xG, or Expected Goals, is the current metric of choice for the football stats community
and has gained some wider traction with its appearance on the BBC’s Match of the Day,
as well as plenty of comment from pundits and analysts who feel it’s either ruined
or solved football.
In fact, xG has done neither – it is a simple tool which can help our understanding of the
game if correctly understood.
What it cannot do, on its own, is predict results, nor does it rob football of any of
its charm.
Expected Goals, put simply, is a measure, based in probability, of whether a given shot
will result in a goal.
Looking at a historical data set of lots of shots taken from lots of positions, and looking
at which of those shots resulted in a goal, Expected Goals shows how many goals an average
team would be expected to score from those shots.
In other words, Expected Goals shows how good a shooting chance was, based on how likely
similar chances were to result in a goal.
An xG of ONE means a guaranteed goal (which doesn’t exist), while an xG of 0.7 means
that if that shot were taken 10 times, you’d expect seven goals.
The closer to goal a shot, the higher the xG.
The wider the angle from goal, the lower the xG.
Headers get a lower xG than shots.
An xG map for Swansea’s recent 1-0 victory over Liverpool shows a series of dots – the
larger the dot, the larger the xG or likelihood of that shot being a goal.
Overall, Liverpool’s shots yielded an expected goals of 2.31 vs Swansea’s 0.35, according
to this model from StrataBet [MUST BE CREDITED IN THE IMAGE].
Of course, Swansea scored a goal and Liverpool didn’t, but what this xG model suggests
is that Liverpool had more and better chances.
If you’d watched the game, you’d know this instinctively – what xG does it clarify
that with a number.
StrataBet’s xG model includes defenders’ positioning; other models, like Opta’s,
currently do not.
Obviously, a shot taken with defenders between the ball and the goal has a lower xG.
xG does not account for who is striking the ball, which is due to the reduced accuracy
of comparison that comes from introducing player identity.
However, it does show that players such as Harry Kane, Alexis Sanchez, or Romelu Lukaku,
who consistently score more than their xG total would suggest, are elite strikers – they
are able to score from certain positions more than the average player, which is what makes
them great.
xG is therefore useful for assessing strikers, as good ones will consistently exceed their
xG over time.
It’s also good for seeing which teams produce lots of good chances, or don’t, and when
combined with a team’s conceded xG, it can give a general indication of quality.
It is not that good for predicting match results and anyway, as Richard Whittall has written,
xG is best used in concert with tactical and video analysis, and in trends of five to 10
games – then it can be part of set of tools that explains why teams are blowing hot or
cold, and stats are always best in context anyway.
While xG is therefore helpful, it’s not going to solve football.
You can’t solve football – it’s too complicated and there’s too much going on.
What xG does well is show how good a team’s chances were in a match and which strikers
manage to score more than they should, or less, and both things are useful in increasing
our understanding of football, which can only be a good thing.