Comments on: Toward a basketball taxonomy

By: burgos

burgos — Mon, 03 Mar 2008 01:20:41 +0000

I am a ‘Moneyballer’ and have often wondered when the SABR approach would be applied more to other sports.

By: Alex A. Hutnik

Alex A. Hutnik — Fri, 29 Feb 2008 22:28:03 +0000

Are you able to publish the code you used for this? I’m assuming you used R and the SNA package. I guess what I’m really after is seeing code examples that show the SNA package in use.

By: NBA similarity networks « The Arbitrarian

NBA similarity networks « The Arbitrarian — Thu, 28 Feb 2008 19:48:19 +0000

[...] on February 25, 2008 at 7:43 pm5 The microcosmic NBA petri dish « The Arbitrarian [...]

By: rapidadverbssuck

rapidadverbssuck — Tue, 26 Feb 2008 01:32:49 +0000

That sounds awesome. I’m glad to see that you seem to have picked up where my knowledge runs a little thin, but to the extent that I can fully follow your process, it seems to be very much in line with my original intent in starting this project–autoclassification of player types. If you’re interested, I could provide you with a .csv of the Top 1000 distances matrix as well.

It sounds to me like you have a position/size (which correlate anyway) eigenvalue, and an offensive/defensive alignment eigenvalue. Shaq, AI, Malone, et. al. all show up as reddish in my simple color scheme classification, while Bowen, McMillan, Horry et. al. are more known for their “grit” or defense. I would love to see more of your results, I hope your APBR clearance comes through soon.

By: findingneema

findingneema — Tue, 26 Feb 2008 01:17:31 +0000

I just registered for an APBR account, when I get that I’ll respond more fully. I saw your post over there as well, and I thought I’d add some stuff.

I’ve looked at your top 500 players distance matrix (p.s., the player names are not in the same format for the rows and columns, a little perl fixed that), and I decided to try some advanced clustering techniques.

I used multi-dimensional scaling to reduce the 500 dimension matrix to something more manageable, and it gave the first 12 dimensions as accounting for 99% of the variance. The first eigenvector (53.4% of the variance) basically corresponding to size, with little guys at one end and big men at the other.

The 2nd eigenvector (15.8%) is more troublesome as players like Shaq, AI, Moses Malone, World B Free, and David Thompson are at one end, and the other end has Bruce Bowen, Nate McMillan, Robert Horry, Chris Duhon, and Popeye Jones. I’ll post more on this later.

I also used self-organizing maps to cluster the matrix (using correlations between the distances), and it looks like a 4×2 model fits the data pretty well. For example, it breaks up the forwards into two classes, with Karl Malone, Kevin Garnett, Charles Barkley, Dirk Nowitzki, and Antawn Jamison into one group, and Shawn Marion, Lamar Odom, Rasheed Wallace, Josh Howard, and Shane Battier into another. There’s some pretty interesting results from it. Anyway, I’ll post more later.