Time Splits, FG%, and eFG%

Came up with a couple more basketball shot location visualizations. I can see more and more how this data can be used in addition to visualizing hot zones for players, teams, and etc. but it's always fun to see more basketball graphics.

It's definitely been an exciting week taking more and more of an indepth look at visualizing some of the data out there. All of that and I've only just scratched the surface of the PITCHf/x data. I hope to do much more PITCHf/x visualization analysis in the future, perhaps after I've exhausted all my questions concerning the NBA and NFL PBP data.

Something to look for in the future: I've been thinking more and more about John Huizinga's paper/presentation at the MIT Sloan Sports Analytics Conference about the value of a blocked shot. Sebastian Pruiti's summary of Huizinga's work was indeed very helpful for those of us who weren't fortunate enough to attend the conference back in March. You can check it out over at his acclaimed blog, NBA Playbook. Anyway, I've been looking at using Basketball Geek's data (under the alias Ryan J. Parker) to see how I could use points per shot by location to assign a value to each block between 2006-2010, since most of the blocks are attributed to their corresponding shot locations. My preliminary calculations show ranges from Brendan Haywood saving 0.987 points per block to Andrew Bogut saving 1.152 points per block, which seems to agree with Huizinga's conclusion that Haywood is not as valuable a shot blocker as traditional numbers indicate. Still, my preliminary estimates aren't that promising, but I'll continue to dig into it. Huizinga did have access to more crucial data (such as turnovers leading to blocks, etc.) but I believe that he did not use shot locations to determine expected values of shots blocked. Instead, he used preblock situations and shot types, which probably is more effective. Anyway, I'm going to continue some work on this, and hopefully I'll publish my findings on Think Blue Crew soon (with a look at players who were blocked the most as well).

Here are the time splits of NBA shot location frequencies for 1st quarter, 2nd quarter, 3rd quarter, 4th quarter, first two minutes of 1/2/3/4 quarters, and last two minutes of 4th quarter plus overtime. And here... we... go.:






The following two graphs are of FG% and eFG% by shot location. Here, I hope to emulate what Eli Witus did over two years ago at Count The Basket, when he compiled his own play-by-play data to produce heat maps of FG% and eFG% by shot location. Check them out here and here. The color schemes and scales are a little different, but the idea is the same. For those of you who don't know, eFG% is effective field goal percentage, which takes the value of 3 pointers into account by adding an additional 0.5 for each 3pt made ((FGM + 0.5*3PTM)/FGA). Take a look:



Anyway, having the titles, axes labels, and more contrasting color schemes adds to the visualizations.

This is probably the end of the mass posts of different looks at shot location heat maps. If I use heat maps and filled contours again in the future, I will probably do studies on different players and different teams, but hopefully, this gave you a good idea of the sheer amount of information contained in Ryan J. Parker's dataset and the amount of exciting basketball analysis that can be done.