xG Stats for Football Teams

Expected Goals data for Football Teams and Leagues

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xG Video : How To Use For Predictions

xG Football Stats: What is xG

Where Did xG Come From?

xG, or Expected Goals, is a methodology formulated by Alan Ryder, who wanted to measure the quality of a shot at goal, but not in Football. No, xG started when Alan stated:

Not all shots on goal are created equal.

He was right, and his research has since inspired other sports and data geeks and gurus to focus on their own models for creating xG (Expected Goals). If xG is the measure of quality for the shot taken by a team, then what factors go into it?

  • Assist Type
  • Distance from Goal
  • Type of Attack
  • Shot Angle
  • Body Part Used to Shoot

You can see why having Kevin de Bruyne or Trent Alexander-Arnold on your side would increase a teams xG. They create better chances for other players to score. The passes are so good that the chance of the forward scoring is higher. Sergio Aguero is a lucky man.

In the past few years, xG has become a key indicator of how dangerous a side can be, or in some cases how little chances they create. It's a very interesting statistic to study and one that really can help when it comes to betting on football. Some teams might have a high xG per game, but a low number of actual or average goals scored. This kind of trend suggests that the side needs to invest in a better forward, as the chances are clearly there.

Football Isn't Played in Spreadsheets

With the recent surge of use of expected goals in punditry and mainstream football, xG has become almost ubiquitous in daily football talk. However it's good to remember that football is not played in spreadsheets or via mathematical models. There is always limitations to expected goals, and we must respect the reality of the performances that occured.
For example, in the 2021/2022 UCL Campaign, the final had Real Madrid accumulate 0.73 xG and Liveprool had 2.9 xG. This is roughly the same value across many models published online (they generally only fluctuate about 0.2~0.3 xG between models). This means that Liverpool was expected to have scored almost 3 goals for the shots that they've taken.

However the fixture ended 1-0 in Madrid's favour.
Why? Because xG is not above player performances in reality. This is a combination of Thibaut Courtois' incredible goalkeeping performances, and Madrid's ability to create quality chances. In fact, Madrid had multiple open goal chances that didn't actually end up in a shot. Casemiro's failed pass across goal to Vinicius, Ceballos' open run towards goal, and Benzema's disallowed offside goal saw Madrid accumulate incredibly dangerous chances but no shots. This means that while Real Madrid was technically very dangerous, none of that was reflected in xG!
Obviously scenarios like this reflects the limitations of expected goals. There are several factors that xG could not take into account in these scenarios :

  • 1. The quality or the form of the goalkeeper faced
  • 2. Dangerous situations without a shot taken
  • 3. Situations surrounding the shots taken (number of defenders blocking or pressuring the shot taker)
And many other factors. In reality, we should not take xG at face value and understand football stats on a more granular level by studying other data as well.

xG Stats For Leagues

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