Modeling Strike Zones with Neural Networks Part 2

In part 1 I looked at building a neural network model of a batter strike zone in R. In this post I will show you have to use that model to estimate the boundaries of his personal strike zone. As a reminder, the reasons we want individual batter strike zones are:

  1. Batter heights and stances vary significantly
  2. The PITCHf/x sz_top and sz_bot fields have problems

Ok, so let’s get right to the R!

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Modeling strike zones with neural networks

In the 2016 Hardball Times Annual I wrote about evaluating umpire consistency. The analysis implements an idea Tom Tango originally blogged about. Here on my own blog I’m going to go a little more in depth on the underlying methodology and potential improvements. Along the way I’ll also relay some R tips I’ve picked up that are useful for sabermetric analysis, particularly on large datasets like PITCHf/x.

[If you haven’t already read the THT Annual, I strongly encourage you to pick up a copy (available on Amazon). Besides the background on my own work there are 300+ pages of great analysis and research.]

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