❓FAQ
This section will provide a deeper understanding of our xG values.
What are the numeric values associated with xG, and how reliable are they?
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.
The xG values are pretty accurate to the actual outcome, which was 3-1, as you can see. The wonder strike from Marcus Rashford, however, 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.
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.
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.
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 do we cover for xG values?
Please keep in mind that not every league is available yet for our Expected Values. This is due to the data not being available yet for leagues that aren't active. Leagues like the European Championship will be added later on.
How are xG values calculated?
xG values are calculated by combining shot data with all types of other data provided by Sportmonks. These data points, along with additional information, are used to create the different xG metrics offered, providing a comprehensive understanding of goal-scoring probabilities.
What is the difference between xG and xGOT?
We have a complete blog dedicated to explaining what the different Expected Values entail. 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 expected goals that were actually on target. This is mainly used to focus on shots that were actually testing the goalkeeper. This metric can be used to calculate the expected goals saved, which is an essential figure in finding out how a specific goalkeeper performs.
What is the difference between xG and xGOT, and why is xGOT lower than xG? xGOT is xG minus the xG from shots that were not on target. That is why xG is higher. It is the combination of xG of all shots that are and aren’t on target.
Are these values available on season level?
Unfortunately, these are not yet available as we don't have the data for the beginning of this season. Once the new season commences we'll be able to add these values on a season level.
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