Archive for SIERA

In a fairly surprising turn of events, the Rockies demoted Jon Gray Saturday. Gray has arguably been baseball’s most enigmatic pitcher this year, posting a career-worst 5.77 ERA supported by career-best peripherals — e.g., a 13.4% swinging strike rate (SwStr%) underpinning a 28.9% strikeout rate (K%), and fielding independent metrics of 2.78 xFIP, 3.08 FIP, and 3.15 SIERA. Given our most basic sabermetric understandings of baseball, Gray should be a very good pitcher, even if he pitches half his starts at hitters’ paradise Coors Field.

I have written about how a common-breed Rockies pitcher’s peripherals might be penalized for calling Coors Field home (Gray inspired this bit of research as well). FIP metrics generally underestimate ERA by anywhere from 0.8 to 1.3 runs for home starts (compared to 0.0 to 0.2 runs for road starts), suggesting that Rockies pitchers may underperform (a) their FIPs by 0.35 runs or (b) their SIERAs by 0.65 runs — given error bars, maybe more.

Still, that doesn’t explain why Gray’s ERA is nearly 6 right now. I shed light on the ridiculousness of the move; his strand rate (LOB%) is suppressed and his batting average on balls in play (BABIP) is elevated, even compared to his uniquely bad baselines. I’m not sure there’s much more to it.

Nick Mariano of RotoBaller noted here that Gray’s fastball has been incredibly hittable since his debut and especially this year. Despite my thoughts on the inevitability of regression in Gray’s favor, I wanted to pursue Mariano’s train of thought a little further. Gray’s fastball is bad, but how bad? And why?

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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.

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Contributing to RotoGraphs’ Bullpen Reports last year brought me much joy: opening infinite Brooks Baseball player cards for sabermetric outcomes and watching glorious GIFs. Oh, Marcus Stroman’s Two-Seamer:

GIF: Marcus Stroman’s Two-Seam Fastball from last week’s outing. It may be our favorite of the year – http://t.co/vyJM8ltUir

— Pitcher List (@ThePitcherList) August 31, 2014

Mmm.

My last Bullpen Report from October looked at possible closers through outcomes and presented BABIP differentials (actual BABIP versus expected BABIP using Inside Edge data).

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Any day now, Zach Sanders (@zvsanders) will come out with his end-of-season FAVRz/Fantasy Rankings. Look out for them.

For this post, I will provide three sets of rankings using that same approach (summed up z-scores) for our end-of-season “Bullpen Report: Expected Fantasy Rankings”. The Bullpen Report team should follow up with role reports for each division in the coming weeks as well.

The first set of rankings you will find almost anywhere: on the fantasy sites that you use, via player-raters, etc. It’s the standard 5×5 fantasy value (Wins, ERA, WHIP, SO and Saves). The second grid will be for 6×6 leagues (addition of Holds). The last grid will be for 5×5 and 6×6 leagues, but instead of standard ERA and WHIP, we’ll look at rankings if you were to use expected ERA (via SIERA) and adjusted (adj)WHIP through BABIP differential: I will explain below.

1) 5×5 Rankings (Wins, ERA, WHIP, SO and Saves) – actual 5×5 value in column 4; expected 5×5 value in column 5:

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I wanted to highlight Pitcher over and under performers. Naturally, Mike Podhorzer has already done that and more concisely and eloquently than I ever could. In conjunction to what he did (ERA vs. SIERA within this year), I wanted to look at pitchers’ skills and luck relative to last year.

By “skills” I mean K%-BB%, combined Contact% and SwStr%, GB/FB and IFFB%. These columns are in green.

By “luck” I mean BABIP, LOB% and HR/FB. These are in some emotional shade of red.

I took all the 2014 vs. 2013 differentials and z-scored them. I summed them up and presented them under column 3 (“SKILL”) and column 4 (“LUCK”). In short, these are not their skill and luck scores for 2014, but their change relative to 2013…in relation to how much everyone else from this list changed (50 IP qualifier).

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