Effective Velocity Disciples

Before I posted my Effective Velocity article from last week, Perry Husband was gracious enough to provide me with some feedback, much of which will guide me in improving the simplified EV calculation I am currently using. But rather than refine my math right away, I want to push forward a bit to a point where I have a real metric to evaluate pitchers. My hope is that I can then more easily test whether my attempted improvements to my EV calculations are making real improvements by testing the performance of the metric. To follow that roadmap, my next step now that I have calculated the EV of every pitch is to figure out which pitchers are executing an EV-friendly game plan, whether or not they realize they are doing so.

As a bit of a recap, the theory of Effective Velocity is that the location of the pitch influences how the batter perceives the velocity of the pitch. That is because pitches that are higher and more inside require the bat to reach in front of the plate to make solid contact while pitches that are lower and more outside allow the bat to make solid contact behind the plate. My research from last week showed that pitch pairs with higher EV differentials resulted in weaker contact on average than pitch pairs with lower EV differentials, even when the actual velocity differentials were the same. And so, to follow that idea a bit further, pitchers who more often throw pitch pairs with higher EV differentials should allow weaker contact on average than pitchers who less frequently throw pitch pairs with higher EV differentials.

There isn’t a rubric for what EV differential is sufficient to throw off a hitter’s timing, and rather than become sidetracked by that tangent, I figured I’d start at what I estimated would be a reasonable boundary of 4 mph. In other words, if a pitcher throws one pitch and then another pitch right after it to the same batter that is at least 4 mph different in EV, then I will say that he adhered to the principles of EV. Meanwhile, if that second pitch is less than 4 mph different than the first pitch, I will say that he failed to adhere to the principles of EV. With that standard established, I could then calculate every pitcher’s adherence rate, which I will label as EVAdh%.

I considered a few different options to test whether or not a pitcher’s EVAdh% mattered, and I settled on ERA – FIP differential. Because ERA includes the impact of balls in play while FIP looks only at strikeouts, walks, and home runs; the major difference between the two should be a pitcher’s allowed performance on balls in play. Traditionally, DIPS theory has suggested that pitchers have limited control over their allowed performance on balls in play, but my belief is that, if Effective Velocity is real, pitchers who follow its principles should on average allow weaker contact and therefore have a lower ERA than FIP.

Effective Velocity Adherence Rate – ERA vs. FIP
EVAdh% Percentile EVAdh% Range ERA FIP ERA – FIP
80th 70.0% – 75.2% 3.69 3.79 -0.10
60th 68.8% – 69.9% 3.81 3.76 0.05
40th 67.2% – 68.7% 3.91 3.89 0.02
20th 65.6% – 67.1% 3.81 3.83 -0.02
0th 54.4% – 65.5% 3.96 4.00 -0.04

From 2013-16, that appears to be the case. In particular, the pitchers who fall north of the 80th percentile in EV adherence, which in practice captured pitchers with EV adherence percentages that ranged between 70.0 percent and 75.2 percent, had a collective ERA that was 10 points lower than their FIP. For the other quintiles of pitchers, the ERA – FIP differential was much smaller, between -4 points and +5 points. If one is willing to buy into the idea that the 10-point differential is meaningful, then it seems that the pitchers who most closely follow EV principles are benefiting from them on their balls in play.

To my mind, that is a pretty significant finding. And, because of that, I’m a little paranoid about it. Is it possible that I’m seeing these results because of some selection bias I’m not aware of? The first one that occurred to me was that maybe the pitchers who better adhere to EV principles just happen to have more diverse repertoires of pitches. However, I think I can rule that out as a factor because, when I ran the same ERA – FIP test using actual velocity differentials instead of EV differentials, there was no discernable pattern to the results.

Normal Velocity Adherence Rate – ERA vs. FIP
VeloAdh% Percentile VeloAdh% Range ERA FIP ERA – FIP
80th 54.6% – 64.7% 3.75 3.72 0.03
60th 49.7% – 54.5% 3.69 3.74 -0.05
40th 47.0% – 49.6% 3.77 3.82 -0.05
20th 43.2% – 46.9% 4.03 4.00 0.03
0th 23.4% – 43.1% 3.96 4.01 -0.05

If you have thoughts about this, I’d love to read them in the comments. For now, I’ll tentatively move forward with the idea that pitchers who best adhere to EV principles can expect to have lower ERAs than FIPs. So who are those pitchers, you might wonder? Well, here are the ones above the 80th percentile from 2013-16.

Effective Velocity Adherence Rate – 80th Percentile Pitchers
Pitcher EVAdh% ERA FIP ERA – FIP
Ricky Nolasco 75.2% 4.58 3.88 0.70
Adam Wainwright 74.8% 3.18 3.04 0.14
Trevor Bauer 74.3% 4.37 4.19 0.18
Jered Weaver 74.1% 4.13 4.61 -0.48
A.J. Griffin 73.6% 4.29 5.02 -0.73
Collin McHugh 73.4% 4.00 3.65 0.35
Scott Feldman 72.8% 3.85 4.15 -0.30
Jeremy Guthrie 72.5% 4.57 4.85 -0.28
Tanner Roark 72.3% 3.01 3.71 -0.70
Tom Koehler 72.2% 4.14 4.30 -0.16
Chris Sale 72.2% 3.04 3.01 0.03
Hyun-Jin Ryu 71.8% 3.28 3.03 0.25
Miguel Gonzalez 71.6% 3.90 4.54 -0.64
Gerrit Cole 71.5% 3.23 2.98 0.25
Max Scherzer 71.2% 2.95 2.90 0.05
Taijuan Walker 71.1% 4.18 4.28 -0.10
Clayton Kershaw 71.0% 1.88 2.03 -0.15
Marco Estrada 70.8% 3.67 4.33 -0.66
Clay Buchholz 70.7% 4.01 3.75 0.26
Julio Teheran 70.5% 3.33 3.81 -0.48
Adam Warren 70.5% 3.50 3.88 -0.38
Ervin Santana 70.5% 3.59 3.79 -0.20
Hisashi Iwakuma 70.5% 3.43 3.68 -0.25
Drew Smyly 70.5% 3.69 3.81 -0.12
Jordan Zimmermann 70.5% 3.44 3.44 0.00
Jeff Locke 70.5% 4.29 4.26 0.03
Johnny Cueto 70.4% 2.80 3.29 -0.49
Mat Latos 70.3% 3.84 3.69 0.15
CC Sabathia 70.3% 4.54 4.37 0.17
Brett Oberholtzer 70.3% 4.36 4.26 0.10
Eric Stults 70.2% 4.30 4.21 0.09
Mike Fiers 70.1% 3.92 4.16 -0.24
Matt Garza 70.0% 4.37 4.14 0.23
Nathan Eovaldi 70.0% 4.23 3.76 0.47
Erasmo Ramirez 70.0% 4.26 4.48 -0.22

Hello Trevor Bauer! I was really hoping to find Bauer near the top of this list as some reassurance I was doing things correctly. That’s because Bauer has been the most vocal proponent of EV theory among actual players. Well, good job, Trevor. You have stayed the course.

Not every pitcher on this list has enjoyed the overall EV trend in ERA – FIP differential—sorry, Ricky Nolasco!—but some of the names do make me wonder whether this could actually explain some of the mystical deception we’ve associated with certain pitchers. In particular, Jered Weaver slots in just behind Bauer in EVAdh% and has had an ERA nearly 50 points below his FIP over that four-year period, the sixth biggest differential in that direction of these 35 pitchers. I think conventional wisdom has been that Weaver has some specific aspect of his pitch mechanics that hides the baseball from batters, but is it possible he has just done a better job of keeping hitters off-balance with consistently big EV differentials with his pitches? Similarly, could EV be the reason that guys like Marco Estrada and Tanner Roark beat their FIPs every year even in hitter-friendly parks? Hopefully, continued research can help answer those questions.

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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So, does throwing inside correlate with outperformaing peripherals?