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Pat Dempsey from Tribalfootball.com's partners Flashscore.com breaks down the xG statistic and details how it reflects in the performance of this season's top strikers across Europe...

Expected goals - or 'xG' as it now is commonly known - has been the boom stat in football over the last few years and is now firmly part of the sport's lexicon amongst experts and fans.

xG illustrates the chance value of a shot based on historical data - that is, how likely it is to result in a goal. It allows us succinct ways of talking about various things in football - most commonly, the pattern of play: which team is dominating a match with the better chances.

We can also use the stat to talk about individual performances. This is especially relevant for strikers, wide forwards and attacking midfielders as xG provides us with tools to talk about the effectiveness of goalscorers.

If a striker has a high xG value in a game or a season, they have had many good chances to score. If a striker has scored more goals than their xG suggests, they have outperformed their peers, based on what historical data suggests.

How shots are rated in xG models

Comparing expected and actual goals leads us to identify three types of players. Firstly, those that score more than they should - i.e. have a higher goals count than xG count. Secondly, those that score just as much as they should (goals being roughly equal to xG). And lastly, those who underperform based on the chances offered to them (goals less than xG).

In what follows, to illustrate these three archetypes and how xG can be used to discuss performance value in football, we will present some well-known examples of players from around Europe's top leagues and how their goalscoring numbers

Aston Villa