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BtB Power Rankings: Through Tuesday, May 26, 2009

Sky's note: I'm excited to announce that Justin's agreed to join the BtB team.  He thinks it's on a limited basis, but we'll convince him otherwise.  You're probably already familiar with Justin's work from his blog, On the Reds, which is now Basement Dwellers (and my new favorite stathead pun).

If you were to try to rank all MLB teams, how would you do it?  W/L records?  Runs scored and runs allowed?  WAR?  I decided to put together a power ranking of MLB teams, and Sky has been kind enough to agree to publish it here at BtB. 

Detailed methodology follows the rankings and commentary, but very briefly: I estimated team runs scored and team runs allowed based on team hitting, pitching, and fielding statistics.  I then used the Pythagorean formula to estimate a team winning percentage, after a league adjustment.  Therefore, actual team wins, losses, runs scored, and runs allowed are not used in these rankings.  You can interpret the estimated winning percentage reported below as the winning percentage we'd expect teams with these hitting, pitching, and fielding statistics to produce if we threw them all into one big league and let them battle it out.  They are NOT forecasts, but rather an alternative look at what teams have done thus far.

With that preamble, here are the team rankings (table is sortable by clicking in the column headers; asterisks indicate park-adjusted data; "e" stands for "expected"):

Rank Team wOBA* eRS* FIP* FIPR* Fld eRA* LgAdj eW%
1 LAN .355 271 3.89 199 2.4 197 -6.5 .626
2 TOR .345 260 4.26 224 10.7 213 6.8 .621
3 TB .361 283 4.86 245 10.3 235 6.7 .615
4 DET .337 212 4.21 197 13.2 184 6.1 .595
5 TEX .351 240 4.91 234 19.2 215 6.3 .579
6 NYA .364 276 5.17 255 1.8 253 6.4 .568
7 BOS .350 249 4.48 220 -8.7 229 6.4 .567
8 KC .326 203 3.72 183 -12.5 196 6.4 .546
9 NYN .346 238 4.06 197 -13.3 210 -6.3 .534
10 CLE .348 255 4.96 244 -9.5 253 6.5 .529
11 MIN .347 246 4.84 240 -6.1 246 6.5 .526
12 LAA .336 219 4.58 218 -3.1 221 6.3 .524
13 ATL .323 198 3.66 179 -3.9 182 -6.3 .507
14 STL .332 213 4.08 202 3.4 199 -6.4 .504
15 PHI .348 236 5.05 236 5.1 231 -6.1 .485
16 MIL .331 214 4.80 233 21.9 211 -6.4 .478
17 PIT .328 207 4.76 226 18.5 208 -6.4 .470
18 CIN .323 202 4.47 221 13.2 208 -6.3 .457
19 CHA .313 179 4.09 194 -15.9 210 6.3 .456
20 COL .320 193 4.08 193 -7.3 201 -6.3 .452
21 WAS .346 248 5.08 246 -15.4 261 -6.3 .448
22 BAL .332 215 5.04 241 -14.8 256 6.4 .442
23 SD .323 201 4.39 217 0.9 216 -6.4 .438
24 HOU .327 204 4.60 220 -1.9 221 -6.1 .434
25 CHN .317 184 4.40 201 0.6 201 -6.1 .430
26 SEA .303 172 4.47 227 4.0 223 6.5 .414
27 OAK .306 170 4.48 213 -6.0 219 6.0 .412
28 ARI .308 183 4.40 220 4.6 215 -6.4 .395
29 FLA .315 200 4.44 227 -11.8 239 -6.5 .389
30 SF .296 156 4.12 200 -0.8 201 -6.3 .359

Team Talents
Top AL offenses (wOBA*): Yankees, Rays, Rangers, Red Sox, Indians
Top AL pitching (FIP*): Royals(!), White Sox, Tigers, Blue Jays, Mariners
Top AL fielding (bUZR and THT): Rangers, Tigers, Blue Jays, Rays, Mariners

Top NL offenses (wOBA*): Dodgers, Phillies, Nationals, Mets, Cardinals
Top NL pitching (FIP*): Braves, Dodgers, Mets, Cardinals, Rockies
Top NL fielding (bUZr and THT): Brewers, Pirates, Reds, Phillies, Diamondbacks

"On Paper" Division Leaders
AL East: Blue Jays
AL Central: Tigers
AL West: Rangers
AL Wild Card: Rays

NL East: Mets
NL Central: Cardinals
NL West: Dodgers
NL Wild Card: Braves

Comments and methods below the fold:

Star-divide

Five teams of note:

LA Dodgers

The Dodgers haven't missed a beat since the loss of Manny, though Juan Pierre's .435 wOBA has helped them absorb the loss of offensive production.  They may be a tad "lucky" in terms of runs allowed (186 actual vs. 197 expected), but this has been a team with excellent offense, excellent pitching, and even roughly average fielding.  None of the other teams in the NL West have a .500+ record...

Tampa Bay Rays

The Rays come up #3, despite a sub-.500 record.  But look at their component statistics!  Best park-adjusted expected runs scored in baseball.  Seventh-best park-adjusted expected runs allowed, largely due to their number seven ranked fielding.  They have played outstanding baseball thus far, and the Blue Jays--who have also been superb--will have to worry about this team as the season wears on.

Cleveland Indians

While not contenders, the Indians probably aren't as bad as they look.  Their defense (pitching + fielding) has been dreadful, but their offense has produced the fifth most expected runs in baseball.  These data put their expected runs scored almost exactly the same as their expected runs allowed, which would make them a .500 team (and a .500+ team after accounting for the fact that they play in the AL).

Washington Nationals

Yet another underperforming team--though you'd almost have to underperform to get a 13-32 record.  The Nationals' offense has been very good thus far thanks to Ryan "Mr. Streak" Zimmerman and the newly acquired Adam Dunn.  But they've allowed the most runs in baseball, after park adjustments.  In a lot of ways, they remind me of some of Dunn's previous Reds teams--great offense, terrible defense.

San Francisco Giants

We can't focus exclusively on the underperformers, so here's a team that looks like an overperformer.  The Giants come in dead last in our ranking, despite a true record just shy of .500.  This team has the worst offense in baseball, which scores so few runs that even their very good pitching staff can't keep them from being outscored.  What is happening here, though, is that their expected runs scored is massively below their actual totals (18 run difference).  And similarly, their expected runs allowed is 21 runs higher than their actual runs allowed.  This team may be lucky to stay out of the dungeon by season's end.

Methods

Here is a run-down of the methods behind the table above.

Estimated team runs scored (eRS) is calculated using wRC, which is a linear-weights estimate of absolute runs scored and pulled from FanGraphs (I heart FanGraphs).  I adjust this number for park effects using Patriot's 5-year regressed park factors.

Team defense is comprised of pitching and fielding, each of which is estimated independently.  Pitching is estimated as FIPRuns, which is a simple modification of FIP to yield an estimated runs allowed total (details here; tRA* would be better, but Excel can't pull it from statcorner).  I park adjust home run rates using Patriot's HR park factors, and do not include intentional walks in the walk totals.  Fielding is the average runs saved estimates of bUZR (from FanGraphs) and the batted-ball team fielding statistic from Hardball Times (converted into runs, assuming 0.8 runs per play saved).  So, estimated runs allowed (eRA) = FIPRuns - FieldingRuns.

Update (5/29/09): Thanks to the discussion below, I decided to switch from using FIP to Graham MacAree's tRA to evaluating pitching.  I think this is an improvement, in that it allows us to better recognize everything that a pitcher can potentially control while still separating pitcher performance from fielding performance.  Future rankings will use tRA, but the above numbers have not been updated.  In all honesty, it ends up not making a huge difference in most cases.



One final adjustment is needed, though--the American League features a higher level of competition than the National League, thus we need to give a bonus to AL teams and a penalty to NL teams if we are to rank them against each other.  How?  If you look over the past 5 years (2004-2008), AL teams have dominated interleague play (702-557, 0.558 winning percentage).  I'll spare you the math (I used the Odds Ratio and PythagoPat), but it turns out that if you give a 22.5 run per season bonus to an average AL team offense *and* defense, and a 22.5 run per season penalty to an average NL team offense *and* defense, you can very accurately predict the AL's winning percentage in interleague play.  So, I am applying these adjustments (pro-rated by games played--the adjustment is 6-7 runs at this point) to teams prior to calculating expected winning percentage. 

(Note: as a check, we usually assume that replacement players will hit five runs better vs. average in the NL vs. the AL per 700 PA.  The average team in 2008 had 6254 PA, so 6254/700*5 runs = 45 runs.  I'm essentially splitting this difference evenly between the AL and NL offenses.  We assume a similar adjustment for pitchers, and thus defense as well as offense receives this adjustment). 

So, expected winning percentages for each team are calculated according to PythagoPat:

(eRS+lgadj)^K
-------------------------------------------
[(eRS+lgadj)^K + (eRA+lgadj)^K]


Where:
eRS = estimated runs scored
eRA = estimated runs allowed
lgadj = pro-rated league adjustment (positive for AL, negative for NL)
K = PythagoPat coefficent

Comment 57 comments  |  4 recs  | 

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This is great stuff

I want to know where you find all these stats and how good you are at applying them.

Rebuild and Restock.

by trademaker on May 27, 2009 4:34 PM EDT reply actions  

Methods

…are above. Feedback, especially with suggestions, is very welcome! -j

by JinAZ on May 27, 2009 7:51 PM EDT up reply actions  

Two things...

1st, Justin + BtB = awesome.

2nd, the Indians are the tenth best team in baseball?

http://statspeak.net

by pizzacutter on May 27, 2009 4:47 PM EDT reply actions  

Correct me if I'm wrong

But it seems this has happened to them for quite a few years in a row now. They always seem to have a worse record than expected.

When I was a kid I used to pray every night for a new bicycle. Then I realized God doesn’t work that way, so I stole one and prayed for forgiveness. - Emo Philips

Proud father of Juan Carlos Perez. Think Albert Pujols at second.

by marcello on May 27, 2009 6:36 PM EDT up reply actions  

They have been horrible in 1 one games

and they Yankee blowout doesn’t help.

I wish that if a game hits like a 98% to 99% winning percentage, the stats are no longer counted for either team.

by Jeff Zimmerman on May 27, 2009 6:58 PM EDT up reply actions  

No. Who are you again?

I agree, and it’s sort of a tautology, that records in one-run and blowout games explain a lot of the difference between actual and Pythag records.

But that’s not really the point. The point is if the actual record or the Pythag record is a better measure of talent or a better measure of winning going forward. If explaining wins was the point, then you’d adjust runs scored by the leverage of the situation they were scored in. But that’s pretty much WPA. And WPA is exactly wins.

If you want to indirectly account for things like one-run and blowout games, you need to figure out which types of teams tend to win and lose them (like you started to in the linked article). If bad bullpens or unleveraged bullpen are bad, then you can build that in (but ignoring specific game data). If highly variable offenses are bad (or good) then you can build that in; not based on actual runs distributions, though, but the expected variability of runs scored based on offensive events. For example, a high HR, low AVG team will have a different distribution than a low BB, high AVG team. And for starters, it’s fair to look at individual starters’ true talents. A rotation of two Zack Greinkes and three Carlos Silvas might be better than five average guys.

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

by Sky Kalkman on May 27, 2009 8:49 PM EDT up reply actions  

Lots of teams near 0.500

…so the league adjustment makes a big difference. -j

by JinAZ on May 27, 2009 7:54 PM EDT up reply actions  

sweet.

one question: how relevant are the park factors that are determined over a whole season to april and may games? presumably the parks that see the biggest temp jump as the weather warms would be the most affected.

THIS STORY ONLY ENDS ONE WAY

by colintj on May 27, 2009 8:01 PM EDT reply actions  

It's a fair point...

By season’s end, it won’t be an issue, and this is something I want to keep updating all year. But I agree that it’s possible that some of the park adjustments may be too severe at this point due to the lower temperatures. Any team in particular you think I’m over-correcting?

One thing, though—I’m applying the same park adjustments to team offense and defense. This means that the expected winning percentage will be minimally affected, even if the rankings of best offense, for example, would.
-j

by JinAZ on May 27, 2009 9:47 PM EDT up reply actions  

Here are the differences in high and low average temps from my previous study (Usually from July/August high temp and April is low temp). Looks like inland and northern cities are effected while southern and Pacific Coastal cities are less effect.

Boston Red Sox 10
Chicago Cubs 10
Milwaukee Brewers 10
Baltimore Orioles 10
Toronto Blue Jays 10
Arizona Diamondbacks 10
Philadelphia Phillies 10
Detroit Tigers 10
Chicago White Sox 10
Colorado Rockies 10
Kansas City Royals 10
St. Louis Cardinals 9
New York Yankees 9
Cincinnati Reds 9
Cleveland Indians * 9
Washington Nationals 9
Pittsburgh Pirates 9
New York Mets 9
Texas Rangers 8
Atlanta Braves 7
Los Angeles Angels of Anaheim 6
Seattle Mariners 6
Houston Astros 5
Los Angeles Dodgers 5
San Diego Padres 4
Florida Marlins 4
Oakland Athletics 3
San Francisco Giants 1
Minnesota Twins 0
Tampa Bay Rays 0

by Jeff Zimmerman on May 28, 2009 12:36 AM EDT up reply actions  

Great

Another great saberblogger assimilated to the SkyBorg

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

by Matt Klaassen on May 27, 2009 10:24 PM EDT reply actions  

BtB will enslave us all!

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

by vivaelpujols on May 27, 2009 11:00 PM EDT up reply actions  

Great job Justin

I really enjoy seeing all those fancy stats we have put to a good use. On a side note, if you want to use tRA for pitchers its pretty easy to duplicate the formula. Graham lists the coefficients on his primer; they’re from 2008, but they should be pretty accurate. The only problem is that FanGraphs doesn’t actually list the number of LD, GB and FB on their team stats. They just have the percentages. However, you could easily convert those to total batted balls by multiplying the percentages by BIP. Then you can just use THT’s batted ball park factors to adjust the numbers, input the tRA formula, and viola!

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

by vivaelpujols on May 27, 2009 11:16 PM EDT reply actions  

Thanks for that

I may give it a go. I’m not sure I can completely replicate it, but I might be able to get pretty close—at least close enough to feel good about the numbers. -j

by JinAZ on May 28, 2009 4:00 AM EDT up reply actions  

xFIP

Use xFIP or at least QERA. Using FIP exacerbates HR/Flyball luck.

by Matt Swartz on May 27, 2009 11:49 PM EDT reply actions  

Is xFIP just FIP subsituting .10*FB for HR?

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

by vivaelpujols on May 28, 2009 12:22 AM EDT up reply actions  

.11*OFFB, so basically yes

The .11 should probably be modified for current average HR/FB rate. I’d rather see it figured out what the correct regression rate is instead of going with 100% regression.

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

by Sky Kalkman on May 28, 2009 12:31 AM EDT up reply actions  

Tango offered this formula:

r = x / (x + c). X is the plate appearances that the batter currently has. C is the plate appearances that you should regress to league average. R is the R at which the rate stabilizes. Of course Pizza Cutter disagrees with that, and I’m not smart enough to take sides.

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

by vivaelpujols on May 28, 2009 12:38 AM EDT up reply actions  

You can read more about it here and here

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

by vivaelpujols on May 28, 2009 12:41 AM EDT up reply actions  

It's an approximation.

It is probably not “correct” (at least, so far as I can discern.) We use things that are incorrect all the time, with the understanding that they are close to correct and much easier.

If we want better results than that, we can use other formulas (there’s a more complex one in the back of The Book.) But frankly, looking at it makes my head hurt.

by cwyers on May 28, 2009 2:59 AM EDT up reply actions  

Do we care?

You may be correct in that it’s a better predictor of future performance than FIP. But these are explicitly not projections but value rankings.

xFIP does a very crude form of regression to the mean, where we just regress something 100% to the mean. Since there is typically little variance in HR/F, we can get away with that shortcut some of the time as a way to express true talent. But for a value metric, we don’t want to regress anything to the mean.

The reason to use a DIPS-like measure of performance in a value metric is not to remove luck, but to separate credit between pitching and defense. FIP is better for those purposes than xFIP.

by cwyers on May 28, 2009 2:53 AM EDT up reply actions  

Defense = Pitching vs fielding

I think Colin makes a great point. As crude as it is, FIP allows complete separation of pitchers and fielders. And while it ignores direct pitcher influence over hit rates, it does keep historical info about HR rates intact. That’s actually not something that tRA* allows, even though I like it better as summative pitching statistic. Fielding will still influence tRA.

That said, my preference is to focus on stats that are repeatable so that we’re not just tracking noise. And it looks like HR/F rates, even at the team level, are sort of al over the place (6% to 14%). I had assumed they would have stabilized given that we’re at the 400 inning mark. I agree with Sky’s point about using appropriate regression instead of 100% regression, though. I’ll have to fiddle a bit with the data and then think about it. -j

by JinAZ on May 28, 2009 3:45 AM EDT up reply actions  

brain fart

I take back what I said re: tRA. It shouldn’t be influenced by fielding.

It’s 3am and I’m rocking an week-old infant. That’s my excuse. :)
-j

by JinAZ on May 28, 2009 3:50 AM EDT up reply actions  

If the sole goal is to determine "value"

then why not just use ERA? FIP eliminates luck in it’s own way because it takes timing out of the equation. However, like HR/FB, giving up an unusually large or small amount of hits w/ RISP reflects value, if not skill. FIP strips that away from the equation. If you truly just want to find a value metric, ERA is the best way to go, as it isn’t really necessary to seperate pitching from fielding if they both work together.

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

by vivaelpujols on May 28, 2009 4:03 AM EDT up reply actions  

What about tRA*?

The Crawfishboxes
A good friend of mine used to say, "This is a very simple game. You throw the ball, you catch the ball, you hit the ball. Sometimes you win, sometimes you lose, sometimes it rains." Think about that for a while.

by Stephen Higdon on May 28, 2009 11:20 PM EDT up reply actions  

Because ERA includes the effects of fielders, which I want to explicitly examine separately.

And ERA also misses some fielding effects (in a clunky way) by excluding unearned runs. RA, or maybe WPA, would be pure value. But then we might as well just look at the standings.

Thinking more about it, I think a home-brew version of tRA may be the way to go. This is a value ranking, so I won’t worry about the regression of tRA* (at least initially…we can talk more on it), but at least tRA looks at more than just home run, k, and bb rates while still keeping fielding out of the picture.

My guess is that stuff won’t change much vs FIP. But we’ll see!
-j

by JinAZ on May 28, 2009 8:05 AM EDT reply actions  

But it's not an extreme value metric.

For example, you’re not taking into account timing of batting events, you’re combining them randomly. Same for fielding. And you’re not taking into account the gradients in the bullpen – that is, if one bullpen is more leveragable than another.

We’re in the gray area here, where you could go a number of different ways, and there’s no perfect answer for which way to go. I wonder if there’s a way to summarize the mission of these power rankings…?

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

by Sky Kalkman on May 28, 2009 12:52 PM EDT up reply actions  

I wonder if there’s a way to summarize the mission of these power rankings…?

That would make things a lot easier.

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

by vivaelpujols on May 28, 2009 2:20 PM EDT up reply actions  

I'm probably a bit fuzzy on this too

The closest I’ve gotten to articulating a mission is this:

You can interpret the estimated winning percentage reported below as the winning percentage we’d expect teams with these hitting, pitching, and fielding statistics to produce if we threw them all into one big league and let them battle it out. They are NOT forecasts, but rather an alternative look at what teams have done thus far.

I think what I want to do with this is to get past the timing issues of team performance and just look at how well each team has hit, pitched, and fielded (regardless of situation), and to weight those factors against each other to get an alternative view of team performance besides simple W/L records (or RS vs. RA).

…That said, I do tend to think that deviations between what these data estimate and actual W/L records have some predictive value. Once you start saying prediction, though, you start thinking true talent. And that means projections. And I don’t really want to get into projections here. I only want to focus on current year-to-date performance. But at the same time, I want to focus on performance in measures that are repeatable, because those are the ones that are the most interesting to me…

I’m still going back and forth, I know, but I’ll keep thinking on it. If anyone has a nice way to clarify this, feel free to comment.
-j

by JinAZ on May 29, 2009 12:09 PM EDT up reply actions  

Well a like the way you are doing it now

Which IMO is basically “how well would this team currently be doing with their current repeatable skills, if they had normalized timing/luck”. I actually like the idea of using FIP, as it takes everything out of the equation besides the stuff a pitcher can actually control.

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

by vivaelpujols on May 29, 2009 2:45 PM EDT up reply actions  

tRA* would be better for that

…as pitchers do have some control over balls hit into play. But it’s not much, which is why FIP works fine most of the time.

And if we’re really interested in pure DIPS, then commenters above are right that xFIP is probably better than FIP.

I’m playing with tRA (no asterisk) right now, and am leaning toward using it. tRA is closer to what wRC does, in that it focuses on counting stats specific to the players, but doesn’t eliminate lucky performances (high or low BABIP’s). It’s not much different from using base runs, except that it removes fielding from the picture. Using it is leaning more toward “value” than using FIP or xFIP, but I think it gives the ranking a bit more clarity in its purpose. There still will likely be deviations from straight pythagorean records due to timing, and that’s part of what will make the ranking informative.
-j

by JinAZ on May 29, 2009 3:03 PM EDT up reply actions  

Got tRA working...

And it really didn’t make much difference. But I think I probably will go ahead and go with tRA.

MSE’s:
FIPRA v. RA: 0.291
tRA v RA: 0.335
FIPRA v. tRA: 0.030

After park corrections to each.
-j

by JinAZ on May 29, 2009 8:38 PM EDT up reply actions  

Looking at the rankings..

For the most part, it’s pretty similar. Biggest jump was the Cubs, which skyrocketed from #25 to #18. Other “big” movers:

Rockies went #20 up to #17.
Pirates #17 down to #20.
Reds #18 down to #21 (bah).
A’s #27 up to #24.
Astros #24 up to #27

I’ll use the tRA numbers as the “previous” ranking next week, since the methods change did make a difference.

Again, this is without any regression to the tRA coefficients. We’re just getting rid of the “randomness” of what happens when you hit a LD and assuming a constant rate of out- and run-generation. I am forcing expected runs and expected outs to equal true outs and true runs using a coefficient for each factor. And I’m using all the component park factors from Gassko’s work that I can. Everything seems to work nicely. Neat!
-j

by JinAZ on May 29, 2009 10:04 PM EDT up reply actions  

Any ideas for an easy SoS adjustment within leagues?

Is there some way to do it iteratively given schedule information and the initial set of power rankings?

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

by Sky Kalkman on May 31, 2009 9:19 AM EDT reply actions  

Are you trying to adjust the current power rankings?

If so, you could just use the current power rankings as the teams true talent level and use the log5 formula to adjust for strength of schedule.

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

by vivaelpujols on May 31, 2009 2:28 PM EDT up reply actions  

How exactly would you go about doing that?

You’d need a list of opponents, and maybe even starting pitcher information to really get it right. And number of home/away games matters, too. COL’s at 20, while another team’s at 30.

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

by Sky Kalkman on May 31, 2009 7:08 PM EDT up reply actions  

right

I guess you could just come up with a quick formula based on the W% of each teams schedule. The problem is that the current rankings are going to be affect by that schedule. It’s probably gonna be pretty complicated. How does BPro adjust 3rd Order Wins?

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

by vivaelpujols on May 31, 2009 9:51 PM EDT up reply actions  

Would this confound with the league adjustments?

AL teams will have a greater strength of schedule than NL teams because of the league adjustments. I suppose I could look at within-league expected w% for the strength of schedule adjustment, and then apply the (apparently controversial!) league adjustments.

Getting the strength of schedule data ready to go won’t be an insignificant task, though. Not really looking forward to that…and I have some other projects in mind to do. Sort of a back burner issue for me…
-j

by JinAZ on May 31, 2009 4:53 PM EDT reply actions  

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