Expected Goals, or xG is a statistic that baffles even the best of us.
But in reality, it is not as confusing as you might think. Let me explain.
Simply put, xG is essentially a statistic used to measure the likelihood of a shot resulting in a goal.
The way that the probability is calculated is by looking at a huge data set that contains the information of many shots that were taken from various positions, and whether or not these shots were scored.
Ruud Van Nistelrooy is remembered as possibly the most ruthless finisher of PL history
So for example if we had 50 shots from a single location and 28 went in, the xG of a shot from that location would be 35/50, or an xG of 0.7.
This can show how many goals an average player or team(the average being calculated from the aforementioned dataset) would score from the shots they take during a match.
In essence, xG can show a viewer the quality of a shot by looking at similar shots and the percentage that they resulted in goals.
The xG of a shot can range between 0 and 1, with neither 0 nor 1 being possible.
An Expected Goals of 0 from a shot would mean that there is no chance of a goal from a specific shot, and an Expected Goals of 1 means a guaranteed goal from a specific shot.
One of the last old-fashioned poachers in the game, Mauro Icardi celebrates a goal for PSG
For example, when the xG of a shot is 0.4, should that exact shot be taken 10 times, 4 would go in on average.
Generally, the closer to goal the shot is, the higher to xG, and the wider the angle from goal the lower the xG.
Because data sets of shots from different positions and their probability of going in can vary significantly, different companies have different xG models. Some, more advanced models take into account the goalkeepers and the defenders’ positioning; others simply use the location of the shot as the measuring statistic. This means that some models are more accurate, and the xG of the same shot can differ hugely between different models.
xG does not consider the player taking the shot, because the variables at play would be too large and may make the models inaccurate.
Therefore, this uniformity can show us the quality of the striker of the ball; players like Jamie Vardy, Harry Kane, and Luis Suarez have consistently overperformed their xG over the course of their careers, showing us that they are clinical and can score shots that many other players would not be able to.
Jamie Vardy, one of the most clinical strikers in the world
It can do the same for teams; if they have a high xG it means they are producing many quality chances; if they do not then they are producing few quality chances.
Combine this with their conceded xG and you can get a pretty good understanding of how well a team should be performing. However, when doing this you must consult a larger sample, not just one match but maybe the whole season, as it balances out any freak matches that would cause the actual result to be drastically different from the expected one.
So by looking at xG, you can see which teams are clinical or lack the finishing touch in attack and defence, and you can do the same for individual players. It is a useful stat that enriches our knowledge of the Beautiful Game, but will not change it for the worse as some fans and pundits believe.