Goal Expectancy Statistics


In football, what is an Expected Goal xG and how is it calculated?

Coaches, pundits, and fans alike are using new vocabulary in football, but what exactly does it mean?

Football is sometimes described as a game of views, and although this is true to some extent, the sophistication of statistical analysis in sport has sharpened the image.

Expected goals have been a frequent element for mainstream broadcasters such as Sky Sports and BBC’s Match of the Day, following early use in the betting and pro markets. xG has risen from the laptops of analysts to the mouths of Premier League managers on a daily basis. Jurgen Klopp of Liverpool recently compared his team’s predicted goals output to that of Manchester City, while Dean Smith of Aston Villa used the metric frequently in interviews this season to assess his team’s underlying results.

Expected goals were one of the first sophisticated metrics to gain widespread acceptance among general football fans, and as a result, it has undoubtedly attracted detractors over time (see Jeff Stelling in 2017). A fight between the old way of looking at the game and the new world of data analytics. However, before we make a decision, we must first grasp how the metric works and how we should use it.

What is xG and what does xG stand for?

In football, the phrase xG is an abbreviation that stands for ‘anticipated goals’ or ‘expected goals’ in soccer. It’s a statistical evaluation of the quality of goal-scoring opportunities and their chances of being converted.

For both teams as a whole and individual individuals, an xG measurement can be derived, indicating how well they should be doing in front of the goal.

Clubs have begun using measurements like xG to analyze their players since statistical analysis has become an increasingly significant component of football and, indeed, sport in general in the twenty-first century.

It’s also become a popular topic of conversation in sports media, with fans taking an interest in the concept.

What is the formula for calculating xG? What is the xG expected goals calculator?

When determining xG, a variety of parameters are taken into account. The sort of assist, whether the shot was taken with the head or the foot, the angle and distance of the shot, and whether it was a good chance are all factors to consider.

The xG rating of a scoring opportunity is determined by its circumstances. The xG score of a rebound falling to a man in front of an open goal six yards away is high, whereas the xG score of a shot taken from 35 yards at a narrow-angle is low.

If a chance is characterized as having an xG rating of 0.35, it signifies that a player may anticipate scoring 35 percent of the time from the chance – a one in three probability. If a chance is specified as 0.5xG, that means it should be scored 50% of the time, and so on.

The xG ratings of a player or team over the course of a season can be used to estimate how many goals they should have scored. Not only can this be used to assess a specific performance, but it may also be utilized to forecast future or long-term results.

What is the definition of expected goals (xG)?

Expected goals (or xG) is a metric that determines the possibility of a goal being scored from a specific position on the field during a specific phase of play. This figure is based on a number of elements that existed before the photo was taken. xG is calculated on a scale of zero to one, with zero being an impossible-to-score chance and one representing a chance that a player should expect to score every time.

We all know that a shot from the halfway line isn’t as likely to result in a goal as one from the penalty box. We can now measure how probable a player is to score in each of these situations using xG. Assume the chance that a certain set of pre-shot characteristics from inside the box was worth 0.1 xG. In this situation, an average player would be predicted to score one goal out of every ten shots, or 10% of the time. Although the concept is new, expressions like “he scores that nine times out of ten” and “he should’ve scored a hat-trick” have been used by football fans and commentators for years before xG was established.

Typical Misconceptions

The most common complaints of expected goals (xG) come from situations when the measure isn’t being used correctly. At the game level, this is the most prevalent. The team with the higher xG in a game does not necessarily mean they should have won it. xG is simply concerned with the quality of chance, not with the game’s projected outcome. Goals definitely affect games, and the scoreline has an impact on how teams play, just as the old adage says. If a side takes an early lead, they don’t necessarily ‘need’ to create more chances, and we frequently expect the opposition to create more goal-scoring possibilities for the rest of the game as they try to make a comeback.

Another misunderstanding concerns the literal meaning of the metric designation. We don’t “expect” goals to happen exactly as the probability suggests. We also recognize that fractions of goals are impossible to score. The term “anticipated objectives” comes from the mathematical idea of “expected value,” which is a measure of the probability of a specific event occurring. A fair coin toss has a 50 percent chance of landing on heads and a 50 percent chance of landing on tails (the expected heads or the expected tails is 0.5). We don’t expect exactly half of our tosses to land on each outcome, but we do expect it to revert to this equilibrium over a larger number of tosses.

A person or team that has been exceeding their xG does not need to underperform in order to return to expectation. The Gambler’s Fallacy is a concept that explains this. While we expect them to score in accordance with their expectations with future shots, they have already ‘banked’ their overperformance, thus we will still expect them to outperform by this amount in the season aggregates. Similarly, if a coin toss lands on heads ten times in a row, subsequent coin tosses are equally likely to land on heads or tails, but the ten times the coin landed on heads have already occurred.

How Do Expected Goals Get Calculated?

We can tell which chances are more or less likely to be scored while watching a game. How close did the shooter come to scoring? Were they able to get a good shot? Was it a one-on-one situation? Is it possible that it was a header?

The problem is that we have to figure out this strategy for an average of 25 shots every game, all of which could come from different situations. The benefit of our expected goals model is that we can now quantify how each of the variables listed above – and others – influences the likelihood of a goal being scored.

This enables us to evaluate the quality of all 9,398 shots taken in the Premier League 2019-20 season in a couple of seconds.

The xG model from Stats Perform is based on a logistic regression model that uses hundreds of thousands of shots from our historical Opta data and combines a number of variables that influence the likelihood of a goal being scored, some of which are stated below:

  • The goal’s distance
  • The goal’s angle
  • ​One-on-one
  • There’s a good possibility
  • A portion of the body (e.g., header or foot)
  • Types of help (e.g., through ball, cross, pull-back etc) ​
  • Playing style (e.g., open play, fast break, direct free kick, corner kick, throw-in etc)

Because we recognize that some situations are extremely distinctive, we model them separately. Penalties are assigned a constant value based on their overall conversion rate (0.79 xG); direct free kicks are assigned their own model, and headed opportunities are evaluated differently for set-pieces and open play.

Stats Perform’s detailed event data has included shot pressure and shot clarity qualifiers on every shot since the start of the 2017-18 season, which directly quantifies the pressure and positioning of defenders and the goalie. These will be used to power a future model.

How Can We Make Expected Goals Work for Us?

Let’s look at two players from the 2019-20 seasons: Gabriel Jesus of Manchester City and Hakan Calhanoglu of AC Milan in Serie A. Last season, both players had exactly 100 shots (excluding penalties), but only 14 and 8 goals, respectively. What was the difference between their shoots, exactly?

xG gives context to each player’s shots by quantifying the quality of their 100 opportunities, which goes beyond typical stats like shots on target or average shot distance. We can now assess the quality of each player’s chances.

We would anticipate the typical player to score roughly 18 goals based on the opportunities that Gabriel Jesus had (17.7 xG). On the other side, we would anticipate the typical player to score only 7 goals based on Hakan Calhanoglu’s opportunities (7.0 xG). We can see why their goal-scoring production was so different right away. Despite the fact that Jesus outperformed Calhanoglu somewhat in terms of projected goals output, their 100 opportunities were of considerably different quality, and their output reflected this.

We may compare the two players’ shot profiles by examining their anticipated goals per shot (or xG per shot), which measures the average quality of a player’s scoring opportunities. Gabriel Jesus’ xG per shot was 0.18, implying that he should expect to score one goal out of every five shots he takes. Calhanoglu’s shots were speculative, resulting in a much lower xG per shot (0.07), as shown in his shot map above, where the growing size of the dot represents an increasing xG value (and hence a higher likelihood of scoring).

We’ve used an individual player as an example, but the expected goals measure may be applied in a similar way to teams or games.

Expected Objectives 

Since depth football is a low-scoring sport, our ability to predict the likelihood of a goal being scored is critical. We’re giving pundits and experts another tool to quantify the story that every football fan wants to hear with predicted goals. Which striker is having trouble finishing? Which team’s recent performance indicates that they should be higher in the league standings?

xG is a metric that goes beyond shot counts, but it’s crucial to keep in mind that it’s still just that: a metric. We can use it to assess underlying performance, but the actual goals are what will win you football games. Football is unpredictable, and goals can come from a variety of unexpected sources, but we can describe how unlikely these were with predicted goals.

Goal Expectancy Statistics FAQs

  1. What constitutes a good xG?

The higher the xG – which ranges from 0 to 1 because all probabilities are between 0 and 1 – the better the chance of scoring. In practice, this means that a chance with 0.2xG should be scored 20% of the time. It should be converted 99 percent of the time if it has 0.99xG, and so on.

  1. What is the formula for calculating xG?

It’s determined by comparing it to thousands of previous shots based on parameters including distance, defender position, pass type, speed, shot type, shooting angles, and other characteristics.

  1. What exactly are xG stats?

“Expected goals (or xG) calculates the likelihood of a goal being scored from a specific position on the pitch during a specific phase of play to determine the quality of a chance.” This figure is based on a number of elements that existed before the photo was taken.