Fantasy managers who sign up as a Member to our website get access to a whole host of player and team information.
Among the underlying statistics on offer is expected goal (xG) data, which many will be familiar with already.
For the uninitiated, xG is the measure of a quality of a shot based on criteria such as proximity of goal and angle of attempt.
For example, a shot from inside the six-yard box will carry a higher xG than a speculative effort from 35 yards.
Along the same lines, expected assist (xA) data calculates the quality of chance being created.
Expected goal data doesn’t predict when goals will be scored, of course, and isn’t a crystal ball for the future – rather, it is a measure of chances created in the past.
Nevertheless, many find xG data useful.
Fantasy managers can use the information to see which players are consistently being presented with – or providing – high-quality opportunities, which in turn can be helpful when making future team selections.
Looking at the calibre of opportunity, rather than just an overall number of shots on goal or chances created, is often key.
‘Big chances’ – a situation where a player should reasonably be expected to score – are a decent barometer of this.
56.5% of the 1,072 goals scored in the Premier League last season were deemed big chances.
Of the 1,532 such gilt-edged opportunities created, 606 were scored – a conversion rate of 39.6%.
Site user Bowstring the Carp has produced an excellent, in-depth guide to xG in his recent Community article, which goes into the finer details.
He followed this piece up with a look at the pros and cons of xG, which is well worth a read to learn about its overall usefulness and limitations.
The rest of this article will be dedicated to looking at the xG data available in our Members’ Area, drawing attention to the players who had significant expected goal involvement in 2018/19 and those who were under/overachieving based on their actual output.
Become a Member and access our data
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Join now to get the following:
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