What is Expected Goals exactly, what does xG mean in football terms, and last but not least – what is xG?!
It is a question spoke more and more, as the metric seeps into the game we all know and love. Well, more commonly abbreviated as xG, it is a model based on historical shot data that reflects the probability (on a scale of 0 to 1) that a shot will result in a goal based on the characteristics of that shot and the events leading up to it.
These variables or characteristics may include but are not limited to: The location of the shooter (distance and angle away from goal), if the shot was with a header or foot, the assist type, as well as other variables that would intrinsically affect the probability of a shot resulting in a goal. A value of ‘0 xG’ would represent a certain chance of the shot not being a goal, and a value of ‘1 xG’ indicates a certain goal.
It is important to note that not all xG models are equal, and some are better than others. Intuitively, xG models richer in historical shot data and other variables will result in an improved and thus more accurate model. StatsBomb’s expected goal model, from what I’ve seen, is the most granular in the industry and has been since 2018 as it includes ‘freeze frames’ on every shot. These freeze frames, powered by computer vision, include the position and pressure of the defenders and the goalkeeper’s positioning.
A guide to Expected Goals…
For example, if a player had a 1v1 against the goalkeeper and went round him, but from relatively far out, other xG models may value the opportunity as a relatively low-value xG or chance. This is because it does not have the defenders and goalkeeper positioning in the model to accurately reflect the high-quality chance like StatsBomb. Hence, StatsBomb refers to these models, albeit arguably harsh, as ‘naive xG’.
Last year, StatsBomb further improved their xG model by including the variable ‘Shot impact height’. This invariably affects the probability of scoring a goal from a shot.
The stat, model or metric of xG has gained much publicity in the world of Football. The big spike in interest started when the BBC’s ‘Match of the Day’ utilised xG. From the beginning of the 2017/18 season they displaying each match’s expected goals. Excitement and outcry followed. Many viewers didn’t know how to take the new metric.
Although MOTD xG’s first mainstream TV appearence, xG has been with us for longer than you might think. Expected Goals was first introduced in a study published in 2004. Subsequently, it grew to prominence among football analytical circles from 2012 onwards after Sam Green’s innovative article for Opta.
What is xG? it’s a bit like blue cheese
xG is a bit like blue cheese; some love it, some hate it. There are three different and distinct ‘camps’ when it comes to one’s opinion on xG. There is the die-hard pro xG camp of data analysts, statisticians and quantitative analysts. On the other hand, the well-versed and integrated within the world of football analytics.
To some, they are the ‘progressives’. The type that see football differently to the ‘traditionalists and realise that there is more to understand from football performance. than just the final result of a match or the standings in a league table.
There is the neutral camp who are ambivalent and do not harbour strong opinions towards it and maybe don’t quite understand xG.
And then there is the dreaded – ‘I f@*^!%g hate xG camp‘. These are your typical ‘old school’ football pundits or fans that view football ‘traditionally’. There have been many notorious or infamous rants on expected goals by these so-called ‘experts’. Craig Burley, on ESPN, and Jeff Sterling, along with several other pundits on Sky Sports, ripped into Expected Goals, calling it a ‘whole lot of nerdy nonsense’ [Burley in 2018], and ‘the most useless stat in the history of football’ [Sterling in 2017] with a look of anger, bemusement and frustration, as if xG is some farce that is taking over our beloved game.
I strongly advise watching the following two videos:
Craig Burley rant:
Jeff Sterling rant:
Whilst I certainly cringe a little and strongly disagree with their viewpoints when listening to their rants, I don my empathetic hat and try to put myself in their shoes for a moment. And immediately, it is clear that they do not understand xG at its core – evidently misinformed on how to use it as a metric.
“I hate xG”
I hypothesise that because xG is a metric or statistic derived from a model rather than an absolute stat that one can count such as completed passes or tackles or even shots on target, they find it difficult to get their heads around the jargon. Perhaps because of the name ‘expected goals’, they believe that it is a ludicrous statistic because it simply does not matter how many goals were expected to be scored in a match because the final score is all that matters.
Now, I don’t know anyone in or out of the industry of football analytics that will be popping open champagne bottles after a cup final if their team has just lost the match but ‘won on xG’ – if their team’s xG was of greater value than that of the opposition. No advocate for xG has ever suggested that it is more important than the final score-line. This is what everyone interested in the sport cares most about. That is what directly affects the league table or who progresses to the next round of the cup.
What is expected goals? A Swiss Army knife, that’s what
However, from my perspective, xG is a metric that is a bit like a Swiss Army knife. You can use it in so many different ways. By looking at a team’s xG over the course of a season, one can see how effective a team is at generating shooting chances offensively as well as preventing oppositional shooting chances defensively. xG is often used to evaluate and quantify the quality of the shooting chances an attacking player generates for himself. xG has also given rise to the birth of new metrics that those in football analytical circles will be familiar with, such as expected assists (xA) and post-shot expected goals (PSxG or xG2), a metric predominantly used to evaluate goalkeeper performance or finishing skill.
No stat is perfect. There are certainly limitations to xG, particularly when used to evaluate a team’s dominance on a per match basis. This is partly because it is challenging to assign an expected goal value accurately on a per-shot basis. Moreover, game state (whether a team is losing, winning or drawing at a certain point in the match) can significantly affect the match’s final xG. Also, because football is a very low-scoring sport with a lot of variance and luck, certain match events may dramatically affect xG.
For example, if a through-ball puts the striker through on goal, it likely results in a ‘high xG chance or value’ if the striker gets the shot off. But what if he doesn’t? What if a defender intercepts the through-ball? Teams can be dominant and create opportunities but not necessarily release the final shot off on goal, which is what ultimately counts towards xG.
Remember, Just 2%…
Shots only account for approximately 2% of all actions in a football match. So, we can’t just use a model or metric based on this to form one’s opinion on a single match. However, xG is not the only metric you can harness for this purpose.
More advanced metrics based on mathematical models can be used in an attempt to quantify a team’s dominance. For example; expected threat (xT), VAEP (Valuing Actions by Estimating Probabilities), as well as Non-shot expected goals (NSxG).
How and Where? Best Sites for Football Stats and xG:
So, you finally ask, ‘what is expected goals in football?’ has been answered. Now, how the bloody’ell should we use xG, then? Well, it’s simple – you use it like you would any statistic or model. Understand it fully, its strengths and limitations, and use it as a guide to form your evaluation. Don’t base your entire thesis on the xG of a single match. You’ll only be setting yourself up for misleading results.
A common phrase, particularly in the world of football, is that stats are or can be misleading. While this can indeed be the case, it is crucial to note that stats are only misleading if you decide to use or misleadingly interpret them. It is not xG’s fault that Craig Burley, Jeff Sterling, and Jim White do not know how to use it.
With the waves of data flooding the internet just finding reliable xG information may feel overwhelming. To help, the list below is, in our opinion, the top five best sites for football stats (xG). Expected goals isn’t an exact science, so don’t be alarmed if the data is different on varying platforms. Regardless, they will certainly be able to aid us in the hunt to best analyse what’s in front of us.
What does xG mean in football? Words by Guy Kaye – @GuyKaye2,
ZICOBALL Graphic by Sam Ingram