attention, and the pressure to perform becomes higher than ever.

As we look ahead to part three on sports scientists, there is already plenty of food for thought related to how all this information—and all the players in this field—can have ramifications for the beautiful game in the future.

Stay tuned.

This piece first appeared on BusinessofSoccer.com, where I cover business and marketing strategy, globalization and technology in football.

The Proliferation of Data-Driven Analysis in Football (Part One: The Scouts)

It’s Saturday, August 17, 2013. The first English Premier League game of the 2013/14 season, Liverpool vs. Stoke City, is televised and about to come on TV at 12:45pm British time, the standard lunchtime kickoff.

As you sit there on your couch looking at the meticulous passing game implemented by Brendan Rodgers, you may well call up the Squawka second screen app on your iPad to look at the individual passing stats of Philippe Coutinho, or check out Daniel Sturridge’s shot accuracy on the FourFourTwo Stats Zone app on your iPhone.

In the second half, after Daniel Agger’s handball in Liverpool’s penalty area leads to a Stoke penalty in the dying minutes of the game, and as Jonathan Walters steps up to take the spot kick, an infographic is shown briefly on screen, reflecting Walters’ last five penalty kick attempts, hits and misses—a first such offering on televised Premier League games, courtesy of BT Sport.

At first glance, what can be considered as elementary, basic, interesting statistics for us fans seems to augment the experience and inject a little more color into discussions during and after the match, but there’s much more to that than meets the eye.

Take, for example, the admission from both Simon Mignolet—he saved Walters’ penalty, by the way, and won the three points for Liverpool—and Brendan Rodgers in their post-match interviews: Yes, there was an element of luck in the penalty save, but it was the pre-match preparation work that Mignolet did with his goalkeeping coaches that helped prepare him for the big moment. Or, in other words, they did their homework.

Take also the analysis that has been written about Mignolet, comparing him with the departed Pepe Reina in terms of goalkeeping stats—saves, punches et al, and we see the reasoning behind letting go of a want-away goalkeeper who hadn’t been in his best form. Reina’s wages will no doubt have been a factor in Rodgers’ decision in letting him go, but make no mistake: There will have been statistical backing to the outcome, and the choice of successor in Mignolet.

Beyond this particular game and instance, there’s an undoubted proliferation in data-driven analysis in football, so much so that even the Economist has run a feature story on it. In this four-part series on Business of Soccer, we’ll take a look at just how the boom in big data and related technologies and trends have influenced and augmented the beautiful game, and how they will continue to do so. Let’s look first at the scouts. Parts two to four will look at the coaches, the scientists and finally, the fans.

Scouting Performance

Every big sports fan will have heard of Moneyball—the concept that stats can show a side to players that perhaps the conventional wisdom of looking at performances on the field might not. It was this idea, of course, that led the Oakland Athletics to assemble an impressive team using under priced players with good performance statistics. At the heart of this was their general manager, Billy Beane, who advocated statistics-based analysis as part of his transfer strategy.

The most high-profile application of Moneyball in English football is Liverpool, whose current owners, John W. Henry and his Fenway Sports Group organization, just happen to be the owners of the Major League Baseball team, the Boston Red Sox, whose adoption of sabermetrics in sports was featured in the 2011 Moneyball film.

Of course, Liverpool fans will in all likelihood recoil in horror when they hear of Moneyball. It brings back unpleasant memories of the Damien Comolli and Kenny Dalglish era, where the term was associated with big-money flops like Andy Carroll, Stewart Downing and Charlie Adam. But underlying it is a statistical approach that more and more football clubs have taken on board.

Let’s start with Manchester City, whose use of data and statistics and whose Performance Analysis group have increased and expanded as quickly as their global reach and rise as a footballing superpower. The Performance Analysis team is headed by Gavin Fleig, who has extensive experience in coaching, sports analytics and performance analysis, and has enjoyed stints with Bolton Wanderers and Newcastle United in similar roles.

As it turns out, that old-fashioned manager who plays old-fashioned, route-one football (or so he is constantly labeled), Sam Allardyce, was actually a pioneer in the use of data and analysis in English football, and it was during his reign at both Bolton and Newcastle that saw Fleig get heavily involved in his expert areas at both clubs.

In a quite brilliant and comprehensive interview conducted by Zach Slaton for Forbes, Fleig discusses his background in a Premier League environment that still hadn’t fully embraced statistics, working with a like-minded team under Allardyce with an aligned vision with statistics at its core, and the transition that he encountered upon moving to an ambitious and forward-thinking organization at Manchester City.

The nuts and bolts of the interview are contained in the link above, but what catches the eye is the extent to which the data and analytics are used at almost every level at the club. Even more impressively—and we will mention football statistics resources like Opta and Prozone in the coming parts of this series—City develops their own data for analysis.

In other words, they capture their own information to share across the club and to use across all levels of players, starting from the academy, and it is with that information that the coaches at City determine which players are best used in which positions, and what key areas of their games have to be improved. Player development thus lies at the core of statistical analysis in football.

In Slaton’s article, Fleig also mentions his take on Moneyball in a City context. Particularly interesting is the fact that not only is data used to find better players, but “how much value they will bring into our business as well.” He goes on: “Our focus in the last three years therefore has not necessarily been finding undervalued talent…but in order for us to get the club from where they were to where they are now in four years it required a certain level of investment in the top players around Europe.”

Sabermetrics in football, then, serves more than just the football side: It’s about the football club as a business as well.

Scouting Business

But we can’t really be shocked about that emphasis on business, can we? After all, with the growing amount of investment in the Premier League (the 20 clubs in the league spent a record £630 million in this summer’s transfer window), the (apparent) advent of Financial Fair Play and the increasing focus on Germany’s Bundesliga as a model for a financially sound yet fan-friendly league, money is an integral part of the game. Just take a look at the outrage that Gareth Bale’s world-record transfer generated taken in the context of the current economic climate.

So when relatively-unknown Michu signed for Swansea City last summer and went on to exceed all expectations in an exciting Michael Laudrup team, all the talk was around how exactly he managed to pull off such a coup for such a low price (Michu cost £2 million from Rayo Vallecano).

Correction: Michu was a pleasant surprise, but only for those who didn’t bother to do the background check. The pundits and scouts who did look at Michu, an unfashionable name playing at an unfashionable club in Spain, came to the conclusion that he would be worth a punt given his performances and statistics, and lo and behold: He became one of the best Premier League finds in many a season.

When it comes to finding value for money—taken a leaf right out of the Billy Beane book—there are many data provision services that work with professional football clubs to supply analytics and numbers that influence their transfer strategies and purchasing decisions.

Prozone is a well-known name in the professional circles—we’ll have much more on them in part two on coaches—but football fans who get their statistical fill from OptaJoe’s cleverly worded Tweets during matches may not know that Opta actually have an OptaPro division that partners with football clubs and provides exclusive data. As John Coulson, the head of professional football services at OptaPro, puts it, “The biggest area we’re involved with now is player recruitment. No team will sign a player based on data alone, but it’s increasingly a shortcut to a shortlist.”

A simple but effective analogy was offered by Blake Wooster, Prozone’s business development director in the previous quoted article: “It’s like when Amazon tells you other books you might like after a purchase. A coach might not have heard of a player in the Polish second division—but he might have similar attributes to the guy he’s looking at in League One. We are just increasing the due diligence process.”

Essentially? It’s about finding those players who have the potential to become stars, not buying stars outright. It’s about finding quality in proportion to price. That’s the chief mandate for scouts these days, and they are accomplishing it with the aid of extensive statistical analysis.

But it’s not just Premier League clubs that are part of this next big analytical wave in football. Far from it. In fact, it’s been most used in the country that Moneyball as a concept first proliferated.

In Major League Soccer in the US, which is known for its innovative use of technology—more on that, again, in part two—sports analytics have migrated from the traditional American sports to the beautiful game, and the New England Revolution (also based in Boston) have been one of the pioneers of analytics in the American scene.

This NESN article, while not quite as comprehensive as Slaton’s interview with City’s Fleig, nonetheless describes in detail what the Revs are trying to do with their own data analytics program, and their objective is not unlike City’s. There is a focus on scouting and bringing in players that suit their system, but also an eye on the academy in terms of player development and training.

There is certainly life after Steve Nicol, who was famous for his eye for talent during his time as the head coach of the Revolution.

Scouting the Future

So that’s what football analytics are up to when it comes to the scouting side of the game. Statistics have come a long way, and the rising popularity that it enjoys (along with easy access for scouts, coaches and fans alike) reflects a growing trend in its actual employment.

But where next for analytics? Will companies like Prozone and Opta still dominate all the scene, or will more clubs follow the Manchester City practice and collect their own data by themselves?

We don’t know yet, but a tantalizing new app has been developed none other than that tactical master, Rafa Benitez. His Globall Coach app, which started as a tool to store his own notes and formations on his iPad, has been enhanced to include a Scout Tool that allows scouts to create animations, line-ups and notes on players, teams and matches.

The target audience is still professional clubs—even though they do offer different versions for different levels—but the key is that scouts can add data themselves.

Looks like the trend for scouting-related analytics is the creation and sharing of customized data by the scouts themselves, for information that best suits their purpose.

In part two in this series on big data trends and uses in football, we will take a look at how data and analytics are used by coaches. Stay tuned.

This piece was my first for BusinessofSoccer.com, where I’ll be covering business and marketing strategy, globalization and technology in football.