What Does xG Mean in Football? Expected Goals Explained

xG means expected goals in football, it measures how likely a shot is to become a goal, based on the characteristics of that chance. Add a team’s shot values together and you get its total xG, an estimate of the goals expected from those opportunities, not a replacement for the actual score.

So when your team loses despite having the higher xG, the numbers aren’t changing the result. They’re helping explain the quality of the chances behind it. That distinction makes the statistic useful, and stops plenty of unnecessary football arguments.

What does an xG number actually mean?

Each shot receives a value between zero and one. The higher the number, the greater the estimated chance of scoring. A shot worth 0.20 xG has an estimated 20% chance of becoming a goal; a shot worth 0.80 xG has an estimated 80% chance.

Shot value	Estimated scoring probability	How to read it
0.20 xG	20%	About two goals from every 10 similar attempts over time
0.30 xG	30%	About three goals from every 10 similar attempts over time
0.80 xG	80%	About eight goals from every 10 similar attempts over time

These are probabilities, not promises. A 0.30 xG chance can be scored or missed, and the figure doesn’t mean every set of 10 similar shots must produce exactly three goals. It describes the expected conversion rate across many attempts.

How is xG calculated?

An xG model compares a chance with historical shots that have similar characteristics. It uses those past outcomes to estimate the likelihood of a goal. Opta’s model, for instance, draws on historical data covering nearly one million shots, according to BBC Sport.

The factors used depend on the model, but common inputs include:

• Distance: how far the player is from goal.
• Angle: whether the attempt comes from a central position or a tighter angle.
• Body part: whether the player shoots with a foot or heads the ball.
• The previous action: whether the chance follows a cross, through-ball, dribble or another type of play.
• Player positions: some models also account for the goalkeeper and surrounding attackers and defenders.

This explains why counting shots alone can be misleading. A central attempt inside the penalty area and an effort from a tight angle aren’t equally valuable. Hudl also explains that, from the same distance, shots with the feet are generally more likely to produce goals than headers.

Think of xG as putting a number on the difference between “he had a shot” and “he had a really good chance”.

How do you get a team’s total xG?

A team or player’s total comes from adding the xG values of their shots. The same method can cover one match or a whole season.

Say a striker takes three shots worth 0.20, 0.30 and 0.80 xG: together, those attempts produce 1.30 xG. That is the combined expected return from those chances, not a claim that the striker can score a fraction of a goal.

Individual shot values sit between zero and one, but a match total can exceed one because it combines multiple attempts. And that total doesn’t tell you how the chances were distributed: the underlying shot values still matter.

Why can a team lose with higher xG?

Because expected goals and actual goals measure different things. One estimates the scoring probability of the shots; the other records what went in. A lower-probability attempt can become a goal while a much better chance is missed.

Having more xG therefore doesn’t guarantee victory. It gives you another way to assess the chances created, beyond looking only at the final score.

In our view, “we created better chances” is a more useful way to discuss a favourable xG total than “we won on xG”. It keeps the argument about performance without pretending the result was different.

A real example: Bryan Mbeumo’s goals and xG

Bryan Mbeumo scored 20 Premier League goals in the 2024-25 season despite recording 12.3 xG, according to BBC Sport. His actual goals were well above the expected return from his chances.

That is what people mean when they say a player outperformed his xG. Scoring fewer goals than the expected total is usually described as underperforming it. Clubs use these comparisons to add context to finishing and player performance.

But don’t turn one comparison into a permanent judgement. Hudl explains that identifying players who consistently stand out for finishing requires a sufficiently large sample. Its broader finding is that generating shots from valuable positions is an important part of what separates good forwards.

So a striker with high xG isn’t simply a player who “should have scored more”. That number can also show that he’s repeatedly finding good shooting opportunities.

Why do different apps show different xG figures?

Different models don’t always use the same information. One might assess location, angle, body part and the preceding action. Another might also consider the goalkeeper’s position and where defenders are standing. Those differences can change the value assigned to the same shot.

Hudl gives an example in which a basic model rates a chance at 0.30 xG, while a model recognising that the goalkeeper is out of position rates it at 0.65. The extra context changes the estimated difficulty.

Our advice is to use the same provider when comparing players or tracking a team’s performances. Otherwise, part of the difference you’re discussing may come from the models rather than the football.

Do penalties count towards xG?

Penalties can be included in xG totals. Hudl explains that many models assign a penalty a fixed value of 0.76 xG, while its updated Statsbomb model uses 0.78. So there isn’t one penalty value shared by every model.

Non-penalty xG removes penalties from the expected-goals total. It lets you look at the chances generated without that contribution. Analysts also separate xG into open-play and set-piece figures.

Other xG terms you’ll come across

• xG against, or xGA: the expected goals allowed to opponents. It describes the chances conceded rather than the chances created.
• Expected goal difference: a team’s xG minus its xG allowed. It compares attacking output with the chances given up.
• xG per shot: the average expected-goals value of a player’s or team’s attempts, used to assess average shot quality.
• Post-shot xG: a measure calculated after the shot that considers placement and, depending on the model, goalkeeper positioning. It is commonly used to assess goalkeepers’ shot-stopping.

What can xG tell you, and what can’t it?

xG helps you judge chance quality, compare goals with expected returns, and look beyond a result. Football clubs also use it in recruitment and performance analysis.

But it remains an estimate built by a model. Different models can disagree, individual chances can produce unexpected outcomes, and actual goals won’t always match xG over a given period.

In conclusion, the best question to ask when xG appears on your screen is: What chances produced that total? The number becomes much more useful when you connect it to the football you watched.