A full breakdown of Ethan’s new and improved metric from start to finish. I decided to completely rebuild my pitch quality metric from the ground up using a much more statistically sound model building process. This article will describe that process in detail and be accompanied by my reproducible code.
Author’s Note: The metric discussed in this article (QOP) will be called xRV (expected Run Value) in future work.
For information about QOS+, a sister metric of the one described in this article, [click here_](https://towardsdatascience.com/revamping-my-pitch-quality-metric-66cb2dbe8d8a#3aac) or scroll all the way to the bottom of this article._
Earlier this year, I created a model to try to quantify the quality of an MLB pitch. The idea was that each pitch can be given an expected run value based on its zone location, its release point, and some of its pitch characteristics. Though I was initially happy with the results of my metric (originally introduced here) and the subsequent analysis I was able to do (here, here, here, and here), I acknowledged that there was room to improve from a modeling standpoint.
In the last few days, I decided to completely rebuild my pitch quality metric from the ground up using a much more statistically sound model building process. This article will describe that process in detail and be accompanied by my reproducible code, found here.
For this project, I began by asking
How many runs would we expect to be scored on each individual pitch of the 2020 season?
In order to answer this question, I decided to use the linear weights framework which gives every pitch outcome (ball, strike, single, home run, out, etc.) a run value based on how valuable that event has been in previous games. The idea is that pitchers who throw more pitches that are likely to get good outcomes (strikes and outs on balls in play) should be rewarded and pitchers who throw more pitches likely to lead to bad outcomes (balls and baserunners on balls in play) should be punished.
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