During the all-star break, I decided to undergo a little experiment. I took two groups of 10 starting pitchers comprised of those whose ERAs outperformed and underperformed their SIERA marks by the largest margins. There were 437 of you who answered the question “Which Group Posts a Lower ERA RoS?” and 61.1% of you voted for Group A, the SIERA outperformers. Despite this group actually posting a higher SIERA than Group B, you felt that the magic would continue. Let’s find out the results and if the majority was correct.
I’ll first start by reviewing how the SIERA outperformers did in both halves:
Group A – The SIERA Outperformers, 1st Half
|Jorge de la Rosa||109.1||16.6%||8.5%||0.294||76.4%||6.7%||3.21||4.32||-1.11|
Group A – The SIERA Outperformers, 2nd Half
|Jorge de la Rosa||58.1||14.2%||9.0%||0.320||73.9%||9.3%||4.01||4.67||-0.66|
Remember that magic this group benefited from that resulted in the trio of a lucky BABIP, LOB% and HR/FB ratio in the first half? Yeah, that good fortune disappeared. Their BABIP and HR/FB marks jumped right back up to the second half league average, but they did sustain an above average LOB%, even though it dropped dramatically from the first half. Over this relatively small sample of 10 pitchers, in aggregate, they do not actually have any special abilities. As a group, their ERA was essentially the same as their SIERA in the second half, a far cry from the 1.40 runs they outperformed their SIERA by in the first half.
I asked another question in my original post, and that was “Which Range Will Group A’s ERA Fall Into RoS?”. The 3.50-3.74 range garnered the highest percentage of votes at 24.4%, while the correct range of 3.75-3.99 earned the third highest percentage at 21.4%. It seems pretty clear that everyone assumed regression, but not as much as actually occurred.
Interestingly, this group’s strikeout and walk rates improved from the first half, which pushed its SIERA below 4.00. In the comments, Sky Kalkman, man of many saber-friendly Internet sites, shared his theory that perhaps as BABIP regresses like we saw in this group, their peripherals will improve. That is exactly what happened. It’s still too small a sample to conclude anything, but this theory has me intrigued now.
Group B – The SIERA Underperformers, 1st Half
Group B – The SIERA Underperformers, 2nd Half
This group was terrible in the first half, hampered by a high BABIP and HR/FB ratio and an inability to strand runners. But, most of those problems suddenly disappeared in the second half and the group went from underperforming their SIERA marks by 1.29 runs to just 0.22 runs. Yes, they still underperformed, but 0.22 is much less significant and within a reasonable error range. In fact, the group actually posted a lower BABIP than Group A in the second half! The other luck metrics weren’t much worse than Group A either.
The leading vote-getter to the question of “Which Range Will Group B’s ERA Fall Into RoS?” was 4.00-4.24, with 36.2% of the vote. This proved to be a bit too optimistic and surprisingly only 9.8% of you guessed the correct range of 4.25-4.49, which garnered the fourth highest percentage of votes.
For a second time, we observe a change in peripherals, this time a decline, as the strikeout rate dropped and walk rate increased. This was on the heels of a BABIP decline, once again giving some early credence to Sky’s theory mentioned above. Ultimately, the worse skills led to a higher SIERA in the second half versus the first half.
Now let’s directly compare the average lines of each group in the second half:
So the 267 of you who voted that Group A, the SIERA outperformers, would post a lower rest of season ERA, give yourself a pat on the back, as you were correct. But I bet it would still surprise many to learn how much the gap narrowed between the two groups. Group A posted the better peripherals and SIERA, so they should have posted a better ERA. But the story is that they essentially matched their SIERA after significantly outperforming it in the first half. It’s always tempting to fish for an explanation and try to justify the outperformance with unique visual observations and scouting type analysis. Nobody wants to shrug their shoulders and say they don’t know. But I’m here to tell you that it’s okay, you are allowed to simply explain it off as luck over a small sample size.
In the comments of the original post, Wobatus was kind enough to figure out each group’s rest of season ZiPS projections. He calculated Group A’s as 4.16 and Group B’s as 4.12. So, essentially the same. With that context, we do note that Group A slightly outperformed their RoS projection, while Group B underperformed it. However, we don’t know what peripherals the ZiPS projections were projecting, so we’re missing crucial information necessary for a complete analysis.
Obviously over just two halves of a season, any pitcher could outperform or underperform their SIERA marks like we saw with Bartolo Colon and Jeremy Hellickson. But knowing ahead of time which pitchers are going to do that is a fool’s errand. If you know that as a group, the outperformers will regress, while the underperformers will improve, then you have to try your hardest to ignore ERA and rely on the underlying skills and SIERA marks.
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