A divisive metric for pundits and fans alike, xG or expected goals has worked its way into mainstream football coverage and analysis. But what exactly does it mean and how can it be used to inform betting decisions?
Expected Goals Explained: What does xG mean?
When referring to football, the term xG stands for ‘expected goals’. Simply put, xG is a metric that calculates the probability of a certain shot resulting in a goal.
Looking at a historical data set of shots taken from different positions and which of those resulted in a goal, expected goals shows us how many goals an average team could be expected to score from their shots.
An xG of 1.00, for example, indicates a guaranteed goal, which obviously doesn’t exist. A goalscoring chance with an xG of 0.40 meanwhile, tells us that if that shot was taken 10 times, you could expect it to hit the back of the net on four occasions.
Expected Goals Explained: How is xG calculated?
As we now know, expected goals measures the quality of goalscoring chances and the likelihood of them being scored – but how is it calculated?
Despite what some may have you believe, it’s actually quite simple. The quality of a shot is measured based on a number of variables, all of which should be pretty self-explanatory to any discerning football fan.
- Assist type
- Shot angle
- Distance from goal
- Was it a header?
- Was it a big chance?
- Did it occur in open play?
- Was it a set-piece?
The closer to goal a shot, for example, the higher the xG, as you would expect. Headers meanwhile, have a lower xG than shots and so on.
Expected Goals Explained: What can xG tell us?
Although undoubtedly popular among the stats community, the debate around what useful information we can actually garner from expected goals rages on.
Jeff Stelling once dismissed it as “the most useless stat in the history of football”. And while the Sky Sports presenter was obviously being a little unfair, his view is one shared by many.
Opponents like Stelling will tell you that if a team wins 1-0 against the run of play, that’s football. They take home the three points, regardless of what expected goals, or any other metric for that matter, says.
Of course this is true, no one is disputing that. What expected goals can do however, is give us an indication of how many goals a player or team should have scored on average, given the shots they had.
So when you hear someone say “Harry Kane should have scored there” or “Liverpool were so unlucky not to win”, we now have actual data to back it up, or rebut it.
Expected Goals Explained: How can xG be used in football betting?
Sports bettors are always looking for any edge they can get, and if used correctly, expected goals can provide exactly that. The final score of a match does not always tell the whole story, and this is particularly true of low-scoring sports such as football.
Understat is a great source of xG data. From their infographic below, we can see that Liverpool finished their recent 1-1 draw with Burnley with an xG of 2.18. Burnley’s meanwhile, was much lower at 0.45. The bigger the circle, the bigger the goalscoring opportunity.
This tells us that the Reds had more and better chances, and should have probably taken all three points. Of course anyone watching the game is likely to have known this instinctively. What expected goals does is give us some concrete data to validate this gut feeling. Football bettors can therefore use a team’s xG to analyse their form based on performances rather than simply results.
The risks of using xG in football betting
Unbiasedly judging team and player performances should help to predict future or long-term trends. But proceed with caution. Over time you would expect teams or players outperforming their xG data to eventually revert back to the mean. However this isn’t always the case. As already alluded to, Burnley have consistently outperformed xG models under Sean Dyche in recent seasons.
On top of this, xG data can be skewed by a number of factors, depending on how a particular match plays out. If a team scores early, they may well decide to sit back and protect the lead. This reduces their xG but would not necessarily mean it was a bad performance, especially if they manage to hold on. Similarly, the attacking team may also see their xG figures affected by the number of defenders behind the ball blocking their shots.
When analysing xG data you should also note that offsides are not recorded at all. This is true even if they were close decisions which could have lead to a decent goalscoring opportunity. Incorrect offside calls however, should now be less of an issue given the introduction of VAR.
Expected Goals: Final Thoughts
No one will ever be able to absolutely predict a football match. There is too much going on. But, when used correctly, there is no doubt that xG can be a useful tool for football bettors. As with any stat or metric, it is best used in context, preferably alongside tactical and video analysis.
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