As football continues to develop with time, more and more tools are becoming available to analyse the game.

One analytical metric that has become increasingly prominent in recent years is Expected Goals (xG).

This is a metric that is essentially used to evaluate team and player performance more effectively than before.

So how does it work?

Expected Goals (xG)

To keep things simple, xG works by assigning a value to a shot that is taken based on the likelihood of it being scored. For example, if a player decides to shoot from 30 yards, that shot will likely have an xG value of less than 0.1, because the large majority of shots won't go in from such distance.

If however, a player is presented with a tap-in from one yard out, that shot may have a value of about 0.9. Gradually, as a match progresses, each team will accumulate an xG based on the chances they have and by the end of the contest, each team's value should represent the scoreline or tell the story of how they match went.

Some xG models vary in the values that are assigned to certain shots, meaning that some numbers amongst different sources may be different.

To provide an example, if a player shoots from five yards, he'll likely have a high value assigned to that shot. However, some models are sophisticated enough to consider whether defenders are in front of that shot, and if they are, the value assigned will be lowered.

Overall though, the picture painted by xG is generally accurate, because the team that creates the best/most chances usually wins.

xG values can also be assigned to players to assess how actively they're presented with goalscoring chances per 90 minutes.

For example, after half a season at Juventus, Cristiano Ronaldo is averaging 0.93 xG per 90, which is quite amazing. This essentially suggests that Ronaldo is pretty guaranteed to score at least once in most matches that he plays based on his showings in Turin. If that is then compared to Roberto Firmino, who's a very unselfish supportive player, his xG per 90 is 0.34.

So generally, Ronaldo's is almost three times more likely than Firmino to score a goal per 90 minutes.

Roberto Firmino of Liverpool
Roberto Firmino of Liverpool

Expected Assists (xA)

There's also a means of judging a player's creative output, and that's called Expected Assists (xA).

So, before examples are provided, here's Opta's explanation: "Expected assists measures the likelihood that a pass will be a primary assist.

The model is based on the finishing location of the pass, what type of pass it was and a variety of other factors. This model is not reliant on whether a shot was taken from this pass, so credits all passes, regardless of whether they result in a shot."

So, to use Ronaldo and Firmino as the examples once more, Ronaldo's xA per 90 minutes is 0.17 whereas Firmino's is 0.19.

There's not much difference there, but Firmino creates more for his teammates on average than Ronaldo does. A better creative example to use is Lionel Messi, who has an xA this season of 0.58, which is very high and means there's basically a 50/50 chance that he'll provide an actual assist per 90 minutes.

Lionel Messi of Barcelona
Lionel Messi of Barcelona

Key passes is another means of judging creativity accurately, more so than simple looking at a player's assists. A key pass is one that leads to a shot, whereas an assist is only recorded if that shot is scored.

To use Mesut Ozil as an example, he averages 3.47 key passes per 90 over the past five seasons, which is an amazing total. However, when focusing on his assists, it's just 0.35.

Ultimately, these modern metrics offer a more thorough understanding than before, and they provide a level of insight beyond the simple numbers.