Poll 2022: Which Group of Pitchers Performs Better? A Review

During the all-star break, I once again polled you on which group of 10 starting pitchers would post a lower ERA during the second half, and which ERA range each group’s aggregate would fall into. Let’s now review the results.

Below are the aggregate averages of the two groups through the pre-all-star break period from the original poll article. Remember that you were voting solely on ERA, all other metrics and fantasy stats were irrelevant. Group A was composed of the SIERA overperformers and B, the underperformers.

Group Averages Comparison
Group LD% GB% FB% IFFB% HR/FB BABIP LOB% K% BB% ERA SIERA Diff
A 18.3% 42.5% 39.2% 12.8% 8.5% 0.245 80.1% 21.0% 7.0% 2.51 4.05 -1.54
B 21.7% 42.7% 35.5% 6.6% 15.2% 0.321 67.7% 22.6% 7.8% 5.15 3.90 1.25
League Average* 20.2% 42.6% 37.2% 9.9% 11.8% 0.290 72.5% 21.4% 7.7% 4.09 4.08 0.01
*Only starters are included

The poll results were as follows:

So 57% of you believed the SIERA overperformers, Group A, would post a lower 2nd half ERA, even with weaker 1st half skills than Group B. In past years, I don’t recall the gap being this large between the two groups. In some seasons, the underperforming group actually garnered a higher percentage of the votes! So it’s definitely not consistent from year to year, which leads me to believe that many of you are looking at the names to make your decision, rather than the underlying skills and stats.

The majority of you voted that Group A would finish with a 2nd half ERA at least a full run above their 1st half ERA, but still outperform their 1st half SIERA. That seems completely fair and the right amount of regression mixed with the acknowledgement of the possibility of actual “luck” metric skill ownership.

It was a similar pattern for the vote on where Group B’s ERA would fall over the 2nd half, with the majority voting between 4.00 and 4.49, which represented a nice improvement versus the group’s 1st half ERA, but above their SIERA mark.

Now on to the second half results.

Group A – The SIERA Overformers
Name LD% GB% FB% IFFB% HR/FB BABIP LOB% K% BB% ERA SIERA Diff
Michael Wacha 18.2% 40.6% 41.2% 14.7% 16.2% 0.286 79.5% 23.4% 3.9% 4.11 3.50 0.61
Tony Gonsolin 19.6% 45.4% 35.1% 8.8% 5.9% 0.232 78.3% 23.2% 7.7% 2.45 3.84 -1.39
Marco Gonzales 17.6% 37.6% 44.8% 12.0% 12.8% 0.282 70.8% 14.2% 4.6% 4.90 4.77 0.13
Sandy Alcantara 18.5% 49.2% 32.3% 7.3% 12.2% 0.291 78.7% 23.2% 4.7% 3.09 3.42 -0.33
Michael Kopech 14.3% 46.7% 39.0% 4.9% 12.2% 0.240 68.4% 20.4% 9.9% 3.96 4.46 -0.50
Alek Manoah 21.6% 35.6% 42.8% 12.6% 6.3% 0.245 84.7% 23.3% 7.9% 2.20 3.98 -1.78
Justin Verlander 13.6% 35.7% 50.6% 20.5% 1.3% 0.247 78.6% 31.6% 4.1% 1.51 2.73 -1.22
Taijuan Walker 19.7% 41.9% 38.4% 12.8% 14.1% 0.297 73.1% 20.6% 7.0% 4.80 4.12 0.68
Joe Ryan 21.7% 26.7% 51.7% 15.1% 11.8% 0.267 74.0% 28.5% 8.4% 4.14 3.59 0.55
Johnny Cueto 20.3% 42.3% 37.4% 12.1% 5.6% 0.309 67.6% 12.5% 3.7% 3.84 4.76 -0.92
Group Average 18.8% 40.0% 41.2% 12.6% 9.9% 0.276 75.1% 21.6% 5.9% 3.52 3.93 -0.41
League Average* 19.8% 42.8% 37.4% 10.4% 11.3% 0.291 73.0% 22.6% 8.0% 3.94 3.84 0.10
*Only starters are included

Group B – The SIERA Underperformers
Name LD% GB% FB% IFFB% HR/FB BABIP LOB% K% BB% ERA SIERA Diff
Hunter Greene 25.7% 32.4% 41.9% 22.6% 3.2% 0.284 89.3% 36.7% 5.8% 1.02 2.34 -1.32
Patrick Corbin 24.3% 41.8% 33.9% 6.3% 15.6% 0.367 63.8% 15.1% 5.7% 7.13 4.60 2.53
Austin Gomber 16.5% 45.9% 37.6% 12.2% 14.6% 0.272 71.4% 19.3% 5.5% 4.54 3.72 0.82
Jose Berrios 18.1% 43.2% 38.8% 6.8% 10.2% 0.342 68.1% 17.9% 6.7% 5.25 4.41 0.84
German Marquez 20.2% 46.4% 33.5% 7.7% 15.4% 0.275 72.4% 19.9% 8.0% 4.38 4.22 0.16
Lucas Giolito 26.0% 44.2% 29.8% 4.7% 12.5% 0.343 69.1% 23.3% 9.0% 5.15 3.95 1.20
Trevor Rogers 22.2% 46.0% 31.7% 10.0% 20.0% 0.356 66.9% 28.9% 6.2% 5.48 3.04 2.44
Alex Cobb 18.6% 60.2% 21.3% 6.4% 8.5% 0.348 75.7% 24.9% 6.9% 3.40 3.12 0.28
Alex Wood 18.5% 42.0% 39.5% 8.5% 19.1% 0.288 57.3% 23.0% 5.1% 7.08 3.63 3.45
Charlie Morton 17.6% 42.9% 39.6% 9.7% 18.1% 0.288 78.4% 29.5% 8.4% 4.19 3.30 0.89
Group Average 20.6% 45.6% 33.8% 8.5% 13.8% 0.320 71.0% 23.1% 7.1% 4.73 3.73 1.00
League Average* 19.8% 42.8% 37.4% 10.4% 11.3% 0.291 73.0% 22.6% 8.0% 3.94 3.84 0.10
*Only starters are included

Group Averages Comparison
Group LD% GB% FB% IFFB% HR/FB BABIP LOB% K% BB% ERA SIERA Diff
A 18.8% 40.0% 41.2% 12.6% 9.9% 0.276 75.1% 21.6% 5.9% 3.52 3.93 -0.41
B 20.6% 45.6% 33.8% 8.5% 13.8% 0.320 71.0% 23.1% 7.1% 4.73 3.73 1.00
League Average* 19.8% 42.8% 37.4% 10.4% 11.3% 0.291 73.0% 22.6% 8.0% 3.94 3.84 0.10
*Only starters are included

Let’s start with the answers to the poll questions. Group A did indeed post a lower ERA in the second half with a 3.52 mark versus Group B’s still ugly 4.73 mark. While Group A still outperformed Group B, the gap between the two group’s ERAs narrowed significantly. And once again, Group B did post better underlying skills as their SIERA finished .20 lower, a slightly larger difference than over the 1st half.

So the 57% of you that voted for Group A to outperform Group B in ERA in the 2nd half were correct. The 43.5% of you that voted that Group A’s ERA would fall between 3.50 and 3.99 were also correct! So far, the majority has gone 2 for 2. Finally, only 5.5% of you voted that Group B’s ERA would land between 4.50 and 4.99. Yup, this group was far worse than expected!

Yet again, Group B posted a higher strikeout rate, but a massively higher HR/FB rate and BABIP imploded their ERA.

From the overperformers group, both Tony Gonsolin and Alek Manoah continued to laugh at the prospect of regression. This may very well cause owners to severely overvalue the two in drafts next year, especially Manoah given his former top prospect status. With a meaningful drop in strikeout rate and decline in SwStk%, his underlying skills were a bit disappointing, but another suppressed BABIP and HR/FB rate, plus an inflated LOB% saved him. He’s now done that for two straight seasons, though it’s been just 308.1 innings. Does he truly own some better than average “luck” metric skills? It’s far too early to tell, but it’s hard to believe a .245 career BABIP is anywhere close to his true talent level.

Michael Wacha, Marco Gonzales, Taijuan Walker, and Joe Ryan all fell hard in the 2nd half, with the middle two experiencing so much regression that they became free agent fodder in most formats. Wacha and Ryan earned significantly less value during that time, but still held some streaming appeal depending on your league format.

Moving on to the underperformers group, incredible only Hunter Greene outperformed his SIERA during the 2nd half. His luck reaaaaaally turned around, as his HR/FB rate went from an absurd 19.5% to a microscopic 3.2%, while his strikeout rate surged. He’s going to be an extremely popular “sleeper” next year whose price is going to rise so much that he’s no longer a sleeper.

What happened to you Patrick Corbin? It’s getting harder and harder to remember how good he was back in 2018 and 2019. Gosh, this was certainly a year to forget for Lucas Giolito. I held onto him all season in two leagues, every week expecting this to be the one his luck turns around and he pitches like the guy I paid for. It never happened. His four-seam velocity dropped by 1.3 MPH, causing a decline in both SwStk% and strikeout rate. I’ll see how his velocity is during spring training before touting him as a strong rebound candidate you could potentially roster for cheap.

Injuries limited Trevor Rogers to just 107 innings, but man did his skills deteriorate as his changeup went from elite to just merely average. With stable velocity, I’m not sure yet what to think of him next year.

Both Alexes, Cobb and Wood, posted similar ERA, SIERA, and differential marks during the 1st half, but they diverged dramatically in the 2nd half. Cobb’s luck neutralized, while Wood’s failed to, thanks to a near doubling of his HR/FB rate, which helped bring down his LOB% to below 60%, the lowest of all Group B pitchers.





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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Joe WilkeyMember since 2016
2 years ago

The column I think is most instructive in this exercise is the IFFB%. The way this is presented on FanGraphs is a little misleading, since IFFB% is IFFB/FB, not IFFB/BBE. By multiplying FB%*IFFB%, you get IFFB/BBE. So group A has an IFFB/BBE of 5.2% where group B has an IFFB/BBE of 2.9%.

This has an effect on so many of the semi-mistitled “luck” statistics. It reduces BABIP (pop-ups are almost always outs), it reduces HR/FB (pop-ups are never HR, but always FB), and it increases LOB% (lower BABIP and lower HR/FB yield higher strand rates, all other things being equal).

If you look at the top 10 starters in IFFB/BBE from 2022 (min. 80 IP in starts) compared to the bottom 10, here’s the stats:

Top 10: 18.8% LD%, 33.8% GB%, 47.4% FB%, 15.4% IFFB%, .253 BABIP, 9.8% HR/FB, 3.24 ERA, 3.73 SIERA, 78.3% LOB%, 24.4% K%, 6.1% BB%
Bottom 10: 19.8% LD%, 51.4% GB%, 28.8% FB%, 5.5% IFFB%, .306 BABIP, 14.5% HR/FB%, 4.30 ERA, 3.83 SIERA, 71.0% LOB%, 22.1% K%, 8.4% BB%

So the high pop-up guys have over a full run of ERA advantage despite a 0.10 SIERA advantage. You’ll notice a big difference in HR/FB, LOB, and BABIP as well. Without doing a whole bunch of research, I would further be willing to bet that the high pop-up guys also induce more “can of corn” fly balls as well.

Fwiw, top 10 group is Joe Ryan, Justin Verlander Cristian Javier, Tyler Wells, Corey Kluber, Mike Minor, Triston McKenzie, Jake Odorizzi, Max Scherzer, and Julio Urias. Bottom 10 group is Alex Cobb, Framber Valdez, Dane Dunning, Logan Webb, Aaron Ashby, Kris Bubic, Lucas Giolito, Yusei Kikuchi, Austin Gomber, and Paolo Espino.

Last edited 2 years ago by Joe Wilkey