If you are a football fan of a certain generation — or player — the most important stat for you to judge a player on is goals. However, football is incredibly unique and very different than other sports with stop-start natures such as basketball or ice hockey. Football is a sport in which it is difficult to measure a player’s performance during a game. To better help football fans, clubs, coaches, scouts, the media, and just about anyone else in the game understand a player’s impact (especially an attacker) the expected goals (xG) stat was created.
Since its introduction to the game, it has been a derided and misunderstood statistic. Watch any football punditry show and it is likely xG won’t be mentioned. It is especially interesting when a player receives countless praise for his/her play only for the xG stat to show their production was lower than it should have been. Of course, many long-time football — and sports in general — pundits, players, etc, don’t want to put faith in statistics and mathematics.
What are expected goals (xG)?
Expected goals are “the measurement of how likely it is that a particular goalscoring chance will be taken”. xG takes data from an almost endless number of variables including the distance from goal of the striker, positioning of the goalkeeper, body part used to take the shot, and angle from goal. These are only some of the variables used in xG data. There are more variables taken into account to create the actual number of xG.
How are expected goals (xG) data tallied?
xG uses data from thousands of hours of video. The data is then calculated to give a number reflecting the “likelihood of an opportunity resulting in a goal”. The use of data, technology, and the use of a number to explain how a player performs rather than simply looking at their total goals number is revolutionary. A scientific approach leads many people to dismiss it without trying to understand it.
Using an xG example, Leicester City striker Jamie Vardy won the Golden Boot Award in 2019-20 scoring 23 goals. His xG figure was calculated at 18.9. Therefore, he exceeded his xG number by 4.10.
In contrast, Manchester City’s Gabriel Jesus, a player that gets an incredible amount of praise, tallied 14 Premier League goals which missed his xG figure by 7.02. According to the data, Jesus should have scored 21.02 goals.
The data measurement of expected goals will remain controversial. However, as more new generations of fans and pundits come along, it is likely xG will be used far more and taken a lot more seriously.