Expected Goals: The story of how data conquered football and changed the game forever

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There is something self-contradictory about The New York Times journalist Rory Smith’s recently released book, Expected Goals. He charts the data revolution in football, the way statistical analysis has become central to how clubs operate. Yet, this narrative on numbers is completely bereft of graphs and formulae of any sort. It’s what initially drew me to the book. I can't even figure out who could be a good audience for this book. An average fan, trying to dip his toe into advanced statistics? The book litters him with way too much numbers while teaching precious little. Someone, who is more interested in the depth of football and/or advanced statistics? The deepness of the actual coverage of xG here is extremely shallow, offers almost no insights on modeling, mathematical or any other level. I can't picture anyone who would like more than a third of this book. This is where xG comes into play. Expected Goals uses various characteristics of the shots being taken together with historical data of such types of shots to predict the likelihood of a specific shot being scored. Since xG is simply an averaged probability of a shot being scored, a team or player may outperform or underperform their xG value. This means that they could be scoring chances that the average player would miss or that they could be missing chances that are often scored.

Also, while we know that Beane's As achieved incredible success as underdogs in a league that was designed to allow money to pretty much buy titles, the same cannot be said for most of the teams examined by Smith. Yes, some of them achieved immense success, but they were also the same teams that had access to astronomical amounts of money. How much money do you ask? Oil money, buy-the-metrics-company-so-that-no-one-knows-our-secrets money. But that is also part of the Moneyball way of doing things, according to Beane himself; that record-breaking signing makes sense if the data says the value is there, bargain or not. So, how does this relate to xG? Well, Lukaku’s total xG for the match was 1.98, meaning that he could have easily scored two of these chances. This shows us that Lukaku severely under-performed during the match.Expected Goals data is collected by several different data companies, football clubs and betting firms. The main provider of xG stats to media companies is Opta Sports, who claim to collect the most complete dataset for the Premier League, English Football League, Scottish Professional Football League and many other divisions across the globe. Opta’s data experts have analysed over 300,000 shots to calculate the likelihood of an attempt being scored from a specific position on the pitch during a particular phase of play. Since granular details aren't available success stories are generally illustrated by modest transfer fees for players that turn out to be brilliant, or mention of set piece prowess. One interviewee used a prior recommendation of a younger De Bruyne to illustrate the value of his data, a discovery less impressive when you realise he had already been bought by Chelsea and was only on loan at Bremen at the time. Without the precise details it was therefore impressive that Smith had put together a coherent but interesting narrative thread.

The main criticisms of expected goals often appear in scenarios where the metric isn’t being applied correctly. The most common of which is at the game level. A team having a higher xG total in a match doesn’t necessarily imply that they should’ve won the game. xG is only measuring chance quality and not the expected outcome of the game. It’s a sentiment that resonates with me. Over the last couple of years, I have developed a disdain for the suffusion of data ino the sport, primarily born out of my inability to understand or appreciate the changing lexicon of footballing discourse. It goes without saying that the increased prize money available and the fierce competitiveness within the sport has lead to clubs seeking to eke out any possible legal advantage to get an edge over their rivals. xG can not only be used to predict the winner of a soccer match, but also which player might score or assist a goal.where it is often, ‘more important to be recognisable than to be intelligent’. The key concept at play is to give us better insight not only about what happened but also, ‘what should have happened’ a method Tippett sees as ‘an antidote to the disease of randomness.’ I was coming from the author's previous book on the subject, The Football Code, which was a disappointment. I found it too general, too simplistic, way too repetetive. Thus, my hope was that this book would offer a deeper look at Expected Goals, its applications and foundations. Yet, again, I was disappointed. He also offers helpful information on important things to bear in mind, including xG’s assertion that a team who creates a few big chances are more likely to win a soccer match than a team who create numerous small ones, even though their xG may be lower than their opponents. As an Argentinian, I have been a football fan (it's actually spelled Fútbol, but whatevs...) my whole life. It is our first love: Fútbol, then our mothers (and believe me, they are well aware of their place).