What Is xG and Why It Matters So Much in Football

I believe you’ve heard of xG as a football term, but what exactly is it and why do we use it? xG stands for expected goals, and it is used to tell us the probability that a shot will become a goal. Here is how it works.

For every shot a player takes, a clever algorithm calculates, out of 10 shots from the same position, how many would be expected to result in a goal. Let us say this one has a scoring chance of 80%. So eight out of 10 times, the ball would go in the net.

An easy chance. A shot from far away might have an xG of 0.1, so only one in 10 is likely to result in a goal. Nobody was expecting him to score from there. So each shot is given a score between zero and one. When we add up all the shots a team takes in a match, we get their total xG.

So if Manchester City had 19 shots that added up to a total 2.38 xG, it means they created chances that would normally result in about two or three goals.

This is not super helpful in itself for a single game, but over the course of a season or multiple seasons, the data it reveals can have huge consequences for managers, tactics and transfers. So let us find out how it has become the fundamental metric to how teams train, play and recruit new talent, both on and off the pitch.

How xG Changed Player Signings

In 2012, an Opta analyst named Sam Green posted a blog that looked into the efficiency of Premier League forwards, grabbing the attention of one Arsène Wenger. Fast forward two years and Arsenal had acquired an analytics company, making quick use of their lavish new spreadsheets to sign forward Alexis Sánchez from Barcelona.

Data analysis would now be used for everything from player recruitment to optimising tactics and has since given rise to the likes of Brighton, Bournemouth and Brentford, who many considered to be the Premier League’s original moneyball club.

It is another fantastic afternoon for Brentford. Their owner, Matthew Benham, is a former statistician who armed his team with algorithms built around identifying the best of the best away from the elite tier of world football, scouring for players with high xG numbers in the hope of picking up an underperforming and undervalued gem.

It was a calculated risk, but the Bees backed their blueprint, signing a series of players that the expected goals data proved were overperforming and snapping them up at below market rates. They then proved their quality in the Premier League and were sold on for healthy profits to the big hitters, allowing Brentford to reinvest in the club and develop their next stars. Take Yoane Wissa, who signed for Newcastle from Brentford, or Antoine Semenyo heading to Manchester City from Bournemouth.

The success of this model has led to huge changes off the pitch, with teams of data analysts now tasked with identifying the perfect talent to suit their manager’s style of play, rewarding organisations with an established and consistent philosophy.

xGA, the Low Block and the Myths About xG

But xG is not all about scoring goals. It can help to stop them too. xGA stands for expected goals against and is a metric to judge the performance of defenders and goalkeepers, with five key factors measured between zero and one.

xGA influences how teams set up when out of possession, with the aim to engineer the opposition into producing low-quality shots like long-range efforts or forcing attackers wide to shoot from tight angles.

If they know an opposition player has a low xG when playing in a certain area of the pitch or using their non-dominant foot, managers can deliberately set up to exploit that weakness.

This explosion of this data has given rise to the low block tactic, which sees defenders camped around their own penalty area with midfielders and forwards sitting slightly more advanced, minimising space for the opposition and forcing the attackers to shoot from distance or cross into a crowded box, both of which carry a low probability of scoring.

This has in turn led to banks of five attacking players when elite teams are in possession, with players like Marc Cucurella immediately leaving behind their traditional defensive line to become an extra player in attack when their team is in possession. Cucurella with a chance to cross, deflected.

But this is all obvious, is it not? Of course players are more likely to score if they shoot from a good area using their stronger foot. Some football fans think that models like xG are making football more and more complicated, but the benefits are in the detail.

One of the biggest misconceptions that a lot of people are having is that if a team has higher xG, they should have won the match. But no, that is not the case, xG measures the quality of chances, not the outcome or the final results. So you can still create better chances than your opponent and still lose the game.

A lot of people make mistakes when talking about xG. It does not mean a player is guaranteed to score that number of goals. It is a probability model (likelihood of something happening).

This is why xG is not very useful when looking at a single game. But over the course of multiple seasons, it is possible to identify which players have what it takes to make it at the very top.This is the reason so much investment has gone into xG.