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

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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. 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. With 10.79xG created, Brentford have been the most proficient team in the league so far this season at creating goalscoring opportunities," said James Tippett, the founder of the xG Philosophy account and author of the book The Expected Goals Philosophy. 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. In 28 Premier League games this season Brighton have scored 28 goals and accumulated 28 points, which leaves them in 16th place, just 3 points clear of the relegation zone.

But the xG value doesn’t (and I cannot emphasise this enough) tell the full story of a game and xG works very well in conjunction with other determining factors, but much less so as a standalone. This statistical approach is especially useful for properly rating teams and players. From a club’s perspective, holding more reliable information about players can help it maintain a competitive advantage by identifying proverbial diamonds in the rough when scouting players. The account was simply a means of sharing Expected Goals data and trying to garner interest in the metric. Really interesting and accessible introduction to expected goals. I managed to get through the book in a 24 hour period. I was thoroughly gripped and learnt a lot. I found the comparisons between Moneyball and Brentford revealing and insightful, particularly how Brentford have utilised xG to exploit the transfer market. Tippett explores the differences between results and performance in an engaging and easily understandable way. The graphics, data and presentation of maths was easy to follow. Now the correlation for rank is improved to 89% and for points it is 83%. Please, remember that correlation is a nonlinear function. So an increase from 73% to 83% is quite a feat and shows that PSxG is informative whereas xG is not in this case.

Expected goals and their uses

Put yourself in Bryan Mbeumo's shoes in the 46th minute of last weekend's Premier League clash against Bournemouth. The ball has been squared onto your good foot and the far corner is gaping, you know exactly what to do. A crisp side-foot finish into an inviting net and you'll be off basking in the adulation. This is your moment. As soon as I started reading up about xG, I knew it could become hugely popular. It’s such a powerful metric for predicting future performance and it’s really intuitive once you begin to understand it. 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. I use a version of this approach in my own coaching. I always want to help my players find the best possible spaces in which to create the most dangerous possible chances. From that perspective, this book and xG approach is helpful.

The final defense of xG by its proponents, is that xG might not have correlation that are high enough to give you useful information, but at least it is the best we can do. First of all, this is simply not the case. There are a number of examples, for instance the Scottisch Premier League, where Shots On Target did better. But even if it were the case, then it is still a fallacy.

How is xG computed?

Yet, the story of xG and correlation becomes even worse. For we haven’t taken circularity into account. If you look at the formula for xG, all data providers have different ways of calculating xG. Nevertheless, it basically is: A different type of institution has also utilised the immense predictive ability of the Expected Goals method. Professional gambling syndicates have used xG to calculate accurate probabilities of events occurring. These companies use Expected Goals data to generate odds, which they compare to the bookmakers’ odds. The success of their businesses depends on their ability to make more accurate forecasts than the bookies. The Expected Goals method has allowed them to do this. Later, we will more closely examine the top secret gambling cohorts who have turned over millions of pounds through utilising xG. 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. Only when we fully embrace the Expected Goals method can pundits begin to more accurately comment of football. Only then can managers give more reasonable post-match interviews. Only then can the fans select the best players for their fantasy teams. Only then can we haul football out of the dark ages and into a more intelligent era of analysis. Many have described this as the worst individual performance by a striker in World Cup history…and it’s hard to argue when you see the chances he missed.

Much of his manifesto feels jarring at first but it is hard to disagree how, far too often, ‘in football, the result dictates the narrative.’ We as “fanalysts” are often consumed by fandom over analysis.Nevertheless, by including even more context as we do in our Bayesian model, you get even higher correlations. The context that we add is the following data from Wyscout. Please note that we do not include xG or any other expected something stat.

It’s certainly helped that people have seen the metric on mainstream broadcasts. Sky Sports, in particular, are increasingly using xG as a form of insight on shows such as Monday Night football. Update 30-7-2020] What a difference nine months can make! The original post was written in october 2019. Since then xG has been uprooted by PSxG or Post Shot xG, also known as xG Shots on Target. As it turns out taking looking primarily only at the shots that were actually on the target, i.e. shots that either scored or would have scored if no opponent would have interfered, we get a more informative stat. 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.To remind you: Ashes has looked at the top 5 leagues and the Russian league. Personally, I am more interested in the smaller leagues, especially the Eredivisie. And I am more interested in whether it helps to use xG in concrete player recruitment, especially in finding exceptional players. At my work at FBM we have found the striker Dalmau for Heracles. Not only did Dalmau become the #3 top scorer that season, we also predicted that he would be worth 1.75 million in transfer fee for Heracles the next year and Heracles did receive 1.7 million for Dalmau (700K transfer fee and Dessers, a striker valued at 1 million). 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.