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2006 League Averages

A reference post inspired by Jerry's comment a little while back. As a word of advice, percentages should be taken as being +/- 0.5% due to some weird issues with the Hardball Times data.

Hitters:

BA: .270
OBP: .337
SLG: .432
OPS: .769
uBB/K: 0.46
uBB%: 7.7%
K%: 17.0%
XTRA%: 34.5%
RC/G: 5.15
P/PA: 3.76
LD%: 19.6%
BABIP: .305
GB%: 43.6%
HR/FB%: 12.1%
IF/FB%: 10.0%
BA/RISP: .274

Pitchers:

ERA: 4.53
FIP: 4.52
xFIP: 4.70
K%: 17.0%
uBB%: 7.7%
HBP%: 1.0%
HR%: 2.9%
K/uBB: 2.19
K/9: 6.58
uBB/9: 3.00
HR/9: 1.13
P/PA: 3.76
K/G: 6.56
uBB/G: 2.98
HR/G: 1.10
DER: .695
LD%: 19.6%
GB%: 43.6%
FB%: 36.8%
GB/FB: 1.18
IF/FB%: 10.0%
HR/FB%: 12.1%
LOB%: 71.6%

Pretty much any individual stat you're ever going to need can be found at The Hardball Times or Fangraphs.

Might as well make this an open thread for any of you with questions about more advanced metrics.

0 recs  |  37 comments

Comments

Thanks Jeff
League average is a phrase that gets tossed around a lot, and its nice to have a concrete idea of what that means.

The pitching stats must be a bit skewed by including relievers (not just FIP, xFIP etc, but K/9 and BB/9), no?

Also, what is the +/-? is it the range for the 25-75 pecentile or something?

Yeah, they are a little skewed.
But trying to separate all the relievers from all the starters on the THT stats page is more work than it's worth to me. I don't think the difference would really be that great in any one category, except maybe HR/FB%, where relievers should be ~2% better than starters or so.

And the plus-minus represents one standard deviation. In other words, assuming a normal distribution, 68% of pitchers should fall within one standard deviation of the average.

Thanks
That is really useful.  

Thanks, Jeff.  

As an M's fan
it always startles me to see that the league average OBP is around .340. I guess I'm just too used to our free-swinging ways.
How is it
There is a different rate for BB%, K%,  IF/FB and HR/FB betweeen  pitchers and hitters?

i don't get it?

It would even out
if I included all the hitters with < 150 PA's and all the pitchers with < 50 innings.
2006 only?
Are these the THT numbers from 2006 only?
Yep.
I'm guessing there's little year-to-year deviation, though.
The Most Average Player
This is a non-scientific study (I just eye-balled it, but you could use some statistical method to compute sim scores), but here are my picks for most average player based on batting/pitching performance in 2006 (and their salaries too for fun):  

Batters:
Garret Anderson ($10 million)
Juan Encarnacion ($3.5 million)
A.J. Pierzynski ($4 million)
Brandon Phillips ($350,000)

Pitchers:
Dontrelle Willis ($4.35 million)
Josh Hancock ($355,000)
Victor Santos ($500,000...and selected in the Rule 5 Draft)
Justin Lehr ($337,000)

Yay
we have one of the most average pitchers in baseball.
The Most Statistically Average Pitcher
This time I actually ran the numbers (I used SQRT(Z-Score[K/G]^2+Z-Score[BB/G]^2+Z-Score[GB%]^2 to compute this), and applied them to all 2006 pitchers in the THT database.  Here are the results:

Most Average (min 50IP):

  1.  Chad Paronto, ATL
  2.  Vincente Padilla, TEX
  3.  Ryan Madson, PHI
  4.  Josh Beckett, BOS
  5.  Victor Santos, MIL
  6.  Justin Verlander, DET
  7.  Byung-Hyun Kim, COL
  8.  Brandon Lyon, ARI
  9.  Jae Seo, LAD
  10.  Dave Borkowski, HOU
*Most average Mariner, any IP:  Cha Seung Baek (he'd be between Lyon and Seo)

Least average pitchers (min 50 IP):

  1.  Cla Meredith, SD
  2.  Joe Nathan, MIN
  3.  Carlos Marmol, CHC
  4.  J.J. Putz, SEA
  5.  Dennys Reyes, MIN
  6.  Takashi Saito, LAD
  7.  Daniel Cabrera, BAL
  8.  Chien-Ming Wang, NY
  9.  Francisco Rodriguez, LAA
  10.  Derick Turnbow, MIL
I'm surprised
More supremely above average pitchers than below average.
I was seriously about to do this myself.
You rock.

$34m/3yr buys average these days, eh?

The sad part is...
...between park and league adjustments, and assuming relative sobriety, Padilla's probably a better contract than Batista.
Most Statistically Average Batters
Because of the way THT displays their data, and the stats I chose to compute "averageness", I went with the fangraphs data instead.  Thus, these are statistically average QUALIFIED players.  

I had a harder time picking out stats to work with compared to pitchers.  In the end I went with:
AVG, OBP, SLG, BB%, K%, and BABIP.  

Most Average Batters:

  1.  Shawn Green    - - -
  2.  Tony Graffanino    - - -
  3.  Melvin Mora    Orioles
  4.  Hank Blalock    Rangers
  5.  Adam Kennedy    Angels
  6.  Garret Anderson    Angels
  7.  Ramon Hernandez    Orioles
  8.  Brandon Phillips    Reds
  9.  Nicholas Markakis    Orioles
  10. David Bell    - - -
  11. Adrian Beltre    Mariners
Least Average Batters:
  1.  Ryan Howard    Phillies
  2.  Travis Hafner    Indians
  3.  Manny Ramirez    Red Sox
  4.  Albert Pujols    Cardinals
  5.  Jim Thome    White Sox
  6.  Jason Giambi    Yankees
  7.  Bobby Abreu    - - -
  8.  Adam Dunn    Reds
  9.  David Ortiz    Red Sox
  10. Miguel Cabrera    Marlins
This one is obviously going to be biased towards good players because players on the extremely good side of non-average would accumulate enough at-bats to qualify.  Actually never mind (Clint Barmes was the 17th least average guy, Angel Berrora was the 21st.)
I am so indebted to you.
You just saved me like an hour of my night.

It's not particularly reassuring to see Adrian Beltre and David Bell occupy consecutive positions on the same list.

You're welcome
You provided the standard deviations, after that it wasn't too hard.

I've come around quite a bit on Beltre.  If he was a FA, and the M's signed him to a 3/$39M deal this offseason, I probably wouldn't complain too badly.  

Then again, I intentionally don't watch many of his at-bats.

cool
thats way awsome.
Most Average or Most Rounded?
I think to call these lot below Most Average is misleading - if they host many of the qualities of a pitcher at average level they're doing pretty damn well. It's the ones who're good in some areas and weak in others who'll get exploited, and the younger a pitcher that fits in that top 10 the greater his potential.

Bodes well for Baek, I'd say.

Well, I agree with your primary points
But not the thing about Baek. Stats lie in small sample sizes.
You know who is remarkably average?
Based on fangraphs stats, Ervin Santana whom everyone has been raving about is almost exactly average in all stats except BABIP, which is out of pitcher control. Its a remarkable correlation.
So there. League average gets you 15 wins. Sometimes. Go Baek!
How is it that
The average ERA+ is 115... do pitchers with less than 50 innings really skew the stats that much?
When I include everybody
the average ERA+ comes out to 105 for some reason. For pitchers with < 50 IP, it's 97.
Hey jeff
can you make this a quick link. Like next to the community projections or something for reference so in the future I don't have to go diggin for this page.
Already did.
It's on the left, under "References".
On the subject of blogkeeping
The Hit Tracker Online link doesn't link to anywhere at the moment.
Whoops.
forgot the 'r' in 'a href'. Thanks for the heads-up.
A team full of players
that put up "league average stats" according to your numbers would win significantly more than 81 games.  Leaving out the players with fewer than 150 ABs or 50 IP, really skews things.  

According to my homemade strato-matic baseball simulator 2000, I've just run 10,000 seasons with a fictional team added to the league filled with "statistically average players" (meaning that injured players were replaced with other statistically average players - thus negating the deflating effects of injuries,) and that team averaged 93 wins per year.  This allowed the team to make the playoffs 54% of the time, and win the world series 12% of the time.

OK, I just made all that up.

Someone who really does have...
a homemade strato-matic baseball simulator should try this though, and see what happens.  I'm really curious.
Well
perhaps that means that players who last a full season should be held to a higher standard than the "normal" league average that takes everybody into account.
DING DING DING WE HAVE A WINNER....
This is something I've been saying for aeons. 180-200 IP of 4.4-ish ERA ball is actually above average.
That's not really what I meant.
I mean, you're right, but...well, let's use an example.

Overall league average: 4.53 ERA
Min. 50 IP league average: 4.33 ERA

A guy who throws 180 innings of 4.50 ERA ball would look average by the first and below-average by the second. And I think the latter is more meaningful, because what's the good in comparing (say) Horacio Ramirez against a player pool that includes a bunch of rookies and no-names who don't last a month?

Well, because...
Talent distribution as expressed in terms of player performance in MLB =! normal distribution (aka bell curve).

It's really a J-shaped curve, with 99% of humanity on the end of the J meaning "you suck at this", .9% at various places in the minors and at replacement level, and .1% being really good ballplayers. (OK, not that, it's more like 99.99999, .0000009, and .0000001, but you get the idea.)

Excluding "replacement level" talent out of your sample of MLB performance levels (which is what you do by tossing out <50 IP and   < 150 PA's) is a HUGE bias in evaluating performance.

I'm going to be perfectly honest...
this is retroactive justification. I limited the sample to > 150 PA's and > 50 innings because including everyone led to screwy standard deviations (because for certain stats there was an incredible spread at lower sample sizes).

I think I'll probably re-calculate all these numbers tonight, including everyone.

All set.
Everything's re-calculated.
Meaningful comparisons
But Jeff,
Given that Bavasi has to make choices between players like HoRam and your "bunch of rookies and no-names who don't last a month," I'd say it's meaningful.  But I'm starting to feel like a big Bavasi apologist weenie, so I'm just going to stop.  ;)

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