Expected Goals (xG) is a statistical indicator mainly present in soccer. It quantifies the likelihood of a scoring opportunity. By evaluating the quality of chances based on factors such as shot location and angle, xG analyses player efficiency and team performance differently. 

The xG feature has been present in sports for almost five years. Now you will see almost all live scores pages using this indicator. The main focus of this article is to explore how xG is revolutionizing soccer and how it is used in other disciplines as well. 


The Basics of Expected Goals (xG)

By assigning a probability to each shot attempt, xG quantifies the chance that a shot will be successful. Calculating xG involves assessing numerous variables that affect the probability of scoring, such as:

This calculation is typically done through complex statistical models, including machine learning techniques. By understanding the quality of the chances created and conceded, teams can adjust their strategies to optimize scoring opportunities and defensive setups. That’s why some teams even have specific people working on this matter in soccer and hockey.


Expected Goals in Different Sports

When you hear about expected goals, you should generally expect two sports: soccer and hockey. We will discuss each of them separately below.

Expected Goals in Soccer

Expected goals have been a major statistical feature in soccer. This metric helps coaches and fans understand not just the quantity but also the quality of shots taken. If you were to coach a team this way, you would evaluate your club’s performance even better.

Several xG models have gained prominence in recent years. For instance, Opta’s xG model takes into account factors like shot location, assist type, and whether the shot was actually a header. 

Another example is StatsBomb, which employs high-level data. For instance, it could focus on the pressure applied by defenders and the goalkeeper’s position to set sports predictions. 

Some of the major leagues that are actively using this feature are the Saudi Pro League, Premier League and MLS. The latter has become one of the most watched competitions in North America. MLS expected goals can now be seen on almost every soccer platform.

These models use historical match data to calculate the probability of a shot resulting in a goal. By predicting expected goals in soccer, teams can optimize formations, create tactics, and better understand opponents. 

Expected Goals in Hockey

Expected Goals in hockey follow almost the same concept you see in soccer. With that said, you should expect teams and coaches to consider the type of shot (wrist shot, slap shot) as well as locations and angles.

In the NHL, xG metrics have notably impacted player evaluation and team strategy. Teams use expected goals to assess not only the offensive capabilities of players but also their defensive contributions.

At the same time, adopting NHL expected goals has become essential in making accurate decisions in several ways. First of all, when signing a particular player you could have a general understanding of his skills. Secondly, while having contract negotiations you can decide whether the deal you are offering is worth it. 

Furthermore, if you are placing bets on hockey, by having a look at the statistics, you can make more accurate NHL predictions. For example, if the Edmonton Oilers have more than 5 xG per game, it is highly likely they will score at least four goals against the opposing side

Another important factor is the development of a player. For instance, an athlete with a high xG but a low actual goal tally might be recognized for creating high-quality chances but has problems finishing those opportunities. Because of these expected goals metrics, NHL teams have found a useful tool for assessing players’ performance.

Calculating xG (Expected Goals) in Soccer

In soccer, the expected goals metric assigns a value between 0 and 1 to each scoring chance. 0 indicates no chance of scoring, and 1 means certainty. The value assigned to each shot is based on various factors such as shot location, angle to the goal, type of assist, and whether the shot was taken with a foot or head. 

This process features a large group of AI tools that use large datasets of previous shots to predict the likelihood of a shot’s success. Different expected goals models may have different variables. For instance, some models might consider the positioning of defenders and the goalkeeper, while others could include the game state (e.g., winning or losing) or whether the shot followed a set piece.

The accuracy of xG models is generally high, but there is residual variance. It does not mean that the higher the number, the more goals the team will score. Not all quality chances result in goals, and vice versa. Over time, however, xG is a reliable indicator of a team’s or player’s scoring potential.

Understanding Goals Saved Above Expected (GSAx)

Goals Saved Above Expected (GSAx) is another pivotal metric that is present in hockey. It measures a goalie’s performance relative to the expected outcomes of the shots they face.

To put it simply, GSAx calculates the difference between the actual number of goals a goalie allows and the expected number of goals based on the xG of the shots. This indicates the number of goals a goalie has saved above or below what would be expected of an average goalie.

Here is the picture of how the NHL goals saved above expected work: 

  1. First, analysts subtract the total xG of all shots faced by a goalie. For instance, consider a goalie who faces 100 shots, each with an xG of 0.03, summing up to an expected 3 goals against. 
  2. If the goalie saves all 100 shots, they have conceded zero goals against a prediction of three, resulting in a GSAx of +3. 
  3. Eventually, this means the goalie has saved three goals more than what was expected. This could indicate that he had an outstanding performance in a specific match.

Expected Goals

Understanding Goals Above Expected

Goals Above Expected (GAE) is another metric in soccer. It analyzes the total number of goals a team scores beyond what statistical models predict. This model is derived from the expected goals. Essentially, GAE helps assess how much more effective a team’s finishing is.

For example, suppose a team takes 1,000 shots with an average xG of 0.03 per shot. It would result in an expected total of 30 goals. If, theoretically, the team scores 60 goals, their GAE is 30, indicating they scored 30 more goals than expected. This positive GAE suggests exceptional finishing efficiency or perhaps extraordinary goalkeeping performance from opponents. 


Expected Goals and Goals Saved Above Expected have revolutionized soccer. xG takes into account like shots or goals to evaluate the quality of scoring opportunities. Similarly, GSAx measures a goalkeeper’s performance by comparing actual goals conceded to those expected.

The use of xG and GSAx in soccer gives spectators and fans a detailed understanding of the game. Exploring additional resources and tools that use xG and GSAx can be useful for people looking to find out more. 


What are the expected goals?

Expected goals indicator determines the goal chances that the team creates at the opponent’s goal. The scoring algorithm assigns a coefficient to each shot – from 0.01 to 1.00. The more dangerous the goal moment, the higher the level of the impact coefficient. 

What does Expected Goals (xG) mean?

xG is a model of expected goals. This model is based on an indicator, which helps to look further into the scores and estimate how many goals the team might have scored. Each shot of a team is assigned a value. It should be noted that shots on goal are not always used as a basic indicator.

What are the goals saved above expected?

GSAx is used to evaluate a goalie’s performance by comparing the actual goals conceded to the expected goals. This figure indicates how many goals a player has saved or conceded compared to what an average goalkeeper would have under similar circumstances.

What are expected goals in hockey?

Expected goals in hockey are similar to soccer in terms of the essence. Advanced algorithms calculate factors such as shot location, shot type, and whether the attempt was a rebound. xG in the NHL provides a value indicating how many goals a player or team should have scored on average.

How are expected goals calculated?

xG is calculated taking into account all potential scoring moments, which include shots on goal and dangerous moments without finishing shots. At the same time, it takes into account their distance to the goal, the angle, the quality of the shot and its characteristics.