Having already calculated launch angle, it seems logical that the next step would be to calculate exit velocity. It would seem as though some relationship between hit distance (calculated using the home plate location found in part three and the coordinates of the batted balls) and launch angle would yield an approximation for exit velocity, and indeed, such a relationship appears to exist at the major league level.
Despite this, using the model that I reverse engineered from Statcast and correcting for differences in hit-tracking between the stringers and the MiLB, I found that such a model was grossly inaccurate at the minor league level. Shown below are MiLB hitters with at least 200 BIP in 2016 and 200+ BIP in the majors in 2017.
Perhaps the depth of batted ball locations are inaccurate, or perhaps the model itself has issues. I think this is a difficult challenge because we're trying to measure the size of an intangible object using its shadow - it's not as simple as plugging the values into excel's equation solver, as we need to have method behind our model. I think of this challenge as a WIP, and I hope to update this post with a solution soon, but for now I have no clear way of estimating MiLB exit velocity.
Still, the rest of the data that we're working with appears solid and powerful. I've already revealed a couple functions that I've been using, and I hope to develop an R-package for all of these functions, including heatmaps, splits, date-ranges, a built-in R scraper, and more. I hope to keep y'all posted on this later this summer.
Thank you for reading this series! I hope this was insightful or at least entertaining. In my opinion, not enough public analysts are using MiLB data, and while it's certainly rough around the edges, there's still valuable information to be gleaned from it.