I ran the accuracy shot chart for Stephen Curry again using data from stats.nba.com, this time for the first 19 games of the 2015-16 regular season and the conclusion is: version 15-16 is better than the MVP 14-15 version. Is he bound to get another MVP?
In my last post I produced some NBA shot charts in R using data scraped from stats.nba.com and
ggplot2. This time I extracted all shot location data available for 490 players and linked it to a Tableau dashboard.
The first dashboard shows each shot attempted during the 2014-15 NBA Regular Season. On the right, it is possible to select team, player, shot type, shot zone and shot range. The table above the chart is also updated in line with the filter selection (click on the image to open the dashboard on a new window). Continue reading
A while ago I found this fantastic post about NBA shot charts built in Python. Since my Python skills are quite basic I decided to reproduce such charts in R using data scraped from the internet and
Getting the Data
First we need the shot data from stats.nba.com. This blog post from Greg Reda does a great job explaining how to find the underlying API and extract data from a web app (in this case, stats.nba.com).
To get shot data for Stephen Curry we will use this url. The url shows the shots taken by Curry during the 2014-15 regular season in a JSON structure. Note also that
PlayerID are parameters in the url. Stephen Curry’s
PlayerID is 201939. Continue reading
This is an interesting talk by Rajiv Maheswaran, Professor at the University of Southern California’s Computer Science Department.
He talks about capturing the massive amount of data that happens during a basketball game and using machine learning to understand complex events like “post ups” and “pick and rolls”. The machine can now see the game with the eyes of a coach.