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.
| 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 |
| 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 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 and three-time Tout Wars champion. He is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. Follow Mike on X@MikePodhorzer and contact him via email.
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!
2017 Results (group 2nd half ERAs nearly identical!) – https://www.fangraphs.com/fantasy/poll-2017-which-group-of-pitchers-performs-better-a-review/
2015 Results – https://www.fangraphs.com/fantasy/poll-2015-which-group-of-pitchers-performs-better-the-results/
2013 Results – https://www.fangraphs.com/fantasy/poll-which-group-of-pitchers-performs-better-the-results/
2013 and 2015 showed continued trends in the second half, but the gap was significantly less. Sadly, couldn’t find the result articles from 2014 and 2016.
Awesome! Thanks!
Care to summarize 2014 and 2016 results from memory?
Ha, I couldn’t possibly remember the results