Poll 2023: 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 | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A | 20.0% | 43.2% | 36.8% | 10.5% | 9.0% | 0.273 | 80.7% | 21.0% | 8.0% | 2.96 | 4.43 | -1.47 |
| B | 20.6% | 38.7% | 40.8% | 10.6% | 15.8% | 0.312 | 63.5% | 19.5% | 8.3% | 6.37 | 4.74 | 1.63 |
| League Average* | 20.4% | 41.7% | 37.9% | 9.4% | 12.9% | 0.297 | 71.8% | 22.0% | 7.9% | 4.42 | 4.34 | 0.08 |
The poll results were as follows:
So a whopping 84.45% of you believed the SIERA overperformers, Group A, would post a lower 2nd half ERA. That has to be the highest percentage of votes any group has garnered since I started doing this poll! I’m rather shocked the gap was that large as both groups displayed weak skills through the first half, with SIERA marks in the mid-to-high 4.00 range.
The majority of you voted that Group A would finish with a 2nd half ERA at least a half run above their 1st half ERA, and perhaps even a full run and a half higher. At the top end of the second most popular vote-getting range would imply an ERA that matches the group’s first 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 surprises me to find that the 4.00-4.49 ERA range was the most popular for Group B, as I would have expected more pessimistic guesses based on how many voted that Group A would post a lower ERA. Shockingly, that range was also below the group’s first half SIERA, so you thought this group’s skills would actually improve slightly, in addition to their luck turning around!
Now on to the second half results.
| Name | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dane Dunning | 19.7% | 47.0% | 33.3% | 7.7% | 17.9% | 0.312 | 77.2% | 23.1% | 8.8% | 4.69 | 4.28 | 0.40 |
| Michael Wacha | 25.7% | 35.7% | 38.6% | 9.3% | 13.0% | 0.293 | 77.9% | 24.3% | 8.6% | 3.88 | 4.26 | -0.37 |
| Shane McClanahan | 14.0% | 47.4% | 38.6% | 13.6% | 18.2% | 0.321 | 48.9% | 25.0% | 3.8% | 7.11 | 3.57 | 3.53 |
| Tommy Henry | 27.1% | 31.3% | 41.7% | 5.0% | 0.0% | 0.354 | 56.0% | 18.8% | 8.7% | 6.14 | 5.00 | 1.14 |
| Bryce Elder | 23.0% | 42.9% | 34.1% | 8.1% | 12.2% | 0.276 | 64.4% | 16.1% | 9.7% | 5.11 | 5.30 | -0.19 |
| Josiah Gray | 22.1% | 29.7% | 48.3% | 15.7% | 9.6% | 0.285 | 75.8% | 19.5% | 12.6% | 4.76 | 5.51 | -0.75 |
| Bailey Ober | 15.2% | 37.4% | 47.4% | 8.6% | 17.3% | 0.306 | 77.1% | 26.2% | 4.3% | 4.52 | 3.64 | 0.89 |
| Justin Steele | 26.5% | 50.0% | 23.5% | 7.4% | 18.5% | 0.353 | 70.2% | 27.3% | 4.9% | 3.62 | 3.23 | 0.39 |
| Jon Gray | 23.5% | 40.6% | 35.8% | 6.0% | 16.4% | 0.350 | 72.1% | 23.8% | 8.5% | 5.32 | 4.27 | 1.05 |
| Sonny Gray | 22.5% | 45.9% | 31.6% | 5.5% | 6.8% | 0.278 | 75.6% | 24.5% | 4.8% | 2.67 | 3.67 | -1.00 |
| Group Average | 22.2% | 41.9% | 35.9% | 8.7% | 13.5% | 0.309 | 72.2% | 23.1% | 7.5% | 4.40 | 4.21 | 0.19 |
| League Average* | 19.8% | 42.5% | 37.7% | 9.9% | 13.3% | 0.294 | 72.0% | 22.8% | 8.6% | 4.39 | 4.23 | 0.16 |
| Name | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lance Lynn | 19.7% | 31.7% | 48.6% | 10.7% | 18.2% | 0.250 | 73.3% | 18.2% | 8.5% | 5.36 | 5.15 | 0.20 |
| Luke Weaver | 29.7% | 33.5% | 36.7% | 17.2% | 20.7% | 0.322 | 73.5% | 22.4% | 7.5% | 5.54 | 4.28 | 1.26 |
| Joey Wentz | 15.8% | 36.0% | 48.2% | 10.9% | 16.4% | 0.343 | 64.0% | 19.8% | 9.9% | 7.15 | 4.82 | 2.32 |
| Connor Seabold | 22.7% | 34.8% | 42.4% | 7.1% | 10.7% | 0.453 | 50.3% | 16.1% | 6.9% | 11.12 | 4.81 | 6.31 |
| Jameson Taillon | 17.6% | 40.4% | 42.0% | 8.7% | 13.6% | 0.276 | 71.8% | 22.4% | 5.5% | 3.70 | 4.11 | -0.41 |
| Ken Waldichuk | 19.0% | 37.9% | 43.1% | 8.3% | 10.7% | 0.263 | 69.8% | 21.8% | 9.2% | 4.04 | 4.56 | -0.52 |
| Austin Gomber | 19.6% | 38.7% | 41.7% | 10.3% | 8.8% | 0.323 | 78.6% | 12.6% | 6.3% | 3.86 | 5.47 | -1.61 |
| Graham Ashcraft | 18.3% | 47.2% | 34.4% | 8.1% | 16.1% | 0.253 | 90.0% | 19.9% | 6.8% | 2.81 | 4.42 | -1.61 |
| Matthew Boyd | ||||||||||||
| Jordan Lyles | 18.1% | 34.6% | 47.3% | 12.2% | 15.4% | 0.253 | 60.5% | 15.2% | 4.5% | 6.11 | 5.06 | 1.05 |
| Group Average | 19.7% | 37.2% | 43.1% | 10.5% | 14.8% | 0.285 | 71.2% | 19.0% | 7.1% | 4.91 | 4.72 | 0.19 |
| League Average* | 19.8% | 42.5% | 37.7% | 9.9% | 13.3% | 0.294 | 72.0% | 22.8% | 8.6% | 4.39 | 4.23 | 0.16 |
| Group | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A | 22.2% | 41.9% | 35.9% | 8.7% | 13.5% | 0.309 | 72.2% | 23.1% | 7.5% | 4.40 | 4.21 | 0.19 |
| B | 19.7% | 37.2% | 43.1% | 10.5% | 14.8% | 0.285 | 71.2% | 19.0% | 7.1% | 4.91 | 4.72 | 0.19 |
| League Average* | 19.8% | 42.5% | 37.7% | 9.9% | 13.3% | 0.294 | 72.0% | 22.8% | 8.6% | 4.39 | 4.23 | 0.16 |
Let’s start with the answers to the poll questions. Group A did indeed post a lower ERA in the second half with a 4.40 mark versus Group B’s still ugly 4.91 mark. While Group A still outperformed Group B, the gap between the two group’s ERAs narrowed significantly. What’s interesting here is how much Group A regressed. They actually slightly underperformed their SIERA this time and posted an ERA barely better than their first half SIERA. Group B, on the other hand, clearly saw their luck turn around and they actually underperformed their SIERA by the exact degree that Group A did. So this year’s poll actually perfectly illustrates the power of luck regression, as both groups ended up performing very close to their SIERA marks over the second half.
Dane Dunning had a magical first half, but it was very obviously smoke and mirrors, and the low strikeout meant that his fantasy value was at risk of completely imploding. And that’s exactly what happened, even though he actually raised his strikeout rate quite meaningfully in the second half. Shane McClanahan’s injury likely hampered his underlying skills so that really hurt the group’s bottom line.
Bryce Elder was one of the first half’s biggest surprises. What wasn’t a surprise is his weak second half. Also with a low strikeout rate, the risk of continuing to start him each week was too high. I had been a Josiah Gray fan in the past, but I just couldn’t buy his first half given the drop in strikeout rat and terrible SIERA. Now I’m going to have to reassess whether he even has true breakout potential anymore.
Sonny Gray was really the only Group A pitcher who continued to significantly outperform his SIERA. He’s done it for most of his career, with a hiccup here and there, so he clearly has some skill not accounted for by the equation.
Welp, Lance Lynn was better in the second half, but his skills also collapsed. I was never a fan of his, but he was extremely tempting as a buy low. Glad I avoided following through. Graham Ashcraft was Group B’s biggest second half breakthrough who actually benefited fantasy teams. I really liked him after his spring training display, but sold him high early in the season when his strikeouts failed to show up and he was massively overperforming his SIERA…before he ultimately underperformed! His skills remained meh in the second half, but his luck turned the other way and it now looks like a second half breakout, but it really wasn’t. I would be interested if the strikeouts came, but not this current version. He may be a popular sleeper pick next year and end up overvalued.
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.
Thanks for the update. It’s always interesting to see how regression inevitably rears it’s head.