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

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Let’s compare two players from their 2022-23 seasons: Marseille’s Alexis Sánchez in Ligue 1 and Wolverhampton Wanderers’ Rúben Neves in the Premier League. Both players took exactly 63 shots last season (excluding penalties) but scored 12 and three goals respectively. Overall: An interesting book, which could have been much shorter to cover the material included or could have been more in depth. At times it felt like there was a lot of padding in the prose to get to some sort of pre-ordained word limit. The subject material is interesting, however I was left wanting more detail. So a good first effort in an interesting subject with room for the author to grow in future. Worth a read.

One unique and innovative feature in our expected goals model is the goalkeeper position feature, which allows us to estimate the probability of a goalkeeper making a save. It uses the distance of the goalkeeper to the shot (a proxy for their reaction time) and their position relative to the line of sight of the shot to the goal, including whether the goalkeeper was inside the penalty box and able to use their hands. Any book of this nature is cursed to be out of date as soon as it is released, case in point Liverpool's performance has not been as stellar this season as the period the book ends with, nor is there little opportunity to discuss how a new strategy to things, like Chelsea's might disrupt the staus quo. Expected Goals : The Story of How Data Conquered Football and Changed the Game Forever (2022) by Rory Smith is another book that looks at the rise of data and analytics in football. Smith writes about football for the New York Times. Two of those analysts, the academics Chris Anderson and David Sally fill much of the book. The two wrote a book ‘The Numbers Game’ about how statistics could be used in football and then moved to England to use their insights. However, I would've liked a more detailed description of how xG is actually calculated rather than just the categories used (e.g., location of the shot, strong vs. weak foot, cross vs. entry pass, air vs. ground, etc.). Plus, there are a bunch of grammatical errors, and that drives me nuts.Obviously scenarios like this reflects the limitations of expected goals. There are several factors that xG could not take into account in these scenarios : 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.

I can't give it full marks because I found it too shallow in its treatment of the metrics being used by the teams to achieve their goals. I didn't expect any math of formulas per se, but maybe more elaborate definitions followed by concrete examples would have sufficed. 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. With the recent surge of use of expected goals in punditry and mainstream football, xG has become almost ubiquitous in daily football talk. However it's good to remember that football is not played in spreadsheets or via mathematical models. There is always limitations to expected goals, and we must respect the reality of the performances that occured. Allardyce’s rudimentary approach to data evolved exponentially. Brighton, an unglamorous mid-table Premier League side, has punched above its weight and become mainstays in the league in large part because of how the way they used data in the transfer market. In 2017, they signed German forward Pascal Gross from unfashionable Ingolstadt who had just been relegated into the German second division. Hardly any club was keeping tabs on Gross but Brighton saw that the quality of chances he was creating was among the best in the league. It’s just that his teammates weren’t able to convert them. Smith’s work as a soccer correspondent for NYT has always been infused by a perspective that goes beyond the field, situating the sport in a wider cultural context. It doesn’t come as a surprise then that he adopts a similar approach in Expected Goals.

Over the years, Opta has collected numerous data points of in-game actions in all of the top football leagues. When creating the xG model, Sam Green and the Opta team analysed more than 300,000 shots and a number of different variables using Opta’s on-ball event data, such as angle of the shot, assist type, shot location, the in-game situation, the proximity of opposition defenders and distance from goal. They were then able to assign an xG value, usually as a percentage, to every goal attempt and determine how good a particular type of chance is. As new matches are played new data is collected to continuously refine the xG model.