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The “Expected Goals” Explainer

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The use of analytics has become increasingly popular in the world of sports and is slowly trickling into mainstream viewership across the globe. One such analytical tool which has roused debates among pundits, sent the internet in a frenzy and taken over the footballing world is Expected Goals.

If you’re an ardent fan of the beautiful game, you would have heard of the term Expected Goals by now too. It has been widely used by clubs for almost a decade but is now also used by the media and journalists and has become a staple of the football fan’s lexicon. But what is Expected Goals, how is it calculated, why are they used and why should you care? Well, today we’re going to answer all those questions in this simple Expected Goals explainer.

What is Expected Goals?

While watching a football game, how often have you heard a commentator say “he should be scoring that” or “9 times out of 10 that’s in the back of the net”? Well in a basic sense that is Expected Goals (xG). xG is simply a metric that measures shot quality and quantifies the probability of each chance leading to a goal based on several different factors. Expected Goals will tell you how many goals a player or team should have scored based on the number of chances they have in a game and how many goals a team should have conceded based on chances they give up in a game.

Why do we need Expected Goals?

Luck plays a huge role in football and the better side doesn’t always win. A team can have 25 shots and only score once while the opposition can have 3 shots and score twice to win the game 2-1. Unlike Basketball or Cricket, Football is a game with limited events so the final score-line does not necessarily tell you the entire picture. Since goals are rare events in a game, an expected goals model is created to give us a better idea of how the game has panned out.

A shot on goal is a more common occurrence during a game and xG quantifies how likely a shot will be scored which gives a better idea of how a team has played during the game and over the course of a season.

How is it calculated?

The various factors that influence Expected Goals are: the type of shot, how far the shooter is from the goal, the body part used to shoot, the angle used to take a shot, where the goal-keeper is, how many defenders are between the ball and the goal, did the shot come from a counter-attack or a period of possession. All these factors influence the value of xG and each shot is compared to 1000’s of shots with similar characteristics to give a specific xG value. If the value of a shot is 0.5 xG that means 5 out of 10 times that shot will lead to a goal. Shots that are taken nearer to the goal have a higher xG and similarly, shots taken from a narrow-angle to the goal have a lower xG.

A good example of xG is a penalty, 76% of penalties are scored so each penalty has an xG of 0.76. Some more examples can be viewed below.

This chance by Gareth Bale in Tottenham’s 4-0 win over Burnley was a high xG chance. Son played a defence-splitting pass which left Bale with the simple job of just guiding it past Nick Pope in goal. This chance had an xG of 0.54 which is quite high because Bale is so close to the goal and there are no defenders in his path. This means this identical situation will lead to a goal 54 times out of 100.

Bale’s second goal vs Burnley was a low xG chance. It had only an xG of only .07 which means a 7% chance of being a goal. This is because Bale is quite far from the goal, the angle is quite tight and he has a defender right in front of him.

Another interesting chance is this Richarlison opener in the Merseyside derby. The shot has an xG of 0.4. You might think this is quite low because he is through on goal but because of the narrow-angle and the onrushing Alisson, it is not as high-scoring a chance as you might think.

Why is xG important?

Expected Goals can be quite misleading in a small sample and is typically meant to be used over a longer period such as 10-15 games to give a clearer picture. 

Expected goals can help identify good finishers and strikers. If a player consistently has more goals than his xG, it means he is a good finisher because he is scoring more goals than average based on the number of chances he is getting. Lionel Messi has averaged 21 xG over the last four seasons but has scored 28.5 goals per season in that time. This means he is scoring 7.5 goals more on average per season based on the chances he is getting and hence is classified as a good finisher. Roberto Firmino, on the other hand, is a poor finisher because for the last 3 seasons he has averaged 12.4 xG per season but only 9 goals a season which means he can’t finish his chances.

xG can also help identify performing players in underperforming teams or players that do well in the limited game-time they receive. Tammy Abraham is a good example. He has been in and out of the Chelsea side but his numbers are incredible and warrants extra minutes. He is averaging 0.5 non-penalty xG per 90 minutes in the league, the third-most in the competition behind Edinson Cavani and Michail Antonio. Given his underlying numbers, it would be no surprise to see him do well elsewhere or put a start scoring goals for fun for Chelsea.

xG can also tell us how well a team is doing throughout the season and how good they are in terms of scoring and conceding goals, as it tracks the number of chances they consistently give up or score. We know Manchester City are a good defensive team and that is backed up by the numbers which show that they only allow 0.75 xG per game.

Football is going through a data revolution and Expected Goals is at the heart of this change. Even established corporations such as the BBC and Sky Sports use these sorts of analytics during their broadcast and it is now an integral part of the game. The interesting part is that this is just the tip of the iceberg when it comes to metrics that aid our understanding of football. Other models such as Expected Threat or Post-Shot Expected Goals are currently being developed and represent the future of how the game will be analyzed and understood.


[Header Image Credit: Freepik]