The Expected Goals Philosophy: A Game-Changing Way of Analysing Football

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It is obviously early days and it is difficult to draw any hard conclusions based on the small data set of the 2023/24 season so far, but xG Philosophy have their own take on why Brentford haven't matched their xG to date in the Premier League. Rather than try to be inclusive and understanding of the limitations of the xG model in the mainstream, the writer (James Tippett) is completely exclusive, and devalues the opinion of a large portion of football fans. The Expected Goals method is not going to be for everyone. Some people just aren’t interested in looking at football through the lens of data and analytics. That’s absolutely fine. Part of the book is devoted to xG’s role in soccer scouting and the example of Brentford, owned by Smartodds founder Matthew Benham. Benham decided to use expected goals data to lead Brentford’s transfer strategy following their promotion to the Championship in 2014. 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.

The increased exposure which the metric has seen over the last couple of years is simply a drop in the ocean of what is to come. The reason why is simple: Every footballing judgement ever made is based on an analysis of the performance of teams or players. And the Expected Goals method offers by far the most advanced, profound and accurate gauge of performance. The longest section of the book explores expected goals’ role in betting to date. Tippett describes how Smartodds produced their own expected goals model to identify value in betting markets and place money on teams that the model recognised as having a better chance of winning than the bookmakers’ odds suggested. The parts about gambling and how to make money were the least interesting as that isn't why I watch football. It seems like only the rich syndicates will ever make.money from that. This can for example be seen in the Premier League at the difference between xPoints based on xG and xPoints based on PSxG. xPoints are expected points scored in the league based on either xG or PSxG.

Chapter 1: Expected Goals

I’m not anti-gambling, and I don’t mind a small tipple myself, but in many places the book feels like an advert for Smartodds’ services, which could be dangerous in the hands of someone susceptible to gambling addiction. Whilst the location of a shot forms the main basis of its danger level, other factors also play their part. Shots which come from crosses are considerably harder to convert than shots which take place when the ball is standing still. Whether the shot is headed, volleyed or hit from the ground also affects its chance of success. So too does it matter whether the effort is taken on a player’s weaker foot. Analysts account for a whole range of such factors in their Expected Goals models. This book challenges us to think differently about the game we took for granted. Tippett offers Expected Goals as, ‘ not simply a statistic which can be used to analyse performance. It is a philosophy that challenges the rigid, entrenched and outdated way we talk about the beautiful game.’ He highlights how conservative the game often is, closed off to differing insights and approaches where ’those lobbying for analytics to become more mainstream have been met with stiff opposition.’ These can include which players are over-performing, how adept they are at getting into the right positions and who is benefiting from (or being hindered by) the quality of their teammates.

Besides the numerous typos (quite unacceptable) in the book while working with numbers, it also fails to incorporate some well-known phenomena which could make the book a bit more insightful. For example, it mentions the importance of shooting from stronger/weaker foot several times, but fails to mention a single time that two-footed players (somewhat unsurprisingly) tend to have a better chance outperforming their xG (Heung-min Son, Mason Greenwood). This could be based on the number of games or shots taken but it’s also important to consider the number of actual goals against the expected goals. Using xG to inform your betting What was glaring about Mbeumo's chance versus the Cherries was that it missed Neto's net entirely, which is particularly surprising given that last season Brentford had the best shots-on-target percentage (36.8 per cent) in the entire division. The score granted to each shot is based upon analysing thousands of similar shots across within a massive database and assesses everything from distance and angle, to strong/weak foot, to whether the assist was a high-speed cross or a simple pass.It only takes a handful of good chances to be missed (0.50xG or thereabouts) for your team to be underperforming by a couple of goals," adds Tippett. At the end of any given match, the Shot Probabilities from either side are added up to reveal the Expected Goals scoreline from a match. For example, suppose that Arsenal play against Manchester City. The London side have six shots over the course of the match, but they are all long shots from distance with a Shot Probability value of 0.1(xG). The Gunners will have amassed a total Expected Goals score of 0.6(xG). Over the course of the same match, Man City only have two shots at goal, but they are both from close range. Suppose that one shot is worth 0.3(xG) and the other is worth 0.4(xG). Man City’s Expected Goals score over the course of the ninety minutes is 0.7(xG). Thus, the xG scoreline from the match would be Arsenal 0.6(xG) – 0.7(xG) Man City. The scoreline would reflect the fact that Manchester City performed narrowly better than their London counterparts. Last weekend @Holstein_Kiel's Steven Skrzybski broke the all time record for an expected goal, with just 0.3%. Essentially, xG indicates how many goals a team could have expected to score based on the quantity and quality of chances that they created in a match. Fans often come away from football matches thinking, “We created much better chances then the opposition, we definitely should have won”. The Expected Goals metric is a way of quantifying these scoring opportunities, allowing a better insight into the ability of teams than the actual scoreline does.

There are echoes here of the Moneyball technique popularised by the Oakland Athletics in baseball (which is referenced several times) and Tippett draws a connection between it and xG via their facility to refine gauging a player’s ability to one number. The weaknesses of xG Likewise, a one versus one from the edge of the box would be worth the same value, even if there were 6 defenders between the striker and the goal. Sorry to correct you, but xG suggests that this player is actually scoring more goals than would be expected of him. 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 would have loved to heard more about defense and goalkeepers and how to measure their performance in terms of xG. Also, a bit more about how strikers such as Michu and Benteke were briefly overvalued due to one good season. This is mentioned once but not really explored any further. Though we’re sure there’s some sort of use for xG somewhere, it does seem like an unreliable statistic to judge a match by.