Poll 2021: 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 were the aggregate averages of the two groups through the pre-all-star break period. Remember you were voting solely on ERA. 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 19.5% 44.5% 36.0% 11.0% 9.6% 0.245 82.4% 24.6% 7.5% 2.44 3.95 -1.52
B 22.8% 40.9% 36.3% 7.3% 15.7% 0.334 64.8% 22.5% 7.5% 5.82 4.23 1.59
League Average* 21.2% 43.3% 35.5% 9.6% 14.1% 0.288 72.8% 23.2% 8.0% 4.19 4.20 -0.01
*Only starters are included

The poll results were surprising. In past years if I recall correctly, the group chosen for “Which Group Posts a Lower 2nd Half ERA?” was actually a pretty even split, sometimes with the Group B underperformers leading. This time, a whopping 84% of you voted that the Group A overperformers would post a lower second half ERA. Nearly 50% of you voted that Group A’s second half ERA would fall between 3.50 and 3.99, while about 34% voted for 3.00 to 3.49. Meanwhile, about 56% of you voted that Group B’s second half ERA fall settle between 4.00 and 4.49 and another 22% of you thought the group ERA would fall within the 4.50 and 4.99 range. It’s clear that despite similar aggregate SIERA marks over the first half, you still felt Group A was comprised of significantly better pitchers, no matter what SIERA suggested.

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
Kyle Gibson 18.3% 52.7% 29.0% 8.6% 12.9% 0.303 63.6% 19.3% 9.5% 5.51 4.65 0.86
Lance Lynn 16.9% 42.6% 40.4% 10.8% 12.2% 0.287 75.3% 26.0% 5.1% 3.66 3.64 0.02
Kevin Gausman 28.9% 39.0% 32.1% 10.0% 17.1% 0.352 72.7% 27.7% 5.9% 4.42 3.51 0.91
Taijuan Walker 8.2% 45.1% 46.7% 3.3% 22.0% 0.242 58.3% 18.6% 9.1% 7.13 4.99 2.14
John Means 18.1% 34.9% 47.1% 14.3% 15.2% 0.277 68.8% 20.2% 4.0% 4.88 4.35 0.53
Kwang-hyun Kim 17.0% 47.2% 35.8% 5.3% 15.8% 0.252 75.1% 14.5% 9.7% 4.19 5.34 -1.15
Wade Miley 26.1% 44.5% 29.4% 12.9% 17.7% 0.332 80.6% 16.7% 8.2% 4.19 4.93 -0.75
Lance McCullers Jr. 17.2% 60.3% 22.5% 2.1% 14.9% 0.284 76.7% 27.7% 10.1% 3.38 3.69 -0.32
Walker Buehler 21.0% 48.1% 30.9% 16.0% 8.0% 0.267 78.1% 26.3% 7.0% 2.60 3.68 -1.08
Anthony DeSclafani 21.2% 42.4% 36.4% 10.4% 11.9% 0.307 74.5% 20.9% 5.1% 4.03 4.19 -0.16
Group Average 19.5% 45.7% 34.8% 9.9% 14.9% 0.293 72.5% 22.4% 7.3% 4.34 4.21 0.13
League Average* 20.9% 42.1% 37.1% 9.0% 14.3% 0.293 72.2% 21.8% 7.6% 4.52 4.37 0.15
*Only starters are included

Group B – The SIERA Underperformers
Name LD% GB% FB% IFFB% HR/FB BABIP LOB% K% BB% ERA SIERA Diff
Matt Harvey 15.8% 44.2% 40.0% 9.1% 10.6% 0.280 73.0% 15.7% 5.1% 4.18 4.83 -0.65
Eduardo Rodriguez 22.7% 43.6% 33.7% 16.4% 8.2% 0.365 74.2% 27.9% 9.0% 3.71 3.82 -0.11
Andrew Heaney 18.5% 35.1% 46.4% 11.4% 21.4% 0.270 66.7% 24.6% 6.6% 6.49 3.98 2.51
Chris Paddack 23.0% 39.0% 38.0% 2.6% 7.9% 0.263 62.5% 14.5% 3.2% 4.31 4.83 -0.52
Mike Minor 19.5% 38.4% 42.1% 6.0% 13.4% 0.261 74.9% 21.2% 3.7% 3.78 4.09 -0.31
Jorge Lopez 14.5% 56.4% 29.1% 2.9% 20.6% 0.318 70.1% 18.2% 10.6% 6.32 4.79 1.53
Justus Sheffield 12.5% 54.2% 33.3% 0.0% 0.0% 0.375 47.1% 11.1% 22.2% 10.80 7.51 3.29
Jake Arrieta 28.6% 48.4% 23.1% 9.5% 33.3% 0.432 55.3% 13.9% 6.6% 10.73 4.85 5.88
Aaron Nola 15.7% 41.2% 43.1% 12.5% 12.5% 0.277 59.8% 30.1% 5.0% 4.76 3.21 1.55
J.A. Happ 21.2% 35.8% 42.9% 4.1% 13.4% 0.309 69.6% 17.9% 7.5% 5.66 4.94 0.72
Group Average 19.4% 41.8% 38.8% 8.5% 14.0% 0.305 67.4% 21.6% 6.7% 5.25 4.29 0.96
League Average* 20.9% 42.1% 37.1% 9.0% 14.3% 0.293 72.2% 21.8% 7.6% 4.52 4.37 0.15
*Only starters are included

Group Averages Comparison
Group LD% GB% FB% IFFB% HR/FB BABIP LOB% K% BB% ERA SIERA Diff
A 19.5% 45.7% 34.8% 9.9% 14.9% 0.293 72.5% 22.4% 7.3% 4.34 4.21 0.13
B 19.4% 41.8% 38.8% 8.5% 14.0% 0.305 67.4% 21.6% 6.7% 5.25 4.29 0.96
League Average* 20.9% 42.1% 37.1% 9.0% 14.3% 0.293 72.2% 21.8% 7.6% 4.52 4.37 0.15
*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 4.34 mark versus Group B’s ugly 5.25 mark. While Group A outperformed Group B, the gap between the two group’s ERAs narrowed significantly. Amazingly, only 9% of you guessed correctly that Group A’s second half ERA would fall into the 4.00-4.49 range. Yet, that was a higher percentage of readers than the measly 3% of you that thought Group B’s ERA would end up at 5.00 or above in the second half! Even when given 0.49 ERA ranges, we still aren’t very good at predicting ERA marks.

The biggest eyeopener is that after posting a sterling 2.44 ERA in the first half, the SIERA-outperforming Group A’s ERA ballooned to 4.34 in the second half. Not only did it rise nearly two full runs, while its SIERA only grew by 0.26 runs, but suddenly the group underperformed its SIERA. The group went from overperforming its SIERA by an incredible 1.52 runs to underperforming it by 0.13 runs. So much for those “generates soft contact”, “keeps hitters off balance”, “bears down with runners on base”, etc etc narratives. The magic only lasted over a half a season.

We find that Group A’s HR/FB rate, which massively beat the league average over the first half, shot up to above the league average in the second half. Meanwhile, its BABIP, which was microscopic in the first half, surged to match the league average in the second half. Finally, the group’s LOB%, which was above 80% in the first half and well above the league average, fell to barely above league average in the second half. Looking at the group’s overall skills and SIERA, this group was almost perfectly league average over the second half after their superhuman first half.

The pendulum swung violently in the other direction for Kyle Gibson, Kevin Gausman, and Taijuan Walker, who went from big overperformers to big underperformers. That’s just what happens when we’re analyzing small sample sizes. At least Gausman still delivered strikeouts, whereas Gibson and Walker posted strikeouts rates below 20%, which means they weren’t doing anything positive for fantasy owners.

Unlike Group A whose metrics fully reverted back to league average, Group B’s did not. On the positive side, its HR/FB rate improved and actually finished better than both Group A and the league average in the second half. But its BABIP remained well above the league average, despite a big improvement from the first half. Furthermore, its LOB% remained below 70%, and below Group A and the league average, but again, represented improvement from the first half. Overall, the group’s skills and SIERA were nearly identical to the group’s first half and were barely worse than Group A.

Andrew Heaney was one of my worst nightmares this year, as home runs torpedoed his ratios, despite strong underlying skills. Somehow, Aaron Nola’s ERA rose in the second half as his SIERA-ERA gap grew even wider. He’s going to be one of the most obvious rebound candidates in 2022.





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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Jolly Good ShowMike Podhorzer Recent comment authors
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Jolly Good Show
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Jolly Good Show

Obviously small sample sizes are the caveat here, but what I find interesting is that, when you look at the groups, the underlying stats are very similar to each other. The real differences being BABIP and LOB%. One group was league average and the other was worse. Could these two stats be the harbinger of death when it comes to ERA? It just goes to show how random some stats can be, and how difficult it is to predict ERA as a result.

Perhaps there are other stats which would tell us more of the story, like opposition win% and park factors.