Without splitting up the data for batter handedness (so including lefties and righties), here are the results:

Family: binomial

Link function: logit

Formula:

whiff ~ pfx_z + pfx_x

Parametric coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) -1.633871 0.039651 -41.207 < 2e-16 ***
pfx_z -0.027619 0.008566 -3.224 0.00126 **
pfx_x -0.045266 0.010214 -4.432 9.34e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R-sq.(adj) = 0.00235 Deviance explained = 0.297%
UBRE score = -0.17328 Scale est. = 1 n = 10000
If I look at sliders thrown to just right handed batters, pfx_z no longer has significance (p = .12) but pfx_x does. For left handed batters, both types of movement are very significant.
I have also not accounted for the count in which these sliders were thrown, and MLBAM classification issues play a role here as well.

is anyone else amazed by jonny venters getting a 72% GB rate? not to mention striking out 10/9 IP to go along with it. ]]>

That’s quite useful.

Could you post the equation your (quick and dirty) regression gets you?

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