Last Updated on 26/03/2026 by Andy Clark
If you spend any time looking at football betting tips, reading match previews or watching Premier League coverage, you will have come across the term xG. Expected Goals has gone from a niche analytics concept to a mainstream part of how football is discussed, analysed and bet on.
Broadcasters show it on screen after matches. Managers reference it in press conferences. Bookmakers use it to price up markets.
But what exactly is xG, how is it calculated, and how can you use it to make better betting decisions?
What is Expected Goals (xG)?
Expected Goals is a statistical model that measures the quality of goal-scoring chances in a football match. Rather than simply counting shots, xG assigns a probability to each shot based on a range of factors – how it was created, where on the pitch it was taken from, what type of chance it was – and aggregates these probabilities into a single figure representing how many goals a team would be expected to score from those chances.
An xG figure of 1.4 means a team created chances that, based on historical data, a team would score from approximately 1.4 times out of every similar set of chances. It is not a prediction in itself – it is a measure of the quality of opportunities created.
xG was developed by football data companies, including Opta and StatsBomb and is now the most widely used advanced football statistic in both analysis and betting.
How is xG calculated?
The model is built from an analysis of hundreds of thousands of historical shots. Each shot is assigned an xG value between 0 and 1 based on the probability that a shot from that position, in that situation, would be scored.
The key factors that affect a shot’s xG value include:
Location. A shot from six yards out has a much higher xG than one from 30 yards. The closer to the goal and the more central the position, the higher the probability of scoring.
Assist type. A chance created by a through ball or a cut-back cross from the byline carries a different probability than one following a long-range pass or a set piece.
Situation type. Headers have lower conversion rates than shots with the foot. One-on-ones with the goalkeeper have higher conversion rates than shots from wide angles under pressure.
Body part. Headed chances carry a lower xG than foot shots from equivalent positions.
Defensive pressure. Some models account for how many defenders are between the shot and the goal.
Each shot’s xG is between 0 (almost impossible to score) and 1 (near-certain goal). A penalty is typically assigned an xG of around 0.76. A tap-in from three yards is close to 1.0. A speculative effort from 35 yards might be 0.02.
The team’s total xG for a match is the sum of all individual shot xG values. A team that creates five shots with xG values of 0.4, 0.2, 0.3, 0.1 and 0.15 has a total match xG of 1.15.
xG Variations – What the Different Versions Mean
| Metric | What it measures |
|---|---|
| xG | Expected goals for a single match – the number of goals a team was expected to score from their chances |
| npxG | Non-penalty xG – expected goals excluding penalties, giving a cleaner picture of open-play chance creation |
| xGf | Expected Goals For over a season – a measure of how well a team creates chances across multiple games |
| xGa | Expected Goals Against over a season – a measure of how well a team restricts opponents’ chances |
| xPts | Expected Points – the number of points a team would be expected to collect based on their xG across a season |
npxG is particularly useful because penalties skew xG figures. A team that wins three penalties in a game will show a high xG but may have actually created few quality open-play chances. Removing penalties gives a more accurate picture of attacking quality.
xGf and xGa over a season are more reliable than individual match xG figures, which can be volatile. A team consistently outperforming their xGa is defending and keeping well. A team consistently underperforming their xGf may be due for a run of better results.
How to Use xG for Betting
xG is most useful as one input in your betting analysis rather than a standalone signal. Here is how to apply it across different markets.
Match Result and Winning Margin
Compare the xG of both teams in recent matches. A team consistently generating xG of 2.0+ while keeping opponents below 1.0 is creating genuine quality chances and defending well – this should be reflected in the match odds. If the market has not fully priced in this form, there may be value.
Be cautious with single-match xG. A team can lose a match despite a higher xG if they were clinical from fewer chances or if the opposition goalkeeper played exceptionally. Look at xG across five or more matches for more reliable signals.
Both Teams to Score (BTTS)
If both teams are generating xG above 1.0 consistently, both teams to score is a reasonable market to consider. Equally, if one side has a low xGa – conceding few quality chances – BTTS no becomes more attractive.
See our both teams to score tips for daily BTTS selections.
Total Goals (Over/Under)
Adding both teams’ xG figures gives an expected total goals figure for the match. If the combined xG of both teams across recent games averages 3.2, an Over 2.5 goals market at reasonable odds has statistical support. For under markets, look for two teams with low xGf and strong xGa records.
Correct Score
xG does not predict exact scores reliably, but it informs probability distributions. A team with an xG of 2.0 is more likely to score two goals than one with an xG of 0.8. This can guide correct score selections, though the variance in this market is always high.
See our correct score tips for daily selections.
Bet Builder
xG data on individual players is now widely available. A striker generating 0.5 xG per game across recent matches has a strong underlying case for an anytime goalscorer bet regardless of whether they are currently converting those chances. Players running below their expected conversion rate are often worth targeting to return to form.
See our bet builder tips for daily selections.
Where to Find xG Data
Several sources provide free xG data for the Premier League and European football:
Understat (understat.com) – detailed match and season xG data for the top European leagues, with individual player xG and shot maps.
FBref (fbref.com) – comprehensive statistical database including npxG, xGa and xPts at both team and player level.
The Athletic – xG visualisations and analysis are published after major matches.
Sky Sports and BBC Sport – both now show match xG in post-match statistics sections.
Most bookmakers, including Bet365, also integrate xG data into their statistics sections within the match pages.
xG Limitations – What It Does Not Tell You
xG is a powerful tool but it has well-documented limitations that are worth understanding before using it to inform bets.
It does not account for goalkeeper quality. Two shots with identical xG values will have different actual outcomes depending on whether the goalkeeper is exceptional or poor. Models are improving but goalkeeper adjustment is not yet standard.
Small samples are unreliable. A single match xG figure can be heavily influenced by one unusual chance or a fortunate sequence of shots. Five to ten matches is a more reliable sample for drawing conclusions.
It does not capture everything. Shots that hit the post, chances that were not taken due to offside, and the quality of build-up play are not fully reflected in raw xG. It measures shot quality, not complete attacking performance.
Teams can consistently outperform or underperform xG. Some teams with elite finishers – or elite goalkeepers – genuinely outperform their xG over extended periods. xG regression to the mean is a tendency, not a guarantee.
Test Your xG Betting With a Free Bet
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FAQs
What does xG mean in football?
xG stands for Expected Goals. It is a statistical measure of the quality of goal-scoring chances created in a football match, expressed as the number of goals a team would be expected to score from those chances based on historical data.
Who created xG?
Expected Goals models were developed by several data companies independently. Opta and StatsBomb are the most widely used sources of xG data in professional football. The concept has existed in various forms since the 1990s but became mainstream in football analysis from around 2012 onwards.
Is a high xG always good?
A high xGf (expected goals for) indicates a team is creating quality chances. However, if a team consistently scores fewer goals than their xG suggests, it may indicate poor finishing or that they are creating chances from positions that are harder to convert than the model suggests. High xGa (expected goals against) indicates a team is conceding quality chances and may be vulnerable defensively.
Can you bet on xG directly?
No major bookmaker currently offers a direct xG betting market. However, xG data informs the pricing of goals, match result and player performance markets and can be used as an analytical tool to identify value in those markets.
More Betting Guides
- Understanding Probability in Betting
- Both Teams to Score Tips
- Correct Score Tips
- Bet Builder Tips
- Football Accumulator Tips
- Cash Out Betting Explained
- Free Bets Hub
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Andy is the founder, owner and editor of thatsagoal.com, with over 20 years of experience in betting on sports. He has a keen eye for stats, particularly when looking at players to be carded, and these form a large part of the bet builder tips you see on the site. As well as creating daily football tips, Andy also keeps thatsagoal updated with all the best bookmaker promotions and offers for our readers.