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Expected goals (XG) explained

In the vast world of football analysis, the so-called expected goals metric is just another piece of statistics that is becoming increasingly popular. Through its appearances on BBC’s Match of the day as well as getting talked about by numerous pundits and analysts. Although the metric isn’t used to predict what will happen, it gives us the possibility to understand the game more in-depth. You will learn all you need to know about “xG” in this current article.

What exactly is Expected goals xG?

Essentially, xG symbolizes the possibility of a single shot to be converted into a goal. The span of value which a single xG can reach is anywhere between 0 and 1, and practically scoring a 1 is impossible. This would mean that a shot is 100% guaranteed to score, which is realistic only in theory.

To calculate the end value of an xG, an extensive list of factors as well as historical data gets taken into consideration. This metric is useful to us because it can show us how many of a team’s chances should have resulted in a goal.

Similar advanced statistical metrics are already employed in other sports and have proven their contribution. It seems like the people of the USA are quite fond of the comprehensive statistics, as MLB, NFL, and NBA all have a type of advanced metrics. With soccer's increasing influence overseas, or perhaps it's the other way around, we now see the uprising of the expected goals in Europe's top-5 football leagues.

Further detailed explanation

Although it's nearly impossible to get an xG of 1 because that would mean a 100% chance of scoring, it's important to note the higher the xG is the better is the player's chances to put it past the keeper. It's also important to mention that penalty shots get treated differently. They get removed from the equation when calculating overall team xG because they are evaluated at 0.76 xG by default. If we take penalty shots into consideration, they would distort the end statistics which prevents the analysis from being as clear as possible.

Before the xG metric was implemented in European football, pundits and analysts used stats such as Total Shots or Shots on Target. Despite being decent sports wagering markets, that every bettor has taken advantage of, Total Shots and Shots on Target get nowhere near close to the Expected Goals stats. Knowing only the number of attempts at goal may be extremely misleading, because as the experts have pointed out. Here is the example they've used – Team A managed 13 shots during the game, while their opponents Team B had just 5. After simply glancing over these two numbers, we would believe that Team A was much more dominant, had better chances at goal, and deserved the win. However, this is where the xG metric kicks in. In this case, despite making 13 attempts, Team A had an xG of 1.21, while Team B had much more plausible chances with an xG of 2.30.

How is xG calculated?

It turns out that there are several providers of xG statistical info for the world, and each one of them has developed its own model to evaluate the expected goal metric. Although there are several base criteria that we will enlist below, the models may vary greatly in their final form. One of the most extensive models is that of Opta Sports a British company that is one of the biggest suppliers of sports data in the world. During the development phase of the base equations, over 300,000 shots were studied and the data employed. Here are some of the variables that get taken into consideration when calculating the expected goals:

  • Distance from goal – xG tends to lower when a player is far away from goal unless the player is known for shooting from a distance.
  • Angle of the shot – xG gets lower corresponding to the difficulty of the angle of the shot.
  • Shooting part – Whether the shot came from the player's strong foot, weak foot, header, or other.
  • Passage of play – This variable takes into account whether the opportunity came from a steady attack, a counter-attack, or a set-piece.
  • Chance creation – Did the opportunity originate from a direct pass, through ball, or a cross.
  • The shot – Was it from a rebound, a volley, or it came after beating an opponent?

These are amongst the most common factors that get encoded in such models. The more comprehensive equations might include variables such as the style of play, the mentality of the shooter, defensive play from the other team as well as the varying characteristics of the enemy goalkeeper.

How can xG be used in regards to sports betting?

The expected goals metric is an important piece of statistics for bettors, as it often gives a different view of the same game. Very rarely does the final score of the game give you the whole story behind that match. We will throw in a small example in here – one that you've most likely stumbled upon in the past and wondered how is it possible. Here is the scenario – Team A dominates the game, has more possession of the ball, has more attempts at goal, and may even have the man advantage, but Team B manages to come up with an upset. In this case, you can’t use the final score to claim that Team A was the better team that had a higher chance of scoring. However, you can use the xG to prove this point.

This is where xG can be useful in football betting. It can easily clear up any misunderstandings of past results when evaluating a squad’s current form. We recommend that you keep an eye on this metric and use it alongside any other statistical analysis that you usually use. This way you can reap the benefits of both worlds and increase your betting performance. XG statistics can also be useful if you are a fan of betting on goal scorers, as it provides you with a very in-depth view of players’ performance.