Expected Goals (xG) is a metric use♋d to qualify and sum up chances in football. Itꦫ 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!
What Is Expected Goals (xG)?
‘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 durin👍g 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, th⛄ey are making their way into mainstream football in the ඣform of Expected Goals.
For a visual explanation of expected goals you can watch this brilliant video by the awesome guys at Tifo Football:
Expected Goals in More Detail
An xG of 1 is the high𒉰est 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 th♎e penalties may not have been earned or deserved, they can provide an inaccurate look to the data. Reading further detail on 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 tal𒁏ent 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 miꦜnd.
How Is Expected Goals (xG) Calculated in Football?
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.
describes xG as “a measure of chance quality”. The variables that the model considers when calcu𝓡lating 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 f�༺�oot, 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 b𓆉all, etc?
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The shot – Was it from a rebound?𓆏 did it come after beating an opponent etc?
Having said that, earlier this year, wrote an article suggesꦯting 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 ꦅdistance and angle)
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More contextual features such as whether the shotꦕ was firs🔯t-time, a rebound, etc
Further evolღutജions 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 Pre🃏mier 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 concede𓆉.
Team xG Versus Player xG
The increasing availability of data means there are now several terms you should be familiar with in the ‘exp🌸ected’ 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 ꦑchances created.
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Expected Goals Against (xGa) – The number of goals a team should have conce𒊎ded based on quality of chances they surrender.
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Expected Points (xPts) – The number of points a team i🌸s expected to h﷽ave won in correlation with the expected goals data.
Player xG
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Expected Goals (xG) – The num🧸ber 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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Benefits of Expected Goals in Sports Betting
Expected goals data is advantageous to sports bettors, as it provides information that a final🍒 score may not always reflect.
F♋ootball 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 ch🍨ances 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 qꦦuality.
Short-Term Profit
With regards to upcoming matches, expected goals data can help us identify value. If a team ﷺhas been over-performing or under-performin🌺g 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 go✱als 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 us𝓡ed 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 syst𝓰em 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 𓂃an 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.
Where To Find xG Football Stats
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 pop⛎ular.
Expected Goals xG Champions League
FBref has some of the
Expected Goals xG English Premier League
Understat has including individual players.
Expected Goals xG EFL Championship
We also recommend FBref for the
Expected Goals xG League One
Check out FBref for the
ThePuntersPage Final Say
In essence, expected goals is a way of assigning a ‘quality' valuꦏe 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, tꦺhat 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.
Expected Goals FAQs
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 sco﷽re 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 hꦉave produced based on expected goals taken directly from their passes.