Corrections to previous Kershaw post

Long story short, I was fine-tuning the regression methods I've been employing to generate those pitch location heat maps, my first example being Clayton Kershaw's slider and curveball. A couple of hours working on this and I realized that much of what was graphed was incorrect (in fact, disregard my previous post). I'm going to reproduce the swinging strike percentage graphs from my last post, which make a lot more sense now (not as high SwStr% values of 60%, smoother plots, down in the strikezone instead of up in the zone, etc.):

These (hopefully) corrected plots still show that Kershaw's sliders induce more swinging strikes than his curveballs. The differences between these ones and the previous post are that the breaking pitches are inducing swinging strikes down in the zone, not up in the zone (the orientation was off in the first post). Needless to say, I regret that I did not mention this in my first post, although I did find the images very fishy. I'm still fine-tuning these sort of local regression models and turning them into surface-fitted filled contour maps. I had several post ideas following the Kershaw one, including a look at Tim Lincecum's pitches and what went wrong this season compared to last season, but I may have to take some time to fully understand the statistics and the method behind the madness of these heat maps before I make a post and include my interpretations again.

Sample size is a huge issue, and I have been informed that looking at a particular pitcher's pitch type may not be suitable for a local regression surface fitting precisely because the sample size is too small (about 200 pitches seems to be too small, especially when modeling swinging strike probabilities where the number of swinging strikes is in the dozens for this particular situation).

Hopefully I don't make this mistake again, but again, I started this blog in order to explore analytical ways of presenting sports information, including graphically, and I'm glad I'm learning a lot along the way.