FanGraphs hosts a statistic for pitchers called ERA Minus FIP (“E-F”), which is as advertised. FIP being a (somewhat) adequate measure of pitcher over-/under-performance, one could look to E-F to identify pitchers who may, as they say, be due for regression. FIP’s correlation with ERA, however, is weaker than that of xFIP due to the former’s inability to account for the volatility inherent to home run-to-fly ball ratios (HR/FBs). To take it a step further, xFIP’s correlation with ERA is weaker than that of SIERA due to the former’s inability to account for a pitcher’s ground ball rate (GB%) and how it interacts with his strikeout and walk rates (K%, BB%).
Alas, I often use SIERA, rather than xFIP or FIP, to identify pitchers who may be ripe for regression. ERA Minus SIERA (“E-S,” henceforth) is not the be-all, end-all by any means, and I would never consider making a roster decision based exclusively on that metric. Player evaluation is a holistic endeavor, which you likely know yet I still intend to demonstrate. Three names stood out to me — four, if you include Luis Castillo, but I covered him a week and a half ago — as interesting E-S targets, but I came away from this feeling good about only one of them.
Marco Gonzales, SEA SP
Among qualified starting pitchers, Gonzales’ 2.05 E-S is 2nd-highest behind Castillo’s 2.75. Gonzales’ 5.19 ERA betrays his sparkling 3.14 SIERA, which ranks 16th-best thanks to superb 25.5% strikeout and 4.0% walk rates. (His xFIP, while an inferior metric, is even sexier at 2.72.) Gonzales seems to finally be meeting the expectations set for him as a former top Cardinals prospect in the. Yet something’s awry here — namely, the swinging strike rate (SwStr%).
One should not rely solely on a pitcher’s swinging strike rate as a leading indicator of his strikeout rate; there are always other factors at play. Yet SwStr% does explain most of the variance in K%, which makes it more permissible for this kind of logical leapfroggging. When it comes down to it, Gonzales’ lackluster 8.9% whiff rate greatly concerning. It tracks closely with both his prior Major League and Minor League performances, suggesting it’s the strikeout rate that’ll move toward the swinging strike rate — which, itself, suggests a strikeout rate closer to 18% — and not the other way around. He doesn’t have a single above-average pitch by whiff rate.
Ironically, Gonzales is due for some kind of regression, just maybe not strikeouts-related. That .400 batting average on balls in play (BABIP) shouldn’t last forever, although, now that I mention it, he does have a .362 BABIP through 112 MLB innings. He’s probably not that bad, but he doesn’t seem particularly adept at limiting contact. He’s just… not very good. In a best-case scenario, I envision his rest-of-season (or, more pessimistically, his end-of-season) SIERA ending up juat north of 4.00. And that’s the best case.
Chris Archer, TBR SP
Ah, the eternal Archer dilemma. After two years of relative suckage, Archer is back to… well, sucking worse. I made a Tweet that, to date, has aged poorly:
is it ok to believe chris archer's chances of posting a sub-4.00 ERA are much higher than everyone makes them out to be, or is that not in vogue
— Alex Chamberlain (@DolphHauldhagen) February 26, 2018
I still stand by what I said — I maintain a bad HR/FB rate ruined his 2016 season and an unlucky BABIP ruined 2017 — but with inflated everythings right now, it’s hard to see the light at the end of the tunnel. E-S is that light! Archer’s 1.71 E-S is 3rd-worst among qualified starting pitchers, and his 3.61 SIERA, while not elite, is very strong and falls in line with his career rate. Unlike Gonzales, Archer’s swinging strike rate, currently a career-best 14.1%, portends more strikeouts, aligning nicely with his 28.6% rate the last three years. Add four ticks to his strikeout rate and give him a league-average BABIP and strand rate (LOB%), and he’s every bit the ace he showed us before 2016.
Of course, those of you personally victimized by Archer are likely reluctant to believe in an Archer redux that’s actually still good. I guess it depends how much you buy his recent BABIP woes (I don’t; he has a career .298 BABIP). Or his current strand rate woes (I don’t; it’s a fairly volatile and unpredictable metric, one in which he has been league-average every year). Or his former HR/FB woes (which are valid, given the current league context, but HR/FB is the most unpredictable of the lot).
All told, Archer is controlling his walks as effectively as ever while showing off one of his best 8-game rolling strikeout rates. I rarely label anyone a “buy-low,” but Archer, with two-plus years of bad luck and superb peripherals, has the requisite baggage to be a nice buying opportunity from the owner who was probably reluctant to draft Archer to begin with. Just make sure to resolve your own cognitive dissonance first.
Jon Gray, COL SP
Ah, the eternal Rockies pitcher dilemma. Gray’s 1.43 E-S ranks 5th-worst; his solid 3.56 SIERA outstrips Archer’s. Gray has been all over the place during his relatively short MLB tenure. He seemed far less effective in 110 innings last year compared to 2016, yet he improved upon his ERA by almost a full run. Fortunately, his swinging strike rate has rebounded; the 12.2% rate fully validates his 24.4% strikeout rate. Unfortunately, he’s struggling to strand runners (an issue in every season except 2017) and limit his BABIP (an issue in every season except 2016).
Through 358.2 career MLB innings, Gray’s .329 BABIP and 68.9% strand rate, both well below average, confirm everything bad about Coors Field. While renowned for being a hitter’s park, it plays up non-home run hits way more than home runs, relatively speaking. (Check out the factor on triples for 2017.) The results manifest in the same painful home-away splits for Rockies pitchers every year:
The typical RotoGraphs reader is likely intimately familiar with the woes of Rockies pitchers and Coors Field. The environment makes them difficult to trust, no matter how effective they are. Worse: like most pitchers, Gray achieves better success in terms of baserunner-prevention peripherals when pitching at home. Calling Coors Field home effectively eradicates this advantage, which finally gets me to my point: you simply can’t trust SIERA for Rockies pitchers when half their starts are at home. From 2014 through 2017, Rockies pitchers at home under-performed their collective FIP by 0.79 runs, their xFIP by 1.13 runs, and their SIERA by 1.30 runs. (Conversely, FIP, xFIP, and SIERA missed the mark on the Rockies’ collective road ERA by 0.01, 0.09, and 0.24 runs, respectively.)
The divide between Gray’s ERA and SIERA — 1.43 runs — figures to narrow quite a bit. But his career E-S stands at 0.74. At this point, it’s the most realistic expectation for Gray’s capacity for regression this year. In other words, he can’t be reasonably expected to be anything more than a low-4.00 ERA pitcher overall, despite his evident talent.
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I realize this post did little to support the notion that SIERA is superior to xFIP or FIP. It’s important, however, to understand outliers — Gonzales and Gray, namely — to better understand where SIERA might break down as an analytical tool. I hope this exercise helps illuminate those instances.