Poll 2018: Which Group of Pitchers Performs Better?

Since 2013, I have polled you dashingly attractive readers on which group of pitchers you think will post the better aggregate ERA post all-star break. The two groups were determined based on ERA-SIERA disparity, pitting the overperformers versus the underperformers during the pre-all-star break period.

I came up with this idea given my faith in using SIERA over smaller samples, rather than ERA, as I generally ignore ERA completely as late as the middle of the season and it’s interesting to learn how everyone else thinks. Will the SIERA outperformers continue to outperform, perhaps due to continued strong defensive support and/or more pitcher friendly ballparks, or will the magic vanish? And is it just bad luck that is due to reverse course for the SIERA underperformers or are they being hampered by one of the aforementioned factors that should continue to play a role the rest of the way?

My initial population group consisted of 117 starters with at least 70 innings pitched, which included some that are no longer in a rotation and/or are injured. Group A is composed of the 10 largest SIERA outperformers, while Group B is composed of the 10 largest SIERA underperformers.

Group A – The SIERA Outperformers
Name K% BB% LD% IFFB% BABIP LOB% HR/FB ERA SIERA ERA-SIERA
Jon Lester 19.1% 8.9% 24.1% 7.5% 0.253 83.6% 10.8% 2.58 4.64 -2.06
Carlos Martinez 21.6% 11.7% 17.9% 14.8% 0.292 75.9% 4.9% 3.08 4.57 -1.49
Blake Snell 28.3% 9.9% 20.0% 7.5% 0.243 86.3% 11.3% 2.27 3.69 -1.42
Michael Wacha 20.0% 10.1% 29.5% 7.6% 0.249 75.0% 13.6% 3.20 4.55 -1.35
Kyle Freeland 19.3% 8.0% 17.9% 7.8% 0.269 82.5% 11.2% 3.11 4.39 -1.28
Jacob deGrom 30.7% 6.2% 24.0% 17.2% 0.282 85.9% 8.0% 1.68 2.93 -1.25
Reynaldo Lopez 17.0% 9.8% 18.6% 14.3% 0.270 73.5% 8.1% 3.91 5.14 -1.23
Miles Mikolas 17.3% 4.2% 21.8% 11.9% 0.266 75.8% 7.9% 2.79 4.00 -1.21
Jhoulys Chacin 18.1% 9.7% 22.8% 8.4% 0.267 71.7% 6.7% 3.68 4.83 -1.15
Aaron Nola 26.1% 7.0% 18.6% 14.6% 0.260 79.6% 6.3% 2.30 3.42 -1.12
Group Average 21.8% 8.4% 21.4% 11.1% 0.265 79.2% 8.8% 2.83 4.17 -1.35
Lg Avg (All Starters) 21.5% 8.1% 21.2% 10.1% 0.290 72.8% 13.1% 4.21 4.20 0.01

Group B – The SIERA Underperformers
Name K% BB% LD% IFFB% BABIP LOB% HR/FB ERA SIERA ERA-SIERA
Domingo German 27.0% 8.8% 15.0% 9.6% 0.289 64.8% 16.9% 5.49 3.65 1.84
Alex Cobb 15.0% 6.0% 17.6% 9.3% 0.334 62.6% 15.9% 6.41 4.58 1.83
Luis Castillo 21.5% 7.8% 21.0% 8.7% 0.303 66.7% 18.4% 5.49 4.14 1.35
Nick Pivetta 27.4% 7.3% 15.4% 8.8% 0.329 71.8% 15.4% 4.58 3.44 1.14
Jason Hammel 14.0% 6.4% 26.0% 6.6% 0.340 61.9% 9.2% 6.15 5.02 1.13
Sonny Gray 20.9% 9.6% 19.4% 8.6% 0.327 69.0% 13.6% 5.46 4.35 1.11
Jakob Junis 20.9% 6.3% 17.2% 9.8% 0.274 76.1% 18.2% 5.13 4.16 0.97
Wei-Yin Chen 17.8% 8.8% 15.0% 11.3% 0.309 66.2% 11.3% 5.75 4.84 0.91
Brandon McCarthy 19.2% 6.2% 18.8% 1.4% 0.332 74.5% 21.7% 4.92 4.07 0.85
Jon Gray 28.5% 6.8% 21.9% 12.0% 0.376 64.0% 14.7% 5.44 3.19 2.25
Group Average 21.1% 7.3% 21.3% 8.7% 0.323 67.7% 15.2% 5.49 4.14 1.35
Lg Avg (All Starters) 21.5% 8.1% 21.2% 10.1% 0.290 72.8% 13.1% 4.21 4.20 0.01

Group Averages Comparison
Group Avg K% BB% LD% IFFB% BABIP LOB% HR/FB ERA SIERA Diff
A 21.8% 8.4% 21.4% 11.1% 0.265 79.2% 8.8% 2.83 4.17 -1.35
B 21.1% 7.3% 21.3% 8.7% 0.323 67.7% 15.2% 5.49 4.14 1.35

It’s pretty crazy to see that the groups have posted nearly identical overall skills sets, as the aggregate SIERA marks are virtually the same. Even odder is that the gap between ERA and SIERA are exactly the same, but in opposite directions!

The outperformers have struck out batters at a slightly higher clip, but have offset that advantage by allowing a higher walk rate. Surprisingly, the two groups stand with identical LD% marks, which you wouldn’t expect given the enormous BABIP disparity. The Group A IFFB% advantage explains a bit of that BABIP difference, but certainly most of it is a result of other factors. It’s rather eye-popping that the outperformers sit with major advantages in all three luck metrics. Some of them are related though, as a low BABIP and HR/FB rate are going to drive a high LOB%.

Overall, if we ignore the three luck metrics and ERA, these two groups look identical. So let’s get to the poll questions. I will close the poll before games start up again. Feel free to share your poll answers and why you voted the way you did.


 

 





Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.

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andrewj
4 years ago

Would you be able to share the results of your past polls, since 2013, and which group has performed better in aggregate (and maybe individually, too, if its not too cumbersome) in the second half? Thanks!

andrewj
4 years ago
Reply to  Mike Podhorzer

Awesome! Thanks!

LenFuego
4 years ago
Reply to  Mike Podhorzer

Care to summarize 2014 and 2016 results from memory?