The Orioles Rotation According To SIERA

Baseball Prospectus came out with another pitching metric recently, so I thought I’d play with the shiny new toy by looking at how the Orioles starting pitchers did last year. First a little bit on what SIERA – Skill-Interactive Earned Run Average – does:

It uses plate appearances against, strike-outs, walks, groundballs, flyballs, and pop-ups – as well as the interrelationships between those stats – and “accomplishes the following:

  1. Allows for the fact that a high ground-ball rate is more useful to pitchers who walk more batters, due to the potential that double plays wipe away runners.
  2. Allows for the fact that a low fly-ball rate (and therefore, a low HR rate) is less useful to pitchers who strike out a lot of batters (e.g. Johan Santana’s FIP tends to be higher than his ERA because the former treats all HR the same, even though Santana’s skill set portends this bombs allowed will usually be solo shots).
  3. Allows for the fact that adding strikeouts is more useful when you don’t strike out many guys to begin with, since more runners get stranded.
  4. Allows for the fact that adding ground balls is more useful when you already allow a lot of ground balls because there are frequently runners on first.
  5. Corrects for the fact that QERA used GB/BIP instead of GB/PA (e.g. Joel Pineiro is all contact, so increasing his ground-ball rate means more ground balls than if Oliver Perez had done it, given he’s not a high contact guy).
  6. Corrects for the fact that FIP and xFIP use IP as a denominator which means that luck on balls in play changes one’s FIP.”

The equation for SIERA is pretty long and complicated (and uses terms that are gotten by running various regressions), so I’m hoping there’s real value added to make it worth using consistently. In any case, I thought it would be interesting to give it a whirl (for 2009 stats):

[Edit: Updated with corrected SIERA formula – I think.]

PA K BB GB FB PU SIERA
Kevin Millwood 850 123 71 269 245 26 4.77
Jeremy Guthrie 874 110 60 239 320 45 5.06
Brad Bergesen 519 65 32 206 133 8 4.58
Brian Matusz 196 38 14 44 67 8 4.08
Chris Tillman 285 39 24 81 99 5 5.01
D. Hernandez 462 68 46 99 182 24 5.32
Jason Berken 560 66 44 272 263 10 5.13

Nothing too surprising, I think, except for maybe Bergesen’s less than stellar mark*.

* I asked Eric Seidman, who is one of the guys who created SIERA, and he said that the formula has been updated to incorporate some corrected park factors, and that using the FanGraphs base data (which I did) might give slightly different results than the Retrosheet data they use. He ran Bergy’s numbers for me and got 4.53, which makes some more sense. The SIERA values here for everyone else should still be quite close to what the updated formula would give though.

Here’s how the new metric compares with some other ones:

SIERA ERA tERA FIP xFIP
Kevin Millwood 4.77 3.67 5.16 4.80 4.78
Jeremy Guthrie 5.06 5.04 5.27 5.31 5.22
Brad Bergesen 4.58 3.49 4.25 4.10 4.42
Brian Matusz 4.08 4.63 4.35 4.08 4.38
Chris Tillman 5.01 5.40 6.13 6.10 5.18
David Hernandez 5.32 5.42 6.33 6.61 5.60
Jason Berken 5.13 6.54 6.17 5.31 5.10

Everything’s pretty close (to xFIP, mostly). SIERA likes Guthrie, Matusz, Tillman, and Hernandez  more than xFIP does – perhaps due to their flyball tendencies. It doesn’t like Bergesen quite as much.

I’m looking forward to seeing what else BP has to say about SIERA – and I’m sure there are going to be some pitchers for whom it works a fair bit better than other metrics – but for now I’m probably going to stick with FIP and xFIP.