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Ten Starting Pitchers Who Deserve Better ERAs

Continuing with my recent theme of separating the real performances from the flukey ones, I'm going to take a look at starting pitchers whose skills indicate their ERAs are currently too high.  You might call these them unlucky.  In yesterday's look at the lucky pitchers, I used FIP, an ERA-like number that only considers a pitcher's strikeouts, walks, and homeruns.  There was some great discussion in the comments about other FIP-like metrics and today I'm going to use xFIP instead.

xFIP is nearly the same thing as FIP, except for the homerun part.  It adheres to the theory that pitchers can control how many outfield fly balls they give up, but hitters control how many of them turn into homeruns.  Adhering to that logic, xFIP calculates "expected homeruns" by multiplying the number of outfield fly balls by the league average rate of .11 homeruns per outfield fly ball.

Is it correct to do it this way?  Do all pitchers actually have the same underlying skill of allowing .11 home runs per outfield fly ball?  No, and it's not even as good of an assumption as assuming all pitchers have the same BABIP skill.  What we should be doing is regressing a pitcher's HR/FB rate towards .11 some amount.  What's that amount?  I haven't seen that study done yet.  I'm guessing it's somewhere around 50% over a full season, or more like 85% so far this year.  If that's correct, xFIP is better than FIP short term, because regressing 100% is better than 0% when the goal is 85%.

The List

Here are the top ten most unlucky, most underrated starters so far in 2009.  (tERA is from Stat Corner, while xFIP and other peripheral data from Hardball Times.)

  1. Ricky Nolasco FLA -- 41.7 IP, 7.78 ERA, 4.21 xFIP, 3.57 E-xF, 4.78 tERA
    If I told you a pitcher was averaging 7 Ks, 2.5 BBs, and 1.2 HRs per nine innings, you'd be kind of excited, maybe a little worried about the home runs, right?  You would not assume the pitcher had an ERA near 8.00, though.  That's Nolasco.
  2. Carlos Silva SEA -- 28.7 IP, 8.48 ERA, 5.68 xFIP, 2.8 E-xF, 6.99 tERA
    Silva's playing Mark Hendrickson's role on today's list.  Sure, he's been unlucky, but that doesn't mean you should expect him to be any good going forward.  He has ten strikeouts and five home runs in about thirty innings.  Uh, something positive, hmm... at least his walk rate is under three?
  3. Jon T Lester BOS -- 41.3 IP, 6.31 ERA, 3.62 xFIP, 2.69 E-xF, 4.32 tERA
    tERA doesn't like Lester as much as xFIP, but he's bound to stop giving up home runs at a rate of one out of every five outfield flies.  How can you not expect success given a K/BB ratio just below 4?  Sure, he might need to start nibbling a bit more to bring the home runs and the .400 BABIP down, but nobody should be worried about Lester suddenly changing into a below-average pitcher.
  4. Jose Contreras CHA -- 29.7 IP, 8.19 ERA, 5.73 xFIP, 2.46 E-xF, 4.60 tERA
    The big change for Contreras over past seasons is that he's walking more batters, but there's really no reason his ERA should be that high.  He's bound to start stranding more than 52% of the runners he puts on base, especially against AAA competition.
  5. Joe M Blanton PHI -- 34.3 IP, 6.82 ERA, 4.50 xFIP, 2.32 E-xF, 6.17 tERA
    19.8% of Blanton's outfield flies have turned into home runs this year.  Now, the average rate in Philadelphia's home park is probably higher than 11%, but it's not that high.  One cause for concern going forward, and tERA picks up on this, is that he's allowed line drives on 28% of balls put into play.  That's some solid contact right there.

Star-divide

  1. Jo-Jo Reyes ATL -- 26 IP, 6.58 ERA, 4.29 xFIP, 2.29 E-xF, 4.85 tERA
    These two numbers -- 1.4 homeruns per game and 51% ground balls -- don't really mesh, especially from a pitcher striking out seven batters per game.  Jo-Jo isn't yet fulfilling his potential, but he's a very good option at the back of the Braves rotation.
  2. Gavin C Floyd CHA -- 39.3 IP, 7.32 ERA, 5.07 xFIP, 2.25 E-xF, 6.19 tERA
    tERA doesn't like Floyd as much as xFIP, and neither one thinks too highly of him so far.  That's what a 1.5 K/BB ratio will do for you.  However, he's not a 7.00 ERA pitcher, since he's actually been pretty stingy with the long ball so far this year.
  3. Scott S Baker MIN -- 27.7 IP, 6.83 ERA, 4.63 xFIP, 2.2 E-xF, 6.09 tERA
    If it weren't for the next guy on this list, I'd say Baker's sporting an unimagineably high HR/FB rate, at 25%.  His strikeouts and great control are still there, but Baker has given up a ton of fly balls and a ton of them have left the park -- he's already allowed 40% of his home run total from last year.  That pace won't keep up.
  4. Randy Johnson SF -- 36.7 IP, 5.89 ERA, 3.70 xFIP, 2.19 E-xF, 6.32 tERA
    35% HR/FB rate.  In AT&T Park?  Seriously?  Everything else looks fine for the Unit, including 10 K/9.  In case you're wondering about that 6.32 tERA number up there, tERA doesn't regress home run rates like xFIP does, so it's not going to like the high HR/FB guys nearly as much.
  5. Andy Sonnanstine TB -- 34.7 IP, 7.27 ERA, 5.11 xFIP, 2.16 E-xF, 5.65 tERA
    Many Rays fans claim Sonny's "too hittable" and while there certainly might be something to that, he's not too hittable to the tune of a .387 BABIP.  And if hitters could really bang him around, they'd have hit more than just three home runs off of him so far this year.  Sonny's increased walk rate is a bit concerning, but even as high as it is, he's been a 5.00 ERA pitcher, not a 7.00 ERA pitcher.

Quick Hits

I've picked out the starters from the next fifteen on the list who have the highest ceiling: Josh Beckett (4.35 based on good peripherals; although there's the unproven theory that injured pitchers have higher BABIPs), Jordan Zimmerman (8 K/9 and 2.5 BB/9 are better signs than a 23% HR/FB rate), Carl Pavano (nice control, solid K rate, and a .360 BABIP point to league average from here on out), Javier Vazquez (I know I know, but he has a 15% HR/FB rate, well above any of his seasons in homer-friendly Chicago, and a 11/2 K/BB!), Jake Peavy (Sure, he's significantly aided by PETCO, but even if his 16% HR/HR rate doesn't come down, his ERA should drop by three-quarters of a run), and Justin Verlander (can this make up for my prescience about Armando Galarraga's fall back to earth? xFIP says his 4 K/BB ratio will produce a mid-3.00's ERA the rest of the way).

Lastly, because I'm using a different methodology today than yesterday, here are some pitchers that xFIP would consider lucky that FIP didn't mind as much: Dallas Braden, Wandy Rodriguez, Johnny Cueto, Zach Duke, and Edwin Jackson.

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Just subjectively

I feel like Scott Baker is doing to be one of those guys who is underrated for pretty much his whole career.

Maybe the Twins can trade him for [insert ovehyped Rays prospect here] or something.

I'm not a sabermetrician, but I do play one at Driveline Mechanics.

by Matt Klaassen on May 14, 2009 1:19 PM EDT reply actions  

So, I mistook tERA for tRA

and now am solidly confused about those numbers. Where do you find tERA?

by jwiscarson on May 14, 2009 2:42 PM EDT reply actions  

I just subtract .35 runs from tRA.

And I do it mentally, aren’t you impressed? ; )

Like I mentioned in yesterday’s post, multiplying by .92 (or something like that, it’s the ratio of earned runs to total runs) is probably a bit more accurate, but that takes more time and isn’t worth it for something like this.

I agree with the statcorner guys that the RA scale is better than the ERA scale. For one, pitchers deserve significant credit for most unearned runs, and two, we do everything else on the RA. However, nobody out there uses the RA scale and I’d like all the numbers presented to be on the same scale to prevent confusion. How many people can put the RA scale into context easily? Uh, not many.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 14, 2009 3:25 PM EDT up reply actions  

I see my problem now...

I accidentally looked at the tRA* column a StatCorner and wondered how you turned Randy Johnson’s 4.84 into 6.32.

In other news, sleep is conducive to increased reading comprehension.

by jwiscarson on May 15, 2009 1:09 AM EDT up reply actions  

Yeah, that'll do it ; )

I like the idea behind tRA*, but I’d like to see how much they’re actually regressing things. I really hope, but it’s not guaranteed, that they’re regressing based on a pitcher’s performance sample, because that’s key. In general, tRA* seem more regressed than any projection system out there (which makes me a bit worried), but it might totally be correct and it might be because projection systems use three years of data whereas tRA* is based only on one season’s data.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 15, 2009 9:36 AM EDT up reply actions  

My total uninformed guesson tRA*'s regression

is that its based on a certain number of BFP of league average all season long, as almost everyone that I looked at the first couple of weeks, almost everyone had an almost a identical tRA*.

I'm not a sabermetrician, but I do play one at Driveline Mechanics.

by Matt Klaassen on May 15, 2009 12:20 PM EDT up reply actions  

That's a good sign.

I’m hoping they regress not on total BFP, but on the opportunities for each event.

For example, HR/FB% is going to become more certain as the number of outfield flies increases, not simply BFP. In 100 BFP, Fausto Carmona’s going to give up many fewer OFFB than Derek Lowe. OFFB and IFFB would be a subset of FB. FB, LD, and GB would be a subset of BIP (including HR). K and BB would be a subset of total BFP, although perhaps K is a subset of AB; that’s how Voros handled it originally, at least.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 15, 2009 12:40 PM EDT up reply actions  

I wonder if there is a scientific way to say how much you should regress HR/FB ratio?

It wouldn’t be that hard to do your own xFIP formula.

St. Louis Cardinals... defying win expectancy since 2008

by vivaelpujols on May 14, 2009 3:57 PM EDT reply actions  

Pizza Cutter's looked at it, and it doesn't reach an r^2 of .5 in under 650 BFP.

K/PA: 150 BF
GB%: 150 BF
LD%: 150 BF
FB%: 200 BF
GB/FB: 200 BF
K/BB: 500 BF
IF FB%: 500 BF
BB/PA: 550 BF
BABIP: Doesn’t reach a 0.50 r-squared at 650 or below.
HR/FB: Doesn’t reach a 0.50 r-squared at 650 or below.

That’s from here, because the statspeak.com link doesn’t work.

I’m not up on my regression methodology to solve the question, but you’d ideally compare HR/FB performances over a certain number of outfield fly balls (or total fly balls or total fly balls plus line drive if you don’t trust the classifications) between two time periods and see what amount of regression to the first period’s data makes it best predict the second set’s data.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 14, 2009 5:00 PM EDT up reply actions  

This is one of those things where pitchers definitely have a skill, but there's just not a lot of samples over a full season.

A pitcher might give up 20 to 30 HRs over a full season, meaning a diference of 5 is huge. But with K%, a pitcher has many many more, and a difference of 5 is much less significant. As an extreme example, think about half-court shots in basketball. Some players are probably much much better than others, but you probably don’t even have enough data over 1000 games to decide for sure who’s better

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on May 14, 2009 5:03 PM EDT up reply actions  

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