Nice work. I expect the OP will be hearing from NBA teams at some point. There was a guy who made something very similar a couple of years ago (not in R, but same functionality) and he now works for an NBA team (a friend of mine works on the same analytics team). Of course, he had to bring his side-project with him and take it offline.
This is why I love R. This works great and the code is super clean.<p>NBA Notes: I love the fact that the players are sorted by first name and that made no difference. This is the easiest to follow professional sport in the world.<p>Carmelo Anthony: He started shooting from the right 3pt corner really well. Now 10 years later he is hot from the left corner and not that right polar opposite. Seems like the sides he shot from changed in a pattern. I bet you the scouts were like Anthony can't shoot from the right make him shoot from there and he would practice that side.
With the way Curry has been playing some of these charts are going to need to be re-scaled to include beyond the half court line. 35/57 from 28 feet and farther currently (61.4%).
I've had looked into Shiny apps for my own ggplot2 work, but after seeing the usage in this post, I'll definitely give it another look!<p>What initially kept me away was the amount of idiosyncratic code required for the server/UI without many tutorials, which made compatibility annoying. Looking at the code in the GitHub, the code still seems pretty difficult. That, and <a href="http://www.shinyapps.io/" rel="nofollow">http://www.shinyapps.io/</a> (which the OP is using) is very ambigious in their pricing on how "Active Hours" work, which is a valid concern when a post hits the front page of Hacker News.
Similar but not as featured and fancy with vega:<p><a href="http://sandbox.github.io/demos/nba-shot-chart-vega/" rel="nofollow">http://sandbox.github.io/demos/nba-shot-chart-vega/</a><p>which includes link for one done in python, and Kirk Goldsberry’s articles on Grantland.
Steph Curry's 2015-16 shot chart is the epitome of sports data analysts insistence over the past few years that three pointers are far more efficient shots than most two point shots. Almost all of Steph's shots this year are either at the basket or behind the three point line.<p>I guess it just took a generational player, who could shoot not only extremely accurate from distance, but can also fire of a shot quickly as well as create his own space for shots, to fully exploit the extremes of that data.
Wait a sec, how does the NBA get the shot data?<p>It looks like there's positional information in there. Are they collecting this using machine vision?