Google research scientist Peter Hauck used Weka and k-means cluster analysis to describe where Mariners right-fielder Ichiro favours hitting the baseball. He then used R to visualize the 6 clusters the k-means analysis identified:

I sometimes find K-means clusting tough to explain as a statistical technique, but this makes for a **great** example: if you're a fielder facing Ichiro, it might be a good idea to keep an eye on those six spots when he hits. See the full ananlysis on the Infochimps blog at the link below.

Infochimps blog: Clustering Baseball Data with Weka

Disclosure: I am not a baseball fan, other than in the most casual sense

Is this really useful? Doesn't this just show that he hits the ball where the typical defensive players are not positioned? Does Ichiro differ from other players somehow?

Posted by: jonmcrawford | June 16, 2011 at 12:13

This says to me that the traditional baseball positions are relatively optimal in terms of covering the field. It looks like if you took k=8 you would get the basic starting positions.

Posted by: rw | June 17, 2011 at 06:58