# The math behind basketball’s wildest moves

At the moment I’m taking the Math in sports course at edX/University of Notre Dame and since you tend to see the things you focus on, math related sports or sports related math, seems to be everywhere right now.
The other day I stumbled across this fascinating video from TED: The math behind basketball’s wildest moves, where Rajiv Maheswaran talks about analysing movement. Wow! I try to extrapolate into the future and imagine where this kind of technology will take us in, say rugby in a couple of years and it’s… well, fascinating! And a bit scary too.
Have a look and see for yourself. Where do you think this technology will take us and our sports?

# Definitions first, then data

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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# Overview – The Performance analysis process

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# Simple frequency form

One of the simplest methods of gathering data is counting the number of times a certain event occurs and writing it down on a piece of paper. That’s really all there is to it. But to give that data a bit more meaning you might want to add a quality aspect to it as well, for instance if you’re counting scrums won you might want to set up a table like this:

```                 Team A       Team B
Won cleanly
Under pressure
Lost

```

where Team A and Team B are representing the team that puts the ball into the scrum. Then you can just count how many scrums there where during the match, how many your team won cleanly, won under pressure or lost. You also get how many times you were able to put pressure on the opponents, even if they won the scrum in the end. With this data you can quickly evaluate scrum efficiency on a basic level and talk to coaches about why the numbers look they way they do. It really doesn’t get any simpler than this, does it?