Poll 2023: Which Group of Pitchers Performs Better?
Since 2013, I have polled you wonderful 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 see how everyone else thinks. Will the SIERA overperformers continue to outperform, perhaps due to continued strong defensive support and/or more pitcher friendly ballparks, or will the magic disappear? 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 98 pitchers who have thrown at least 70 innings. Group A is composed of the 10 largest SIERA overperformers, while Group B is composed of the 10 largest SIERA underperformers. Let’s compare.
Name | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Dane Dunning | 21.6% | 45.7% | 32.6% | 9.8% | 6.5% | 0.268 | 77.8% | 15.9% | 6.5% | 2.84 | 4.71 | -1.87 |
Michael Wacha | 20.6% | 33.7% | 45.7% | 11.7% | 7.2% | 0.251 | 80.9% | 21.3% | 7.3% | 2.84 | 4.49 | -1.65 |
Shane McClanahan | 20.2% | 43.1% | 36.7% | 6.6% | 12.1% | 0.264 | 88.0% | 26.0% | 9.8% | 2.53 | 4.11 | -1.58 |
Tommy Henry | 18.0% | 37.3% | 44.7% | 13.7% | 11.8% | 0.261 | 82.9% | 16.3% | 9.3% | 3.75 | 5.30 | -1.55 |
Bryce Elder | 18.6% | 54.8% | 26.6% | 7.2% | 12.0% | 0.273 | 81.5% | 18.4% | 7.8% | 2.97 | 4.39 | -1.42 |
Josiah Gray | 20.5% | 42.7% | 36.9% | 12.0% | 13.0% | 0.297 | 83.3% | 21.1% | 10.8% | 3.41 | 4.80 | -1.39 |
Bailey Ober | 16.8% | 31.8% | 51.4% | 10.6% | 7.1% | 0.254 | 80.8% | 24.6% | 5.6% | 2.61 | 4.00 | -1.39 |
Justin Steele | 17.3% | 48.8% | 33.8% | 9.1% | 4.5% | 0.286 | 75.7% | 22.0% | 5.2% | 2.56 | 3.91 | -1.35 |
Jon Gray | 23.5% | 39.6% | 36.9% | 11.5% | 11.5% | 0.258 | 78.7% | 20.1% | 8.0% | 3.29 | 4.60 | -1.31 |
Sonny Gray | 21.9% | 48.5% | 29.6% | 11.1% | 3.7% | 0.309 | 77.5% | 24.1% | 9.3% | 2.89 | 4.14 | -1.25 |
Group Average | 20.0% | 43.2% | 36.8% | 10.5% | 9.0% | 0.273 | 80.7% | 21.0% | 8.0% | 2.96 | 4.43 | -1.47 |
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 |
Name | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Lance Lynn | 22.2% | 39.1% | 38.7% | 13.6% | 20.0% | 0.328 | 64.1% | 27.9% | 8.1% | 6.03 | 3.73 | 2.30 |
Luke Weaver | 22.7% | 36.7% | 40.6% | 8.8% | 16.7% | 0.336 | 65.8% | 17.3% | 6.9% | 7.00 | 4.86 | 2.14 |
Joey Wentz | 19.8% | 37.9% | 42.2% | 9.2% | 16.3% | 0.323 | 63.4% | 20.0% | 9.4% | 6.78 | 4.80 | 1.98 |
Connor Seabold | 19.1% | 33.1% | 47.9% | 5.3% | 14.2% | 0.305 | 64.0% | 16.5% | 6.9% | 6.65 | 5.03 | 1.62 |
Jameson Taillon | 20.7% | 35.1% | 44.1% | 10.2% | 13.3% | 0.310 | 57.8% | 20.3% | 7.1% | 6.15 | 4.56 | 1.59 |
Ken Waldichuk | 20.7% | 41.4% | 37.9% | 8.1% | 17.4% | 0.352 | 69.5% | 19.7% | 12.8% | 6.63 | 5.14 | 1.49 |
Austin Gomber | 20.8% | 43.3% | 35.9% | 11.2% | 18.7% | 0.304 | 65.5% | 15.4% | 7.6% | 6.40 | 5.07 | 1.33 |
Graham Ashcraft | 23.5% | 48.1% | 28.4% | 14.5% | 17.1% | 0.318 | 66.2% | 16.4% | 9.4% | 6.28 | 5.00 | 1.28 |
Matthew Boyd | 17.9% | 38.8% | 43.3% | 10.3% | 12.6% | 0.302 | 62.0% | 24.1% | 8.3% | 5.45 | 4.19 | 1.26 |
Jordan Lyles | 17.7% | 32.6% | 49.7% | 13.6% | 13.0% | 0.256 | 53.1% | 16.7% | 7.3% | 6.42 | 5.17 | 1.25 |
Group Average | 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 |
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 |
My gosh, I can’t remember a time when the names on the overperformer list seemed so far superior to the names on the underperformer list! It makes for an obvious knee-jerk reaction to expect the overperformer group, A, to continue performing significantly better than the underperforming group, B. And heck, I can’t recall this massive of an ERA gap either, with Group A just below 3.00, and Group B well over 6.00! I can’t believe this many qualified pitchers have recorded an ERA this high.
But let’s ignore the names for now and examine the underlying skills. Group A has generated a higher GB% than B, which should offset the slightly lower LD% and result in fairly similar BABIP marks. This is especially true since both groups have near identical IFFB% marks. However, Group B seemingly should perhaps hold a lower BABIP given the higher FB%, and therefore, higher rate of pop-ups. We find out that that’s not the case, as the BABIP disparity is significant. With similar batted ball profiles, how much does team defense play a role or the ability to suppress hard contact? Or has it just been good fortune benefiting Group A and bad luck hindering Group B?
The hard contact theory gets another boost when we look at HR/FB rates. Group A’s is much better than league average, while B’s is above. There’s luck involved here too, and home park plays a role. Even if Group A has allowed softer contact, it doesn’t mean they will continue to. It’s no surprise then that given the gaps between the HR/FB rates and BABIP marks, Group A has stranded a far higher rate of runners than B. Again, this gap is far greater than I’ve seen in past annual polls!
Next, we move on to strikeout and walk rates. In the past, Group B would typically be on par with or actually better than Group A. This time, Group A has posted the higher strikeout rate and lower walk rate. We then shift over to SIERA and learn that Group A does indeed include the better crop of pitchers. Last year, Group B’s SIERA was lower, and both groups were more highly skilled, with SIERA marks hovering around 4.00. It’s been an odd year indeed.
Before I ask you to vote, I think the better question is what to do with the pitchers in Group A if you own any of them on your fantasy team. The majority of the names look like clear sell high candidates, some of which should net you a real nice return. Only one of Group B’s pitchers has posted a sub-4.00 SIERA, so even if these pitchers do enjoy better results, they still might not earn any fantasy value. I’ve never been a fan of Lance Lynn, but if there was ever a time to target him in a trade, now is it.
So which group performs better over the rest of the season? Let’s get to the poll questions. 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.
What is it about pitchers with the last name “Gray”? Weird.
(And the correlation of the name with SIERA overperformance!)