Expected Goals (xG) has become increasingly prevalent among statisticians, and its growing use in the media has made it part of the wider football discourse.
Like most things in the sport, xG divides opinion. While its backers see it as a fairer assessment of a team’s attacking play, critics view it as another example of the game’s descent into data.
Read on to discover what xG is, how it works and whether it is still relevant today.
In this article we’ll cover:
What is xG?
Early football statisticians used variations of xG in the 1960s, but Vic Barnett articulated the concept in 1993, followed by later research by Richard Pollard, Samuel Taylor and Jake Ensum.
Opta officially started using the metric in 2012, but it became more prominent after broadcasters widely adopted it in 2017, with Match of the Day first using it that year.
Put simply, xG measures the likelihood that a player will score from a given chance, which in turn estimates how many goals a team should score.
The factors considered include distance from goal, angle, shot type, pattern of play and the previous action.
An opportunity with an xG of 0.5 means the player is judged to have a 50 per cent chance of scoring. These values are tallied across matches and seasons to assess a team’s overall performance, giving a deeper look at the data beyond points earned.
Is it still relevant today?
Expected goals can be extremely useful for assessing the overall performance of a team and its players.
Analysts and pundits use it to interpret the quality of chances a team has created in a game and over a longer period, offering a more in-depth look at attacking play than simply how many shots a side has taken.
Managers can be criticised for using it to defend their side’s performances when results are poor, though a team that loses 1-0 but created 2.5 xG has arguably performed better than their opponents.
The growing use of this data has led supporters to liken it to goal difference, since teams with the best xG tend to finish towards the top of the table.
The metric is also used in player recruitment, as it can identify underlying trends. A player who consistently outperforms their xG becomes an obvious target in the transfer market, while clubs with a low xG may look for more creative players.
Similarly, if a defence concedes a low xG, it suggests their defensive approach is working, so a recruitment team may look at other positions when trying to strengthen the squad.
Those who deride it often end up using its own logic to argue it is needless. For instance, does a high xG tell us anything more than that a team created the better chances? Conversely, does a team with a high xG but few goals need a new forward, or are they just unlucky?
xG is not perfect, and several companies produce this data using different criteria, so numbers can vary significantly across platforms.
For instance, some providers consider the position of defenders while others don’t, and some may not account for an attacker’s ability to create space or the likelihood that they will pass the ball.
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