Soccer Analytics: An Interview with Chris Anderson

The age of Big Data is upon us. In industries throughout the world, the collection and analysis of data is a focal point of decision making.  Sports are no exception --  not surprising given that professional sports are billion dollar industries.  In the past decade, sports such as baseball and basketball have started integrating statistical and objective analysis into player evaluation and team management. Soccer --futbol, football, the Beautiful Game -- is slowly beginning to accept analytics as a decision-making tool. 

Cornell's own Chris Anderson is one of the leading innovators in the burgeoning field of soccer analytics. On campus, he is a Professor of Government and Labor Relations whose work primarily converges the fields of  economics, politics, and sociology.

Outside of  the classroom, Professor Anderson's interests lie on the soccer pitch, where he played as a goalkeeper in the German lower divisions.  Along with David Sally, Professor Anderson authored The Numbers Game: Why Everything You Know About Soccer is Wrong, a book that breaks many established conceptions in soccer and counters them with an objective, analytical approach -- all supported with careful statistical analysis. It is a great read for any soccer fan looking to nuance his/her view of the game, but it can also serve as a great introduction to anyone that is just becoming interested in the sport.

I hope to review the book later in the semester, mostly as a basic introduction to the field of soccer analytics. Professor Anderson is also co-partner of Anderson Sally, a sports analytics consulting firm that works closely with professional teams.

Professor Anderson recently took time out of his busy schedule (check out his recent interview on CNN ) to answer some questions for the Cornell Sports Business Society. 

Professor Anderson, first I’d like to thank you on behalf of the Cornell ILR Sports Business Society for taking time out of your busy schedule to do this interview. It is very exciting to see that a member of the Cornell community is one of the leading figures in the growing field of soccer analytics. First, would you mind giving us your personal definition of “soccer analytics”?

It’s become kind of a catch-all term for all kinds of things. I think it’s basically one thing that’s applied to another: first of all, it’s analytics – which is about collecting and interpreting information, evidence, data, what-have-you. That information can be quantitative or qualitative in nature; and analytics is not just about having information, but also about deriving meaning from it, and doing so in a systematic way.  

Wikipedia defines analytics as “the discovery and communication of meaningful patterns in data”, and I think that sums it up nicely. Analytics is becoming a common tool across lots of industries, and soccer analytics is simply analytics ideas and practices applied to the game of soccer. Within soccer, we’re talking about analytics with regard to playing the game, recruiting players, or player fitness – the various areas that affect a team’s performance.

How did you first become interested in analytics in soccer, and how did you start getting involved in the field?

For me it started with a love of the game. I've always been interested in understanding soccer as a game played by 22 people who have to make decisions both in isolation (e.g., do I pass, dribble, or shoot?) and together (as part of a team). We tell a story in the book about how I took an analytical approach to soccer from an early age; more recently, Michael Lewis’ book Moneyball got me excited about the potential of applying similar ideas to soccer.

Then I started a soccer analysis blog on a lark, and attended the MIT Sloan Sports Analytics Conference (which was pretty inspiring). As the blogging and analysis became more serious, David Sally and I started talking about writing a book about soccer analytics. That book eventually became The Numbers Game. I guess the lesson for me was that it’s fun to start small and go from there – the key to any of it is to stick with it over time.

Do you think the growing economic disparity between the richer clubs and the poorer clubs in the European leagues will help the field of soccer analytics grow even faster than it already is? There was a similar context in the mainstream emergence of sabermetrics, where Billy Beane had to look for ways to compete with the big money teams in the Majors.

That’s a good question, and I’m not sure of the answer. In principle, the clubs with less money to spend on superstars should be willing to try new ways of winning or to get more bang out of the buck for money invested in analytics. A great example of a club that did some fairly basic but very effective things coming out of analysis were Bolton Wanderers under their then-manager Sam Allardyce (who now coaches West Ham United). Bolton was able to do much better than their wage budget would have suggested.  

But the reality at many of the lesser clubs is that money is really tight, and clubs find it difficult to justify spending money on people, software, data, and computers to ramp up their analytics operations. So ironically, the better-financed clubs like Manchester City or Liverpool are spending more money and resources on analytics, and they benefit from those investments. By the way, Billy Beane is a huge soccer fan, and I’d love to see him give advice to soccer teams (and you’d only have to hire one guy) – but I don’t think he’s available!

One of the bigger and most counter-intuitive points you make in your book “The Numbers Game” is the importance of luck in the game of soccer – significantly more than in any other sport. Do you think that observation should have any effect on the way teams and fans analyze on-field performance?

I would hope so, but I’m note sure. It should be pretty logical. More randomness and luck means more noise in the data, and that should make fans and clubs look longer term. In statistics language, what you want is a bigger sample before drawing any kinds of firm conclusions about performance because outcomes can be too much influenced by chance in the short term. But of course, telling a fan or a coach not to worry about the last 2-3 games is likely to encounter resistance. So we have to divorce our role as fans and the emotion that comes with that from the reality of what the data really do or don’t tell us.

If patience is hard to come by – and it always is – then another thing the role of luck and chance should teach us is that fans and coaches might be well-advised to focus more on those aspects of a team’s or player’s performance that are more controllable or have less chance. Shot conversion rates are an example of a performance indicator that is less replicable than, say, producing high quality chances in the first place. The former regress more quickly to the mean than the latter.

On a similar note, we have seen the emergence of analytics in other major sports, namely baseball, where analytics are firmly entrenched. However, soccer is a very different game than baseball – it is much more fluid with fewer fixed events. How does that limit the extent of the objective analysis that can be used to view the game?

It doesn't really; people working on basketball and hockey, for instance – two sports that are fluid and team-based – have already shown us that quite a lot of interesting insights can be produced about soccer’s “cousins”. At the same time, it’s probably naive to think you can simply apply ideas from one sport – especially one, like baseball, that’s very different – to another. So you have to be careful, and every sport has to find its best ways of using analysis. More fundamentally, the nature of the game makes soccer analytics simply a harder set of analytical problems. But that doesn't mean it can’t be done.

One could say that soccer has always been a mathematical game, but in a different way than baseball in that it is a very geometric game. Formations have been a big obsession from the very beginnings of the sport, shape – mostly defensive -- is always emphasized by youth coaches, and triangles are a big part of the ideology of FC Barcelona. Do you think the close connection between soccer and geometry opens up other objective frontiers within the game of soccer?

It’s only natural. Soccer is a game of space and a game of timing, so when it comes to the spatial aspects, and the team aspects of players having to coordinate, I think there is a lot of potential here. It’s also an area that is easier to explain to coaches (say, on a blackboard or a computer screen) than a set of numbers.

Would you mind sharing with us any soccer analytics research you are currently working? Any upcoming books or projects we can look forward to?

Not at the moment. Actually, I am busy working on various projects related to my job as a political scientist in the Government Department. That’s keeping me pretty busy.

Lastly, could you recommending some crucial readings for any readers looking to start learning about soccer analytics?

Going back to your first question, I think it would be important for any aspiring analyst to get a good handle on “analytics” – analytical thinking, analysis tools (econometrics, statistics, etc.) – as well as soccer as a game. There are a variety of sources out there, and many of them aren't very technical (which is nice). My personal favorites that I recommend with regularity are books like Jonathan Wilson’s Inverting the Pyramid: The History of Football Tactics and Kuper and Szymanski’s Soccernomics. 

More generally, I would say that becoming familiar with soccer analytics does not imply only learning about soccer. I would always recommend reading (not just watching) Michael Lewis’ Moneyball and Jona Keri’s The Extra 2%.  Scorecastingby Moskowitz and Wertheim and Basketball on Paper by Dean Oliver are excellent, too. General texts explaining decision making and analysis like Daniel Kahneman’s Thinking, Fast and Slow or Silver’s The Signal and the Noise are very useful for understanding how people think about and interpret information.

Finally, I would recommend reading all the great material that’s now available courtesy of various analytics-focused blogs. For soccer, I would recommend socceranalysts.com and statsbomb.com. But there are also lots of great analysis blogs on hockey and basketball, for instance.