Tuesday, February 9, 2010

I Can't Decide Whether to Call it SEE-air-UH or SEER-uh...

Today, Tango mentioned Matt Swartz and Eric Seidman's latest pitching metric, what they call Skill-Interactive Earned Run Average (SIERA). Anyway, I had some time on my hands and some intrigue my heart, so I thought I'd apply their nifty formula (below) to our beloved Cubs (2009 stats, plus Carlos Silva in the Google Doc). Here is their formula:

SIERA = 6.262 – 18.055*(SO/PA) + 11.292*(BB/PA) – 1.721*((GB-FB-PU)/PA) +10.169*((SO/PA)^2) – 7.069*(((GB-FB-PU)/PA)^2) + 9.561*(SO/PA)*((GB-FB-PU)/PA) – 4.027*(BB/PA)*((GB-FB-PU)/PA)

And here is why we like it:
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.
Follow the jump as we examine how this relates to our beloved Cubbies.

All of the data (except Carlos Silva) can be downloaded as a .xls file from Google Docs here. For Carlos Silva's numbers and for your own pleasure, I've also built a SIERA Quick Calculator on Google Docs. Feel free to copy the file and play with it!

So what do we see from SIERA? Do we get the intended affects? I tried to limit my analysis to players with more than 25 IP, but I've included a few with less.

The Winners (SIERA > -0.5 difference in xFIP)
Esmailin Caridad: SIERA 3.08, xFIP 3.89 (19.1 IP)
Ted Lilly: 3.37, 3.98
Kevin Gregg: 3.66, 4.16
Carlos Marmol: 4.22, 5.16

The Middle Pack (SIERA difference between -0.49 and 0)
Rich Harden: 3.24, 3.70
Tom Gorzelanny: 3.41, 3.64
Ryan Dempster: 3.79, 3.81
Carlos Zambrano: 4.11, 4.27
Angel Guzman: 4.13, 4.19
Aaron Heilman: 4.16, 4.20
David Patton: 4.72, 4.78
John Grabow: 4.93, 4.96
Jeff Samardzija: 5.10, 5.16
Kevin Hart: 6.12, 6.20

The Kind-of Losers (anyone with a higher SIERA than xFIP)
Sean Marshall: 4.01, 3.82
Randy Wells: 4.34, 4.24

What this tells me right off the bat: SIERA is typically lower than both FIP (not shown) and xFIP, so maybe it could use a little calibration in that regard. Additionally, as the Excel doc shows, SIERA tends to pick up the same inefficiencies that FIP does. All-in-all, it's like a FIP that allows a little more lee-way for groundball pitchers (which I like!).

Also, I automatically approve anything that tells me Marmol might be okay and Lilly could be even better.

In other, even more exciting news, Fangraphs has added splits to their database -- something I've really been wishing for over the last few months as David Appleman continues to expand the site. (h/t ACB)

Also, Rich Lederer of Baseball Analysts tooks a second look at Carlos Marmol, and -- frankly -- it's about as sad as the first look. :(


  1. Good work man. Enjoyed this. Thanks for that SIERA tool.

  2. No problem! I'm happy to expand the world of sabermetrics in what ever ways I can.

  3. Brad, do you know how to scale this to RA rather than ERA?

  4. I think multiply by .92 should do it.