XG explained

Welcome to our xG-explained blog. Here, we will explain the meaning of all the xG-metrics Sportmonks offers. In our previous blog we explained what xG is, in this blog we will dive into the deeper meaning of all new metrics xG brings to the table.

Time to read< 1 min
XG explained

What xG-metrics does Sportmonks offer?

Sportmonks offers the following xG metrics:

Meaning of the various xG-metrics

xG is the first metric. We have a complete blog dedicated to explaining what xG is. We will explain it here briefly as a reminder.

Expected goals (xG)
In football, xG is a statistical metric quantifying the quality of goal-scoring chances created or conceded during a match. It measures the probability that a particular goal-scoring opportunity will result in a goal based on various factors such as the location of the shot, the angle, the distance from the goal, the type of pass that led to the chance, and other situational variables.

xG On Target (xGOT)
xG On Target measures the probability of an on-target shot achieving a goal. Shots ending up in the corners are more likely to result in a goal than shots through the middle of the goal (of course, depending on the location of the goalkeeper, if you shoot in the left corner, while the keeper is there, you might be less successful than when you decide to shoot it in an empty right corner or the middle). The value zero represents a shot on target that is expected to be never scored, while an xGOT value of 1 is expected to be scored 100% of the times. Keep in mind that xGOT is a post-shot model, while xG is a pre-shot model.

What is the difference between xG and xGOT?

Rashford’s shot resulted in a 0.03 xG. However, the post-shot model (xGOT) is higher due to its great speed and position in the corner. The xGOT for Rashford’s shot is 0.23. In this case the SP would be 0,21 (xGOT minus xG).

 

Now, let’s take a look at Haaland’s shot for an (almost) empty goal. As you can expect, this has an xG value above the big chance range (an xG of 0.38 or above). The xG value is as high as 0.75. However, the xGOT is non-existent, as Haaland couldn’t shoot the ball on target.

Non-Penalty xG (npxG)
Non-penalty xG calculates the xG while penalties are excluded. It offers a clearer picture of a team or player’s expected goals from open-play situations. This helps evaluate the team’s ability to create scoring chances in open-play situations. Remember that other set-play situations like corners and free kicks are still included.

xG Open Play (xGOP)
xG Open Play calculates the xG while excluding all xG created through set pieces. It helps to evaluate a team’s ability to create scoring chances during the run of play, highlighting their attacking prowess in open-field situations.

xG Set Play (xGSP)
xG Set Play measures the expected goals from set-piece situations like corners, free kicks, and penalties. This is in contradiction to xG Open Play. The xG Set Play metric assesses a team’s effectiveness in capitalising on opportunities created from dead-ball situations, which can be crucial in breaking down organised defences. The following xG metrics are all part of xG Set Play.

xG Corners
xG Corners focuses specifically on the expected goals from corner kicks. It evaluates the quality of delivery and finishing from corner situations, providing insights into a team’s set-piece strategy and proficiency in converting corner opportunities into goals.

xG Free kick
xG Free kick calculates the expected goals from direct free kicks. It assesses a player or team’s ability to convert scoring chances from set-piece situations outside the penalty area, considering factors such as distance and angle.

xG Penalty
xG Penalty measures the expected goals from penalty kicks. It evaluates the likelihood of a penalty being converted into a goal. Each penalty is worth .79 xG.

Shooting performance (SP)
Shooting Performance measures how well a team or player shoots. It compares the xGOT to the expected goals (xG). When a team/player’s xGOT surpasses its xG, it signifies its proficiency in taking higher-quality shots relative to the quality of the team’s or player’s chances. Take a look at the Rashford example above. The xGOT (0,23) surpasses the xG (0,02), so the shooting performance is quite positive.

Expected Goals Prevented (xGP)
Expected Goals Prevented is used to assess a goalkeeper’s performance in preventing goals based on the quality of shots faced. It measures how many goals a goalkeeper prevents compared to what would be expected based on the quality of chances conceded.

Expected Points (xPTS)
Expected Points estimates the number of points a team is expected to earn from a match based on their xG for and against. It provides a quantitative assessment of a team’s performance in a match, helping to gauge whether they deserved more or fewer points based on their underlying performance during the season.

Expected Points Table
The Expected Points Table ranks teams based on the expected points they would accumulate from their xG performances over a season. It offers an alternative perspective on league standings, accounting for the quality of chances created and conceded by each team.

xG Against (xGA)
xG Against calculates the expected number of goals a team is expected to concede based on the quality of scoring opportunities their opponents create. xGA provides valuable insights into a team’s defensive performance. By comparing xGA to actual goals conceded, teams can evaluate the effectiveness of their defensive strategies and goalkeeper performance.

Expected goal difference (xGD)
Expected Goal Difference calculates the difference between a team’s xG for and conceded. It offers insights into the balance between a team’s attacking and defensive performances, indicating whether they are overperforming or underperforming relative to their xG metrics.

FAQ

How are xG values calculated?
xG values are calculated using historical shot data. The calculations are made based on the shot's location, angle, and distance. Next, the type of shot (for example, header or foot) is considered. The position of the players is also used in the calculations. This data and additional information are used to create the different xG metrics, providing a comprehensive understanding of goal-scoring probabilities. Expected goals is usually expressed as a value between 0 and 1, where 0 indicates a very low probability of scoring and 1 indicates a very high probability. For example, a clear one-on-one chance with the goalkeeper might have a high xG value close to 1, while a long-range speculative shot might have a low xG value closer to 0. An xG value above 0.38 for a specific shot is considered a big chance. For example, a penalty has an average xG of 0.79 to be scored. Keep in mind that the quality of a player is not accounted for. So, for expected goals, it does not matter if Lionel Messi is the one taking a shot or if the data scientist of Sportmonks takes the shot.
How can I get access to xG Data?
You can simply get access by going to MySportmonks and adding the Expected Goal add-on to your subscription. Not yet registered to MySportmonks? Create your account and start with Expected Goals by creating your New Subscription.
How does speed impact the availability of xG values?
The availability of xG values is influenced by the speed of data processing. Since xG calculations require match statistics like shots, there may be a delay before xG values become available. Patience is key as relevant match data is processed to generate accurate xG insights.
How does the reliability of xG values depend on the context of the match?
The reliability of xG values is contingent on various factors, including the context of the match. A nuanced understanding of the game's flow, tempo, and dynamics enhances the interpretation of xG values, providing valuable insights into the scoring probabilities.
What are the numeric values associated with xG?
xG statistics generate numeric values that show the chance of scoring based on the quality of chances created or conceded. These values for one specific player usually range between 0 and 1.5 for a specific fixture. For example, Erling Haaland had an xG value of 1.1634 in the Manchester Derby (18842545). In that fixture, he scored 1 goal in total. Pretty accurate, right? For that same fixture, the xG for both teams was as follows: Manchester City: 3.6439 - Manchester United: 0.3841. As you can see, the xG values are pretty accurate to the actual outcome, which was 3-1. However, Marcus Rashford's wonder strike shows that xG doesn't always tell the full story. He had an xG of 0.3553 for that match but did manage to score 1 goal. Remember: This will not always be the case. You may expect Erlin Haaland to finish a 0,89 chance or to score at least once or twice if he has a total of 3,10 xG. However, that doesn't mean the xG metrics are wrong if he doesn't. xG metrics are just parameters to calculate the quality of a chance or multiple chances. Even if a chance has a 99% chance of resulting in a goal, there is a 1% chance it will not.
What can I use xG Corner/Free Kick/Penalty for?
Of course, we offer xG Open Play and xG Set Play. But if you want to dive deeper into xG Set Play, the metrics xG Corner, xG Free Kick, and xG Penalty will come in handy. These statistics have particular use cases. For example, in the 2023/2024 season, Arsenal scored many goals from set pieces. For some people, it might be very interesting to see if the XG Arsenal generates is higher than the XG for other title contenders (like Manchester City and Liverpool). At the moment of writing, they are only 1 point apart and are numbers one to three in the league standings. As you can imagine, the over- and underperformance of these teams from set pieces might have a small or big influence on the rest of the season. It might be interesting to show which of the set pieces generates the most xG for certain teams and to find out if there are any differences. For one team, free kicks might be way more dangerous (maybe because they have a free-kick specialist), while others have a way higher xG from corners (maybe because they have two giants as centre-backs who join the attack when they have a corner). As you can see, these use cases can be really interesting to get a better understanding of the tactics of specific teams and to find out if a late free kick or corner is actually as interesting as it seems when the whole stadium is cheering to support their team with this late chance to score.
What is the pricing for the xG metrics add-on?
At Sportmonks, we use three types of packages: a basic package, a Standard package, and an Advanced package. In the Basic package, xG per fixture will be available 12 hours post-match. In the Standard package, xG per fixture and xG on Target per Fixture will be available straight after a match. The xG advanced package will have all our xG metrics, including xG per Fixture, xG On Target, Non-Penalty xG, xG Open Play, xG Set Play, xG Corners, xG Free kick, xG Penalty, Expected Points, Expected Points Table, Expected Goals Conceded, Expected Goal difference, Over/Underperformance players, Over/Underperformance team, Expected Goals Saved. The Advanced package has all metrics live.
What is the update frequency for xG values?
xG values are continuously calculated and processed throughout the match, with updates occurring every couple of minutes. The maximum time between these updates should not exceed 5 minutes. Stay informed with real-time insights into evolving goal-scoring probabilities.
What leagues have xG available?
We have a list available in our documentation. Please keep in mind this list is not yet complete, as not all leagues that will have xG available is active. Once leagues like the European Championship have played xG will also be available here.
Can I use xG with a custom plan?
Absolutely! If you have a personalized plan and wish to leverage the xG feature, simply reach out to our support team. We'll swiftly arrange the setup of a tailored xG add-on for you. Feel free to email us directly at [email protected] or get in touch with support via our contact page. We're here to assist you every step of the way!

Written by Jordy Post

Jordy Post is a seasoned football data and marketing expert with over 3 years of industry experience. With an in-depth understanding of Football Data, he stands out as a leading authority in delivering comprehensive insights. Jordy specializes in uncovering new stats, tracking market trends, and identifying emerging patterns, consistently providing innovative analyses that offer invaluable insights to Sports Data lovers.

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