Expected Goals (xG) is a metric used to qualify and sum up chances in football. It is becoming increasingly popular, making its way to TV analysts’ desks and being used more and more by Premier League clubs. In this article, we explain everything you need to know, including what xG is exactly, how to calculate it, and more!

‘Expected Goals’ (xG) is a measure – usually expressed as a number between 0 and 1 – on whether a given shot will result in a goal. By taking into account a range of factors and historical data, it allows us to identify how many goals a player or team should have scored based on the quality of chances they had during a game.

Advanced Metrics (the term used in relation to the analysis of sports to measure in-game productivity and efficiency) are already utilised within many sports around the world – most notably baseball, basketball and American football. Now, they are making their way into mainstream football in the form of Expected Goals.

For a visual explanation of expected goals you can watch this brilliant video by the awesome guys at Tifo Football:

An xG of 1 is the highest value a single shot can be, which implies that a player has a 100% chance of scoring. The higher the value of the xG, the more likely the player is to convert the opportunity.

The use of npxG (Non-Penalty Expected Goals) is particularly useful as it provides a more accurate analysis. Since penalties have an xG of 0.76, they can significantly distort both a player’s and team’s expected goals. Since the penalties may not have been earned or deserved, they can provide an inaccurate look to the data. Reading further detail on Expected Penalty Goals (xPG) will also give you a clearer idea of how you can work out the xPG for each penalty in a game.

Prior to expected goals, statistics such as ‘Total Shots' and specifically ‘Shots On Target' were used when analysing a match, and similarly to a final scoreline, they can be deceptive when considering that a shot with an xG of 0.13 classed as the same as a shot that has an xG of 0.83.

For example, let's imagine that Team A took a total of 17 shots during a game while Team B only took 8. From these stats, we would be under the impression that Team A deserved to win. However, if we looked at the expected goals data, we would see that Team A had an xG of 1.34 from the game while Team B had an xG of 2.18.

One popular criticism of the data is that current models do not take into consideration the talent levels of:

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    The player shooting

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    The goalkeeper in goal

This is something that will obviously influence the xG value when it is implemented and is therefore something to keep in mind.

How Is Expected Goals (xG) Calculated in Football?

Expected Goals (xG) Explained

There are a range of different models used to measure expected goals, ranging from the simple to the complex. For example, Opta, the world’s leading supplier of sports data, analysed over 300,000 shots to help create their model.

Opta's Duncan Alexander describes xG as “a measure of chance quality”. The variables that the model considers when calculating xG include:

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    Distance from goal – Generally, the closer you are, the higher the xG.

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    Angle of the shot – Overall, the more acute the angle, the lower the xG.

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    Shooting part – Was the shot from a strong foot, weak foot, or a header?

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    Shot type – Was it a volley, tap in, or an overhead kick?

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    Passage of play – Was it from open-play or from a set piece?

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    Chance creation – Did the opportunity come from a cross, a through ball, etc?

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    The shot – Was it from a rebound? did it come after beating an opponent etc?

Having said that, earlier this year, The Analyst wrote an article suggesting they have further evolved their xG model to improve accuracy. More factors are now being taken into account when calculating xG. These are:

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    The amount of pressure the shooter is under from opposition players

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    The positioning of the goalkeeper (provides context on the shot distance and angle)

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    More contextual features such as whether the shot was first-time, a rebound, etc

Further evolutions of the xG model are expected as technology continues to develop.

While these provide a good standard for expected goals analysis, some of the more complex models also take into consideration factors such as the defensive play of opponents. Defending is just as big of a factor in a game as attacking is, so by taking it into account, the data is likely to be more reliable.

xG figures are useful when working out the sustainability of short-term trends. For example, if a player or team typically scores more than their xG, some would argue that their current scoring rate isn’t sustainable. However, there are some world-class finishers, like Tottenham’s Son Heung-min, who typically buck xG trends and defy any sustainability cries.

According to FBRef.com, Son has scored more than his xG in the past five full Premier League seasons. The South Korean scored 23 times last season despite notching an xG of just 16.4.

Nevertheless, these expected figures are brilliant for assessing a team’s underlying performance, irrespective of the goals they actually score and concede.

The increasing availability of data means there are now several terms you should be familiar with in the ‘expected’ realm. These terms can be separated by whether they account for an entire team, or an individual player.

Team xG

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    Expected Goals For (xGf) – The number of goals a team is expected to have scored based on their quality of chances created.

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    Expected Goals Against (xGa) – The number of goals a team should have conceded based on quality of chances they surrender.

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    Expected Points (xPts) – The number of points a team is expected to have won in correlation with the expected goals data.

Player xG

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    Expected Goals (xG) – The number of goals a player would be expected to score based on the quality of chances presented to him.

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    Non-Penalty Expected Goals (npxG) – The number of goals a player would be expected to score based on the quality of chances presented to him in open play. Penalties – which are valued at 0.76 xG each – are not taken into account.

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    Expected Assists (xA) – The number of assists a player would be expected to register based on the quality of chances they create for teammates. The model measures the likelihood of whether a pass will turn into an assist. Like xG, several variables are considered when calculating xA. These are: – Type of pass (e.g., cross, non-cross, header, through ball, etc) – Pattern of play (e.g., corner, throw-in, open play, free-kick, etc) – Location of where the pass is received – Location of where the pass is made from – Distance of the pass

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Expected goals data is advantageous to sports bettors, as it provides information that a final score may not always reflect.

Football is generally a low-scoring sport and as such, goals come in a small commodity, meaning the final score of a game can be misleading.

For example, you may see a team dominate a game in possession, territory and chances created, yet somehow still manage to lose. The basic goal data (final score) will, therefore, be unrepresentative of the game, and thus can't be used to form an opinion on future fixtures.

Nevertheless, what can be used for future purposes is the xG goals data that has come from the game. Using this data, we can remove any perils about the likelihood of finishing at both ends of the pitch and get a more reliable interpretation of a team’s overall quality.

Short-Term Profit

With regards to upcoming matches, expected goals data can help us identify value. If a team has been over-performing or under-performing their xG metric, they are likely to soon return to their average.

For example, let's imagine Team A has picked up only 1 point from their last three games despite comfortably beating all three of their opponents on the expected goals data. Due to this poor run, Team A are priced at greater odds to win their next fixture than what the data suggests. This would represent value.

While xG data can and should only be used as a guideline, if it supports your research and you believe a price is deemed value, then it is likely to be a good bet.

Smarter Ante-Post Predictions

There is always money to be made in ante-post markets and by using a system rather than going on gut instinct, you are more likely to be successful.

While you can use the expected goals data to predict upcoming matches, it can also be used for forecasts, such as table standings and golden boot standings.

By using xG goals data both for and against from previous campaigns, we can create an alternative league table, which provides an informative display of how the season went and can help us predict future performance.

However, when using this data for future predictions, it is important to remember that these statistics do not take into consideration factors such as transfers, injuries, form and new managers.

There are plenty of websites where you can find xG stats for almost any league you need. We recommend that you take a look at the most relevant sites we have compiled for UK punters, with Fbref.com and Understat.com being two of the most popular.

Pos Team %
1 Inter Inter 29/38 76.32
2 Milan Milan 22/38 57.89
3 Atalanta Atalanta 21/38 55.26

Expected Goals xG Champions League

FBref has some of the best xG stats for the Champions League.

Expected Goals xG English Premier League

Understat has great xGstats for the EPL, including individual players.

Expected Goals xG EFL Championship

We also recommend FBref for the best xG stats for the EFL Championship.

Expected Goals xG League One

Check out FBref for the best xG stats for the EFL League One.

In essence, expected goals is a way of assigning a ‘quality' value to every goal-scoring opportunity, based on the information available. There has been a serious amount of growth in the modelling of xG, and as time goes on, the more data that is collected, and the more reliable and accurate the metric will become.

It is important to remember, however, that the analysis is not always 100% representative of a situation and there will therefore always be outliers.

Football fans, managers, and punters are still divided on the utility of the metric; however, expected goals is here to stay.

xG uses metrics such as Distance From Goal, Angle of the Shot, Shooting Part, Passage of Play, and Chance Creation to calculate how likely a goal will be scored from any position of situation. Statisticians use an Expected Goals formula to create a score between 0 and 1. For example, a shot with 70% chance of creating a goal gets 0.7.

It is a metric that shows how likely a goal is from a shot in any position and situation. 0 means 0% chance, while 1 means 100% chance. Thus, it is a score between 0 and 1. For example, a shot with 34% chance to get a goals has expected goals of 0.34. These expected goals can be added up to show how many chances, a team or player got, and how valuable they were.

xG is the expected goals for a shot – in other words, how likely it is for a goal to result from a shot in a particular situation and position. You often see it as the sum of the expected goals for a player or team. xA is the total number of assists a player should have produced based on expected goals taken directly from their passes.

WRITTEN BY Matteo Ebejer View all posts by Matteo Ebejer

Hi, I'm Matteo, a writer who's passionate about all things sports. The typical weekend for me revolves around being glued to all things football on TV, ruining my Fantasy Premier League team, and getting off my lazy butt for a run.

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