Hello everybody,

this
is a follow-up to my last post on 'distancology' – the science of
turning all shot charts into one colorful picture. You can find a half-way clean code in my github account. If you run MakeHeatMap.R, you should actually be able to reproduce the result.

One
question that one naturally can ask, when comparing the shot
distribution of players, is how consistent or reliable those shot
distributions are. For example, in my last article I sorted around
200 players into 10 distinguishable groups, using a (vague) cutoff.
But I could as well have used 5 or 20 groups. Now, the question
regarding reliability is: If you compare year to year, how many
players would remain inside the same shot cluster?

Because,
if I would label somebody as a 'corner three guy' due to his shot
distance distribution in one year, but the next year there is a 50%
chance that he's actually a 'typical wing player' guy – that would
be pretty useless.

^{1}
Long
story short, what I did is to combine the distance distributions for
two years – and the result is pretty mindblowing

^{2}. The following plot works very similar to the one that I used in my previous article. I just changed the column on the left, so that it indicates the effective field goal percentage instead of the shot attempts. This way it is easier for people to be in awe about Stephen Curry. It shows both seasons for every player that had at least 600 attempts (data is from the 3rd of March) during this and the last season – and here it is^{3}: