Modern football is filled with new terms, new ideas and a real development in tactical understanding by not just experts anymore, but by the average football fan. With coaches, players and fans agreeing that statistics have become a crucial part to gaining an advantage in 21st century sport in general, it’s easy to lose track of what new piece of information is being discussed and spoken about by pundits and fans.

As a result, we have begun to put together a series of blog posts, trying to provide an explanation for these football terms, some old and some new. By putting this together, hopefully it will allow for a greater understanding of these terms so that you can better understand the data/information or even just the game you are watching in greater detail.

The first post in this series is probably the second most controversial introduction to football in the last few years apart from VAR, and that is the statistical explanation of ‘xG’ or ‘expected goals’. This idea is used by many modern coaches in the game, and often used to highlight how attacking a team has been, and thus how efficient they are in taking there chances. It’s often misunderstood by those who simply think it translates to goals or how many a team should have scored, but actually in itself it’s more about interpreting the chances of scoring from the chances that a team/player have created.

This stat it’s probably one of the most derided by ‘old school’ fans who think the game is simple, and just about passion and putting in a shift, and to some extent this is completely correct, but the introduction of xG to the general fan is one that begins to explain more about how teams play, how attacking they are or perhaps how wasteful they have been.

xG has really taken off on social media with many aspiring tacticians and coaches using this to make their point, however it’s also developed much further and now you can find stats on xA (expected assists), xGOT (expected goals on target) and more. It’s something those who appreciate the statistical side of football love to delve into, however even in those circles it can be up for debate, with different models for calculating xG attributing different results to the same chances.

Don’t forget to head over to Twitter (@NextGoalWinBlog) if you want us to provide an explanation of any other term or football-related concept, and also if you have any additional detail to add that we may have missed.

Dictionary Definition

The full dictionary definition of ‘xG’ states that:

In association football, expected goals (xG) is a performance metric used to evaluate football team and player performance. It can be used to represent the probability of a scoring opportunity that may result in a goal.

Wikipedia

This explanation is actually quite straight forward to follow/understand, but let’s take a bit of a deeper dive into how this plays out, and how it is calculated…

Explanation

What is xG?

Expected goals, or xG, is a statistical measure of the likelihood that a given shot will result in a goal. The xG value of a shot is calculated based on a number of factors, including the location of the shot, the angle of the shot, the type of shot (header, volley, etc.), and the number and position of defenders between the shooter and the goal.

The xG value of a shot is expressed as a decimal between 0 and 1, with higher values indicating a higher likelihood of the shot resulting in a goal. For example, a shot from the penalty spot with no defenders in the way might have an xG value of 0.9, while a long-range shot from a tight angle with several defenders blocking the goal might have an xG value of 0.05.

How is xG calculated?

To calculate xG for a shot, analysts use a variety of data sources, including video footage, tracking data, and historical shot data. They typically train machine learning models to identify patterns in the data and predict the likelihood that a given shot will result in a goal.

Some of the factors that might be considered when calculating xG include:

• The distance between the shooter and the goal
• The angle of the shot
• The number and position of defenders between the shooter and the goal
• The type of shot (e.g., header, volley, etc.)
• The speed and trajectory of the ball
• The position and movement of the goalkeeper

By analysing these factors and comparing them to historical data, analysts can estimate the likelihood that a given shot will result in a goal.

Why is xG useful?

xG is useful for a number of reasons. For one, it allows analysts to evaluate players and teams based on the quality of their scoring opportunities, rather than just the number of goals they score. A team that creates a lot of high-quality scoring chances but fails to convert them might be playing better than a team that scores a lot of goals from low-quality chances.

xG can also be used to evaluate individual players and their performances. A striker who consistently gets into good scoring positions but fails to convert might still be a valuable player, while a striker who scores a lot of goals from low-quality chances might be overrated.

Finally, xG can be used to evaluate the effectiveness of a team’s tactics and strategies. If a team consistently creates high-quality scoring chances from certain areas of the pitch, for example, it might be a sign that their tactics are working well.

In conclusion, xG is a powerful tool for football analysts and fans. By assessing the quality of scoring opportunities, xG allows us to evaluate players and teams more accurately and gain deeper insights into the game.

Example

The example I have chosen to highlight is tough to help further the understanding of xG unless you sit and watch the full 90 minutes, however what this game does show is high accuracy of xG to the actual score line. In a recent Europa League match, Nantes lost 3-0 to Juventus, and the xG stats are pretty accurate to this score line.

Although the game appears quite balanced in the traditional statistics, with shared possession, Nantes having more shots, but the same on target, you’d be forgiven for not quite understanding the 3-0 win for Juventus just based on this data. Hover xG allows you to dig deeper and see clearly that although Nantes had 6 shots and 2 on target, their xG of just 0.59 suggests these weren’t major chances that should have resulted in goals, whereas for Juventus, they had an xG of 2.54 for their 3 goals which shows how they were getting into better positions to have their 4 shots.

xG is great to compare with the traditional stats we are all familiar with, as it really paints a picture of this game from looking at how sides in a game like this don’t need to dominate possession, or have more shots, or even have more shots on target, but instead need a higher quality of chance to score, and that’s exactly what happened for Juventus.