The Catcher’s Call for Effective Velocity

To date, all of my Effective Velocity research has centered on pitchers. That is probably because the story that introduced me to the concept of EV focused on Trevor Bauer in the narrative, which tied the concept to the position in my mind. However, the more I’ve thought about it over the last several weeks, the more convinced I’ve become that catchers would likely share the responsibility for their pitchers’ EV adherence. After all, for every Bauer, there are probably dozens of pitchers who rarely if ever shake off the signs their catchers throw down. Meanwhile, the concept of EV falls into the game-calling strategy bucket that we more typically associate with catchers than pitchers.

To test their relative responsibility, I ran a series of year-to-year correlations for Effective Velocity Adherence Rate (EVAdh%), the seasonal EV stat I developed a few weeks ago. As I suspected, catchers show a strong correlation of 0.526. However, I don’t think that answer is satisfactory because of an inherent selection bias. Many catchers play for the same team in consecutive seasons, and that means that many catchers share many of the same pitchers on staff in consecutive seasons. I don’t want my catcher correlations to really be telling me about the consistency of their pitchers, and so I ran a separate test that looked only at the catchers who changed teams from one year to the next—and so would presumably be catching all new pitchers. For those catchers, the correlation decreased but remained a moderately strong 0.438.

Pitcher and Catcher Year-to-Year EVAdh% Correlations, 2007-16
Players Sample Size Correlation
All Pitchers 962 0.697
New Team Pitchers 183 0.666
All Catchers 350 0.526
New Team Catchers 90 0.438

Pitchers show similar patterns in their year-to-year consistency in EV adherence. The fact that their correlations are higher than catchers suggest they probably have more to do with EV adherence than their battery mates. That would make sense if for no other reason than a pitcher’s ability to vary his Effective Velocity is somewhat tied to his repertoire of available pitchers. However, it is worth nothing that my catcher tests have smaller sample sizes, an unavoidable annoyance given that teams tend to comprise of five regular starters and at most two regular catchers. There is some chance that catchers are similarly responsible for EV adherence as their pitchers, but there is not enough of a real-world balance in the data to demonstrate it.

Whatever the case on that point, it’s clear that catchers have a lot to do with EV adherence. Unfortunately, it’s no easier to determine what the impact of that EV adherence is on catchers than it was for me on pitchers. The initial two tests I ran to try to identify some performance impact compared EV Adherence Rate to Catcher ERA and Adjusted Earned Runs Saved, the latter which is a component of Baseball Info Solutions’ Defensive Runs Saved (DRS) statistic that compares catchers’ ERAs to other catchers’ ERAs with the same pitchers using a with-or-without-you calculation. I thought in particular that second test had some potential because it has a built-in mechanism to compare catcher performance to other catchers, and so differences in EV adherence would presumably manifest between the different catchers. However, neither Catcher ERA nor Adjusted Earned Runs Saved correlated with EVAdh% at all.

As I settled on last week, I think the issue with those tests might be that the impact of EV is felt on specific pairs of pitches, which represent a small fraction of all pitches and may therefore be getting drowned out in aggregated seasonal results. I’ll try to narrow my focus accordingly going forward. In the meantime, if you are interested in knowing which present-day catchers best adhere to EV theory, here are the leaders from 2016:

Catcher EVAdh% Leaders, 2016
Catcher EVAdh%
Josh Phegley 70.8%
Jason Castro 70.2%
Curt Casali 70.0%
Wilson Ramos 69.8%
Jonathan Lucroy 69.6%
Yan Gomes 69.5%
Evan Gattis 69.3%
Mike Zunino 69.2%
Sandy Leon 68.8%
Chris Gimenez 68.8%

And here are the trailers:

Catcher EVAdh% Trailers, 2016

This article includes research on the theory of Effective Velocity, which was created by Perry Husband. The research presented here estimates EV but is less sophisticated than Husband’s work. To read more about Husband’s work or to learn about the services he offers, check out his web site.

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Scott Spratt is a fantasy sports writer for FanGraphs and Pro Football Focus. He is a Sloan Sports Conference Research Paper Competition and FSWA award winner. Feel free to ask him questions on Twitter – @Scott_Spratt

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