In his article “Longevity, production and efficiency in rugby kickers: a re-analysis of Ken Quarrie’s kicking dataset“, Eoin Gubbins are explaining different ways of looking at the dataset on which a study by New Zealand Rugby Union sports scientist Ken Quarrie, earlier mentioned in my post “What a study of rugby kickers can teach your business” is built.
But – what he does, in a very interesting and well written article, is just that. He’s explaining that there is multiple ways to view a dataset.
That leads to a missing focus. So in the article Eoin introduces a focus, something he wants to study. In this case – greatness.
(To be perfectly clear, the study didn’t lack focus. On the contrary. But it wasn’t greatness that was the focus of the study.)
Eoin argues that the study doesn’t account for certain variables, but I would argue that until you’ve defined your focus, you can’t know which variables are required, obsolete or missing.
I’m also not arguing with Eoin, or the commenters of The Score, about the fact that greatness probably involves more variables than the study used, but then the study was not intended to rank players by greatness, only on a percentage altered to account for kick location on the ground, the context of the match (based on score difference and time elapsed) and which stadium the kicker was at.
So when wanting a list with the greatest kickers, Eoin has to come up with a definition of greatness – “true greatness involves production over a long period at a highly efficient rate” – to be able to create it. Greatness can be a lot of things depending on your view, but this was Eoin’s definition of it and when defining it this way, the study was lacking certain variables to be able to produce a list of the greatest kickers. So in that aspect Eoin was right.
So definitions first, so we know what we are looking for/at. Then we can select and study the data appropriate for the definition. Otherwise it’s just a matter of opinion/view and that is not what evidence based coaching and learning is about.
Thanks for a good article on some fascinating data, Eoin. And thanks for forcing me to think twice about definitions and data.
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