Poll 2017: 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 underperformers versus the overperformers 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 population group consisted of 114 starters with at least 70 innings pitched, which included some that are no longer in a rotation and/or are injured. I have decided to only include in the two groups those who will remain a starter and whose return from the DL is not up in the air. 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% | GB% | FB% | IFFB% | BABIP | LOB% | HR/FB | ERA | SIERA | ERA-SIERA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Andrew Cashner | 11.6% | 10.4% | 19.9% | 49.8% | 30.3% | 3.7% | 0.282 | 74.8% | 7.4% | 3.54 | 5.70 | -2.16 |
| Jason Vargas | 18.2% | 5.8% | 18.9% | 37.4% | 43.7% | 9.4% | 0.276 | 84.7% | 7.9% | 2.62 | 4.63 | -2.01 |
| Ervin Santana | 18.8% | 8.3% | 15.2% | 44.3% | 40.5% | 12.9% | 0.217 | 83.6% | 12.9% | 2.99 | 4.75 | -1.76 |
| Gio Gonzalez | 23.5% | 10.5% | 19.5% | 44.1% | 36.4% | 11.1% | 0.259 | 85.0% | 13.0% | 2.86 | 4.45 | -1.59 |
| Jose Urena | 15.9% | 8.2% | 18.3% | 39.5% | 42.2% | 10.8% | 0.245 | 78.2% | 10.8% | 3.54 | 5.10 | -1.56 |
| Brandon McCarthy | 18.5% | 7.0% | 22.8% | 43.4% | 33.8% | 22.1% | 0.272 | 72.3% | 5.2% | 3.12 | 4.57 | -1.45 |
| Dallas Keuchel | 24.4% | 6.4% | 14.2% | 67.4% | 18.4% | 5.7% | 0.222 | 88.7% | 17.1% | 1.67 | 3.11 | -1.44 |
| Ivan Nova | 13.9% | 3.1% | 22.8% | 47.9% | 29.3% | 8.0% | 0.267 | 79.1% | 13.4% | 3.21 | 4.52 | -1.31 |
| Chase Anderson | 23.4% | 7.4% | 18.8% | 38.1% | 43.1% | 10.7% | 0.272 | 78.7% | 7.8% | 2.89 | 4.15 | -1.26 |
| Kyle Freeland | 14.0% | 8.8% | 17.1% | 55.0% | 27.9% | 12.6% | 0.287 | 77.9% | 12.6% | 3.77 | 5.03 | -1.26 |
| Group Average | 18.0% | 7.6% | 18.8% | 46.3% | 35.0% | 10.7% | 0.260 | 80.5% | 11.0% | 3.04 | 4.62 | -1.58 |
| League Average (All Starters) | 21.6% | 8.6% | 20.2% | 44.3% | 35.5% | 9.5% | 0.297 | 72.5% | 13.7% | 4.35 | 4.27 | 0.08 |
| Name | K% | BB% | LD% | GB% | FB% | IFFB% | BABIP | LOB% | HR/FB | ERA | SIERA | ERA-SIERA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ubaldo Jimenez | 19.3% | 11.2% | 19.0% | 44.7% | 36.4% | 9.8% | 0.272 | 64.1% | 20.7% | 6.67 | 4.96 | 1.71 |
| Masahiro Tanaka | 23.2% | 6.1% | 17.7% | 48.9% | 33.4% | 12.7% | 0.316 | 71.2% | 22.5% | 5.47 | 3.86 | 1.61 |
| Josh Tomlin | 17.5% | 2.7% | 24.7% | 39.4% | 35.9% | 5.4% | 0.347 | 64.8% | 15.2% | 5.90 | 4.31 | 1.59 |
| Trevor Bauer | 25.8% | 8.5% | 20.7% | 47.3% | 32.0% | 9.8% | 0.336 | 67.9% | 17.1% | 5.24 | 3.88 | 1.36 |
| Jeff Samardzija | 26.2% | 2.9% | 24.7% | 43.2% | 32.1% | 8.3% | 0.323 | 67.1% | 16.7% | 4.58 | 3.26 | 1.32 |
| Matt Moore | 17.9% | 9.0% | 20.8% | 37.3% | 41.9% | 5.8% | 0.347 | 66.2% | 10.9% | 6.04 | 5.01 | 1.03 |
| Kyle Gibson | 13.6% | 10.1% | 22.3% | 50.7% | 27.0% | 2.6% | 0.335 | 68.8% | 21.1% | 6.31 | 5.32 | 0.99 |
| Marco Estrada | 24.9% | 8.8% | 20.9% | 32.1% | 47.0% | 11.9% | 0.325 | 72.0% | 12.6% | 5.17 | 4.22 | 0.95 |
| Adam Wainwright | 20.7% | 7.7% | 24.7% | 47.7% | 27.7% | 12.0% | 0.347 | 67.4% | 12.0% | 5.20 | 4.25 | 0.95 |
| Kevin Gausman | 18.4% | 9.6% | 23.8% | 41.4% | 34.8% | 10.8% | 0.371 | 70.6% | 12.6% | 5.85 | 4.93 | 0.92 |
| Group Average | 20.9% | 7.6% | 22.0% | 43.1% | 34.9% | 9.0% | 0.333 | 68.1% | 16.0% | 5.59 | 4.35 | 1.24 |
| League Average (All Starters) | 21.6% | 8.6% | 20.2% | 44.3% | 35.5% | 9.5% | 0.297 | 72.5% | 13.7% | 4.35 | 4.27 | 0.08 |
I would have liked to include additional metrics for comparison, namely Hard%, but there’s simply not enough room to display every relevant one.
It’s quite interesting that Group B has actually posted slightly stronger skills (lower SIERA) than Group A, with a significantly higher strikeout rate. The most notable difference comes in the line drive rate, which we know highly correlates with BABIP. Even though there’s limited consistency within the stat, it’s an important backward-looking indicator of performance. So the gap there goes a long way to explaining the ridiculous BABIP difference of .73 points.
In addition to the difference in LD%, Group A has been more adept at inducing pop-ups, another BABIP suppresser. Naturally, all those extra hits, along with the much higher HR/FB rate, has pushed down Group B’s LOB%.
So let’s get to the poll questions. I will close the poll before games start up again.
Mike Podhorzer is the founder of ProjectingX IQ, an advanced fantasy baseball analytics platform that transforms projection data and in-season performance signals into actionable intelligence. He 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.
If the ball stays live, I would not be surprised to see the first group perform better since they have a higher GB%. It’s a tough time to be a flyball pitcher.
Mike, if you’re completely bored at some point, I’d be interested in the average HR/9 for each group.
My exact first reaction. Lotta GB% upside in that first group.
The two groups have essentially the same FB% though, so I don’t think that makes sense. The difference is B allowed more liners instead of grounders, which should have no effect on homers allowed. Now maybe you can argue that B’s liners will become flies in the second half, and some might, but also maybe A’s grounders become flies or liners as well since their LD% is below league average.
Not sure if this is the point of the exercise, but it’s easier to see guys in Group B like Ubaldo and Gibson pulled from the rotation due to their first halves, while similar guys in Group A might get longer leashes due to their results, giving an advantage to Group B as a whole.
Group A stays ahead of B. B has a couple great potential values but dragged down by a few horrid names
Group B has posted better skills, so is there specific reasons you feel the group will continue to allow an inordinate rate of hits on balls in play and homers on fly balls? Similarly, is there a reason you feel A has the skills to continue suppressing hits on balls in play and homers on fly balls. That’s the real discussion here, not whether you think A’s group of pitchers are better than B’s.
I’ll uh…go ahead and take the group without Ubaldo Jimenez. Thanks.
advanced metrics be damned. the first half results pass the sniff test. ride or die with the overperformers!
I’d be interested to see that comparison for quality of contact. If I had to venture a guess, I’d say Group A proves to be the stronger of the two. This could explain some of the overachievers success.
How would you measure quality of contact and is it a repeatable skill in-season?
Well it’s late so I definitely might be mistaken but I thought there was exit velocity data available for pitchers that you could also further break down by examining the EV for a pitchers different batted ball types.
Yes, there is def EV available for pitchers, but I can’t remember what the correlation to BABIP is. I think it was fairly low.
I see, probably a waste of time then. I was just curious since my initial reaction to the two groups was that Group A seems to, generally speaking, have better command than group B. The guys in A like Kuechel, McCarthy, Nova, Anderson, and Vargas seem like they would be good contact managers this season. While Bauer, Wainwright, Moore, Tanaka and ubaldo seem like guys who might be struggling with command due to control issues or injury/age related decline.
But of course command is a hard thing to nail down. I guess when I was the two groups with basically the same walk rates I immediately though of control vs command. Their control might appear equal but perhaps command might be a place with significant diversion.
How are you measuring command? And your perception is most certainly clouded by the respective pitchers’ BABIPs.
Ahh, read the rest of your comment. Well sure, we can speculate the reasons for it, but it’s very difficult to prove why one group dramatically overperforms, while the other underperforms, outside of defensive support and home park.
Well thanks for the responses. And I apologize for the typos earlier, I was commenting from my phone during my lunch break.
Group B should have its worst guys heading to the pen and/or getting sent down/released, which will help their ERA.
Group A has earned a lot longer leash and should still pile up innings no matter how bad they are.