In the aftermath of Bayern Munich defeating Paris Saint-Germain in the 2020 Champions League final, there was a lot of talk on social media regarding the big chance missed by Kylian Mbappé. In particular, many were highlighting how Mason Greenwood wouldn’t miss such a chance.
Finishing versus Positioning: Mason Greenwood Shot Map 2019/20
On the face of it, there may be some truth behind this statement. If we look at his Premier League shot map for last season, the only big chance he had in this position, he scored.
Goals are indicated by the red circles on the shot map to the left. The larger the circle, the bigger the chance. Chances with an Expected Goals (xG) value of 0.28 and above are considered big chances. As in, statistically speaking, such chances have typically been converted at a rate of 28% and above in the past.
The large red circle on the edge of the 6-yard box is the big chance we are referring to. It was Greenwood’s first of ten Premier League goals and came during the 3-3 draw with Sheffield United.
Of course, that isn’t his only big chance of last season. We can see Greenwood also missed a big chance against Norwich.
This isn’t the only big chance he has missed in his career.
We can see above Greenwood missing an almost identical chance to the Norwich one in pre-season against Perth Glory.
Of course, this isn’t a huge problem, all players miss chances. We provided an article for Empire of the Kop showing how using big chances missed as a measure of a player’s finishing ability was counter-intuitive.
All players miss big chances. Those who don’t, it is usually down to small sample sizes. But there isn’t a high-output forward in world football who has scored at least one-hundred career goals without also missing a host of big chances. It comes with the territory.
Finishing versus Positioning: How valuable is getting big chances
In that EotK article linked above, the graph of big chances vs total goals scored suggests how frequently a player is getting into the best scoring chances will largely determine how many goals they can score.
With that in mind, we can reasonably assume that players who are frequently getting into the best positions to score goals are going to score a lot of goals.
It’s good he is getting into the position to miss them. I start to worry about strikers when they aren’t getting into those positions because then where do the goals come from?– Every former striker on Match of the Day when players miss chances
Greenwood managed just two big chances in the Premier League last season.
He averages a big chance every 7.25 games, or 0.14 per 90.
It is extremely impressive that he has managed to score as many goals as he did last season in spite of not getting into good scoring positions often.
Is this sustainable? Or just a small sample size outlier?
In comparison, Mbappe had twenty-six big chances in a similar amount of games to Greenwood.
He averages a big chance every 0.65 games, or 1.54 big chances per 90.
Mbappe is over eleven times more effective at getting big chance shooting positions than Greenwood.
Finishing versus Positioning: How sustainable are their numbers?
If we plot all wingers and forwards big chances per 90 and goals from open play per 90, we can see the correlation between the two.
This chart suggests that those who will top the goalscoring charts will be the players who get into the best scoring positions for big chances to come their way.
It also shows how the biggest outliers on the graph are those with the smallest samples of data. Players like Haaland, Fati, Depay and, of course, Greenwood.
The R² and p-values are: R²=0.647, p-value < 0.0001.
In very simplistic terms, R² tells us what percentage of the variation in data can be explained by the model. In this case, around 65% which is quite high.
The p-value indicates how well the data fits the model. A p-value of less than 0.05 is considered a good fit to the model.
Therefore, the numbers here suggest this visual both explains a lot of variation in the data and it is significant.
Working with just one season of data gives us a lot of small sample size outliers. Plus, adjusting metrics per 90 further exacerbates this.
Looking at total goals scored from open play versus big chance shots the last six years gives us this.
Note the R² has now increased to 0.931 and the p-value is even lower at < 0.000001.
What this graph very clearly tells us is that the players who score the most need to also get a lot of big chances. Therefore, in the battle of finishing versus positioning, finishing is of course vital. But, if you want to be hitting those big numbers, your positioning will largely dictate your ‘glass ceiling’ in terms of the number of goals your finishing can get you.
The graph also passes the ‘Messi test’. Essentially, if you plot any two attacking metrics against each other, is Messi a freak of nature?
Yes. Yes he is!
A glossary of all the terms used in this article and throughout the site as a whole is available here. Also, click on any image in the article for a full-size high definition version.
All data used in our articles is sourced from Understat, FBRef, Sofascore, Transfermarkt and 538.