ExpectedGame

What is xG in Football?
Expected Goals Explained

Learn how Expected Goals (xG) are calculated, what they mean, and how this revolutionary stat transformed football analytics

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Drag the player to position them on the pitch and adjust parameters to see how xG changes

This is a simplified demo. Real xG models are more complex and use many additional factors.

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Chapter 1

What is xG in Football?

Picture this: It's the 90th minute of a crucial match. Your team has taken 25 shots but hasn't scored. The opposition has had just one shot and scored. The commentator says, "That's football for you." But is it really just luck?

Expected Goals (xG) is a statistical metric that measures the quality of a shot based on several variables. In simple terms, xG tells you how likely a shot is to result in a goal, expressed as a number between 0 and 1. For example, a penalty has an xG of around 0.76, meaning it has a 76% chance of being scored.

Born in the early 2010s through the work of analytics pioneers like Ted Knutson and StatsBomb, xG emerged from a growing movement of statisticians who believed football deserved better than "that's just football." While shot location models existed earlier, modern xG as we know it today took shape around 2012-2014 when more sophisticated models began incorporating multiple variables beyond just shot location.

How is xG Calculated?

Expected Goals (xG) assigns a probability (from 0 to 1) to each shot, representing how likely it is to result in a goal based on historical data from thousands of similar situations.

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Distance from goal

The most influential factor

📐

Angle to goal

Wider angles reduce xG significantly

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Body part used

Foot, head, or other

🎯

Type of assist

Through ball, cross, cutback, etc.

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Pattern of play

Set piece, counter-attack, possession

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Defensive pressure

When available in tracking data

Penalty

0.76-0.79xG

6 yards out

0.4-0.6xG

30 yards out

0.01-0.03xG

See xG Explained

Video: Tifo Football's excellent explanation of what xG means in football

Further ReadingThe Athletic: Expected Goals (xG) – what it is and why it matters

Chapter 2

What Does xG Mean in Football? The Revolution

In 2012, a small team called Brentford FC made a bold decision. They disbanded their traditional scouting department and went all-in on data analytics. Their secret weapon? Expected Goals.

While bigger clubs were still relying on "the eye test," Brentford was identifying undervalued players who were creating or getting into high-xG positions but hadn't converted them into goals yet.

The result? A meteoric rise from League One to the Premier League, all while operating on a fraction of their competitors' budgets.

The Brentford Miracle

2009: Struggling in League Two

Fighting for survival in the fourth tier of English football. Budget: One of the smallest in the division

2012: The Data Revolution

Adopted data-driven approach with xG at its core. Key insight: Identifying undervalued players with good xG stats

2014: Championship Promotion

Reached the second tier of English football. Outperforming teams with 5x their budget

2021: Premier League Glory

Promoted to the Premier League. First time in top flight since 1947

2017: The Tipping Point

When the BBC's Match of the Day – the world's longest-running football TV program – introduced xG graphics in 2017, it marked xG's arrival in the mainstream. What was once a stat for nerds had become essential viewing for millions.

Before 2017

Limited to Analytics Blogs

Niche stat discussed in forums
Used mainly by data analysts
Complex visualizations

After 2017

Mainstream Adoption

Featured on prime-time TV
Simplified for mass audience
Part of regular match analysis

But the real power of xG isn't just in measuring past performance – it's in predicting future outcomes. Research has shown that a team's xG difference is more predictive of future results than their actual goal difference or points tally.

xG in Action: Brentford's Rise

Video: How Brentford used data analytics to climb the football pyramid

Chapter 3

How Does xG Work? Real-World Examples

Let's move beyond theory and see how xG works in practice by examining some real-world examples from football matches.

Real-World xG Examples

2024/25 Premier League

Newcastle vs Crystal Palace

Newcastle

Goals5

Expected Goals (xG)1.4

Crystal Palace

Goals0

Expected Goals (xG)1.9

What This Tells Us

A fascinating match where Newcastle's clinical finishing (5 goals from 1.41 xG) defied the expected goals model, while Crystal Palace's 1.87 xG went unrewarded. This Premier League game perfectly illustrates how xG can reveal the true story behind a scoreline, showing that Palace created better chances despite losing heavily.

See the full match report including xG shot maps, and xG timelines.

These examples show how xG adds depth to our understanding of football. What seems like magic or destiny often has a statistical explanation. But that doesn't make these moments any less special – it just helps us appreciate the true nature of what we're witnessing.

Want to explore more real-world xG stories? You can see live xG stats and post-match reports for matches across all major leagues, helping you understand the true story behind every result.

Chapter 4

xG in Football Stats: Myths Debunked

As xG has grown in popularity in football statistics, so have the misconceptions about what it can and can't tell us. Let's separate fact from fiction.

Did You Know?

Teams that consistently outperform their xG often regress to the mean over time, making xG a valuable predictor of future performance.

Myth: "xG is trying to replace goals"

Reality: xG doesn't aim to replace goals – it provides context for them. Goals will always be what wins matches. xG simply helps us understand whether those goals (or lack thereof) reflect sustainable performance or temporary variance.

Myth: "xG is perfect and objective"

Reality: xG models are constantly evolving and have limitations. Different models can produce different values for the same shot. They struggle with certain scenarios like measuring the value of off-ball runs or accounting for specific player skills. xG is a tool, not the oracle of football truth.

Myth: "xG is only for stats nerds, not real fans"

Reality: xG enhances the fan experience by providing deeper insights. It helps explain why your team might be struggling despite "playing well" or why a winning streak might not be sustainable. It adds a layer of understanding without taking away the emotion and drama that make football special.

The Future of xG in Football Stats

xG is just the beginning. New metrics are emerging that measure the value of every action on the pitch, not just shots. Passing models, defensive contributions, and off-ball movement are all being quantified. The next revolution in football analytics is already underway.

Ready to See xG in Action?

Explore live xG data, interactive visualizations, and in-depth match analysis that will transform how you watch and understand football.

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Frequently Asked Questions About xG

How is xG calculated in football?

xG is calculated using statistical models that analyze thousands of historical shots. Each shot is assigned a probability (between 0 and 1) based on factors like distance from goal, angle to goal, body part used, type of pass received, defensive pressure, and game situation. These models use machine learning algorithms to weigh each factor appropriately based on historical data.

What does xG mean in football?

xG (Expected Goals) in football means the probability of a shot resulting in a goal, expressed as a number between 0 and 1. For example, an xG of 0.3 means that shot has a 30% chance of being scored based on historical data from similar situations. It helps measure the quality of chances created rather than just counting shots.

What is xG in football stats?

In football statistics, xG (Expected Goals) is a metric that quantifies the quality of scoring chances. Unlike traditional stats like shots or shots on target, xG considers the difficulty of each chance. Teams and players can be evaluated on the quality of chances they create (xG) versus the goals they actually score, providing insight into finishing ability and luck.

How does xG work in football analysis?

xG works in football analysis by providing context to goal-scoring. Analysts use xG to determine if a team's results are sustainable or influenced by luck, to evaluate player performance beyond goals scored, and to assess team strategies. For example, if a team consistently generates high xG but scores few goals, they may be experiencing bad luck or poor finishing that could improve over time.

What is an xG calculator?

An xG calculator is a tool that estimates the probability of a shot resulting in a goal based on various factors. Our interactive xG calculator above allows you to adjust parameters like shot position, angle, body part used, and defensive pressure to see how they affect the xG value. Professional xG calculators use complex algorithms and vast datasets to provide accurate probabilities for real match situations.

What is xG in Football?Expected Goals Explained