Unfortunately this almost mystical ability has constantly eluded every effort to pin it down even as the data generally available becomes more extensive and plentiful.
It is relatively easy to find strikers who are under or over performing their expected goals model based on any number of shot location variables, but persistence of this trait is less obvious.
Often the "cold" player from one month/week/match/half is the same "hot" scorer from a similarly recent time frame.
The Magical Finishing Skill Aura of "Dead Shot" Keen's boots worked for Billy Dane.
Shot volume and location can usually be relied upon to produce an expected goals figure that tracks fairly well a player's actual goal tally. But expecting even a season-long over performance to extend to a subsequent season (at least with a rudimentary model) is often a forlorn hope.
Random variation or rare or unlogged events, such as deflections and defensive pressure appears to overwhelm any attempt to observe a quality that is currently worth around 2 billion Chinese yuan.
A player may differ in finding space, receiving passes and anticipating where to be inside the box, but it is likely that the difference in finishing ability once the chance presents itself is going to be small between the elite.
Marginal gains, but also expensive mistakes if luck is purchased masquerading as a repeatable talent.
The biggest talent gap in finishing skill at the top level should lie between strikers and the rest of the outfielders.
So I looked at every shot (headers excluded) taken by every oufield player in a chance created solely from open play, which wasn't deflected and created an expected goals model based simply on the location of the shot. Sample size well into five figures.
Unsurprisingly, the location of the attempt in this sanitized shooting competition was a significant indicator as to the likelihood of a goal being scored.
I then told the model which shots were taken by "Dead-Shot" strikers and which came from the boot of non-strikers. The expectation being that this additional variable would prove significant and improve the likelihood of the strikers scoring at the expense of their team mates who were less talented at finishing (or they would presumably be strikers themselves).
It didn't.
In this dataset, knowing that a striker had taken the shot slightly decreased the likelihood of a goal, but this effect had almost certainly arisen entirely by chance. The model couldn't see a difference in the likely outcome regardless of whether the shot came from a defender or a striker.
If there is a difference in finishing ability between Premier League outfield players in different positions, as opposed to other desirable attributes possessed by a striker, a naive shot location model can't cut through the missing variables and noise to find it.
So instead I looked for a set of Premier League shots that should/might be (much?) less likely to be scored than others and could be picked up by a simple shot location model.
Scorcher's Billy Dane aside, most players don't have magical football boots, but they do have a preference for one foot over the other. I've yet to find a penalty taker who hasn't taken all his kicks exclusively with a particular foot.
Regular penalty takers used their penalty taking foot for nearly 80% of their shots from opportunities created in open play. So you also have to think they know something about the "finishing ability" of their standing leg.
I re did the model.
Again in the model shot location was a significant variable in the outcome of the shot. But this time when I added a variable for whether the shot originated from the player's penalty or non-penalty taking foot, that too was (almost) significant.
Benchmark figure, a shot with a player's "weaker" foot reduces the chances of a goal by around 10% of the value if it had if it had been taken with his penalty kick foot.
Every player demonstrates finishing ability and that difference might show itself on the 20% of occasions he uses his "swinger" and hits and hopes.
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