Expected goals (xG) model: What is it, and why is it useful?

Perhaps less so than you might think. According to Sportec Solutions’ 'Expected goals' model, fans could be the ones who need to rein in expectations.

bundesliga.com has the lowdown on the statistical metric shaking up the way professionals and fans alike analyse the beautiful game…

Expected goals - what is it?

Expected goals (xG) is a predictive model used to assess every goal-scoring chance, and the likelihood of scoring.

A xG model computes for each chance the probability to score based on what we know about it (event-based variables). The higher the xG - with 1 being the maximum, as all probabilities range between 0 and 1  - the higher the probability of scoring.

In practice, that means if a chance has 0.2xG, it should be scored 20 per cent of the time. If it has 0.99xG, it should be converted 99 per cent of the time and so on.

Watch: The top 10 unexpected Bundesliga goals of 2018/19

A typical xG model takes into account the following event-based variables when assessing the quality of a chance:

• Distance to the goal
• Angle to the goal
• Did the player strike it with his feet or was it a header?
• In what passage of play did it happen? (e.g. open play, direct free-kick, corner kick, counter-attack)
• Has the player just beaten an opponent?

As an example, a close-range shot from a central position will have a higher xG value than a header from an acute angle, assuming all other factors remain the same.

The above is a model broadly adopted by xG providers worldwide, but match data and technology firm Sportec Solutions (STS) – a joint venture between the DFL group and Deltatre - is taking the predictive power of xG to another level.

Using their unique algorithm to combine event and tracking data, STS is able to add brand new variables to existing xG models to improve its predictive power. These new variables include, for example, goalkeeper positioning or the pressure on the player attempting the effort on goal.

Expected goals - why is it useful?

The idea behind xG is that it gives an indication of whether results are based on sustainable factors like a steady creation of chances, or whether it is down to aspects such as luck or world-class goalkeeping.

For example, if a player has a higher xG figure than actual goals scored, it will likely be a result of poor finishing or bad luck.

By the same token, if a player is scoring more than his xG, it could be because of individual brilliance.

Put simply, xG can be thought of as effectively evaluating the quality of 'chances'. Whereas the ubiquitous 'shots on goal' count does not differentiate between a long-range strike and a missed open goal from two yards out, xG does.

That is to say, just because Bayern Munich’s Robert Lewandowski topped the 2018/19 Bundesliga scoring charts on 22 goals does not necessarily mean he is the league’s best finisher.

Watch: All 22 of Robert Lewandowski's Bundesliga goals in 2018/19

As well as helping us to assess individual players, xG also enables us to evaluate teams - and to predict future performances.

For example, if a team is performing to a certain level at the start of a new season, a look at their expected goals could reveal whether that run is likely to continue.

What’s more, xG can be used to illustrate offensive and defensive trends (the source through which a team creates/concedes chances), and determine expected assists (xAS) - i.e. the likelihood of a player’s final pass resulting in a goal.

So next time you find yourself lambasting your favourite player for failing to beat the opposition goalkeeper from close range, just ask yourself: Was it really a golden opportunity?