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 mindblowing2. 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 is3: