Football Explainers 23 June 2026 · 6 min read

What is xG? Expected goals explained

You hear it on Match of the Day, you see it in post-match graphics, and it’s all over football coverage. But what does xG actually mean — and does it matter?

The basics: what does xG mean?

xG stands for expected goals. It’s a statistical measure that quantifies how likely a shot was to result in a goal, based on where it was taken from and the circumstances surrounding it.

Every shot in professional football is assigned an xG value between 0 and 1. A value of 0.9 means a 90% chance of scoring from that position, on average. A value of 0.03 means roughly 3%. A penalty is typically around 0.76.

How is xG calculated?

xG models are built from large historical datasets — millions of shots from professional matches. Each shot is assessed against several variables to produce a probability score.

Distance from goal

The further from goal the shot is taken, the lower the xG value. A shot from 30 yards has a much lower probability of scoring than one from six yards — even if it goes in.

Angle to goal

Shots from a central position have a higher xG than shots from tight angles near the byline, even at the same distance. The amount of goal visible to the shooter matters significantly.

Type of assist

Was the shot from open play, a cross, a through ball, or a set piece? Research shows that shots following certain assist types are significantly more likely to result in goals.

Whether it was a header

Headers have a lower conversion rate than shots with the foot, even from the same position. This is factored into the model.

Goalkeeper position

More advanced models — sometimes called xGOT (expected goals on target) — also factor in where the shot was placed within the goal and whether the goalkeeper was well-positioned.

A simple example

A team creates three shots. Shot one: a one-on-one from ten yards — xG: 0.62. Shot two: a header from a corner — xG: 0.08. Shot three: a volley from 25 yards — xG: 0.04. Total xG: 0.74. Over many matches with these same chances, a team would score around three quarters of a goal on average.

What xG tells you — and what it doesn’t

The most useful application of xG is over time, not in a single match. A team that consistently generates high xG but scores fewer goals than expected is likely to see results improve — conversion rate normalises over time. A team scoring far more than their xG suggests may be enjoying a run of exceptional finishing or fortune that won’t continue.

xG is best thought of as a measure of chance quality, not outcome. A world-class striker will score chances a lower-quality player wouldn’t — their xG outperformance is skill, not luck. Context always matters when interpreting the numbers.

Where xG is less reliable is in individual matches. Football is low-scoring and single games contain too few shots for xG to be a dependable predictor. A team can significantly outperform their xG on a given day and deserve their win entirely.

xG and in-play betting

For those who bet on football, xG has become an increasingly useful tool — particularly for in-play markets. If a team is losing 1–0 at half time but has created 1.8 xG to their opponent’s 0.3 xG, the scoreline may not reflect the balance of play. Live odds might not fully account for that dominance, which can create value.

Similarly, a team winning 2–0 with an xG of only 0.4 may be in a more precarious position than the scoreline suggests. In-play markets often reflect the score more than the underlying performance data — and that gap can be worth watching.

Watchsport includes xG data in live match stats for supported fixtures, so you can follow the numbers in real time alongside the score.

Follow live match stats including xG in the Watchsport app

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