BtB Power Rankings: End of 2009 Season Data

by JinAZ on Oct 11, 2009 9:09 PM EDT in Power Rankings Comment 30 comments

Pwr-20091009-sm_medium

See the complete rankings below the jump!

"On Paper" Division Champions

American League: E=Yankees, C=White Sox, W=Angels, WC=Rays
National League: E=Phillies, C=Cardinals, W=Rockies*, WC=Dodgers

Converting Runs to Wins

Team	RS	eRS	RA	eRA	W%	pW%	cW%	LgAdj	TQI
ARI	686	689	745	677	0.432	0.461	0.508	-20	0.481
ATL	735	735	641	682	0.531	0.563	0.535	-20	0.508
BAL	734	716	867	856	0.395	0.420	0.415	20	0.439
BOS	838	845	708	731	0.586	0.580	0.568	20	0.593
CHW	696	685	704	682	0.488	0.495	0.502	20	0.529
CHC	680	687	646	659	0.516	0.523	0.520	-20	0.492
CIN	660	642	709	730	0.481	0.467	0.441	-20	0.414
CLE	773	778	865	850	0.401	0.446	0.457	20	0.481
COL	738	755	656	608	0.568	0.554	0.599	-20	0.572
DET	743	742	745	759	0.528	0.499	0.490	20	0.515
FLA	788	794	782	785	0.537	0.504	0.505	-20	0.481
HOU	649	662	778	778	0.457	0.417	0.425	-20	0.399
KCR	686	667	842	793	0.401	0.404	0.419	20	0.445
LAA	892	860	769	816	0.599	0.572	0.525	20	0.549
LAD	796	798	623	654	0.586	0.612	0.593	-20	0.567
MIL	785	784	818	824	0.494	0.480	0.476	-20	0.452
MIN	817	817	765	816	0.534	0.532	0.500	20	0.524
NYY	915	961	753	749	0.636	0.594	0.620	20	0.643
NYM	671	697	757	776	0.432	0.444	0.450	-20	0.424
OAK	774	732	777	721	0.463	0.499	0.507	20	0.533
PHI	804	803	695	722	0.574	0.568	0.550	-20	0.525
PIT	649	639	784	775	0.385	0.413	0.412	-20	0.386
SDP	693	694	836	811	0.463	0.412	0.427	-20	0.402
SEA	660	672	713	731	0.525	0.464	0.461	20	0.487
SFG	650	610	605	652	0.543	0.533	0.470	-20	0.441
STL	745	745	653	683	0.562	0.561	0.541	-20	0.514
TBR	811	835	762	709	0.519	0.530	0.577	20	0.601
TEX	761	728	718	704	0.537	0.527	0.516	20	0.543
TOR	782	798	756	751	0.463	0.516	0.529	20	0.554
WSN	703	728	865	875	0.364	0.402	0.412	-20	0.388

Table Legend

RS = Actual Runs Scored, after a park adjustment
eRS = Estimated Runs Scored, after park adjustment (see "Offense" table below)
RA = Actual Runs Allowed, after a park adjustment
eRA = Estimated Runs Allowed, after park adjustments (see "Defense" table below)
W% = Actual Winning Percentage
pW% = PythagenPat Winning Percentage, based on actual runs scored and run allowed totals
cW% = Component Winning Percentage (previously eW%lg), using estimated runs scored and estimated runs allowed totals
LgAdj = League adjustment, based on differences in league quality (justification here and here).
TQI = Team Quality Index, a hypothetical winning % based on component estimates of runs scored and runs allowed after the league adjustment.

Team Offenses and Defenses

Team	RS	eRS	wOBA	OBP	SLG	wRC	EqBRR	Clutch	RA	eRA	ERA	FIP	tERA	tRuns	Field	Catch	BABIP
ARI	686	689	0.318	0.324	0.418	696	-7	-34	745	677	4.44	4.13	3.95	686	20	-11	0.308
ATL	735	735	0.326	0.339	0.405	751	-16	-29	641	682	3.57	3.63	3.84	674	-13	4	0.307
BAL	734	716	0.324	0.332	0.415	737	-22	-22	867	856	5.16	4.94	4.80	823	-36	3	0.314
BOS	838	845	0.346	0.352	0.454	845	0	-28	708	731	4.35	4.26	4.02	692	-30	-9	0.320
CHC	680	687	0.318	0.332	0.407	701	-14	-46	646	659	3.84	3.99	3.81	661	-5	7	0.294
CHW	696	685	0.320	0.329	0.411	694	-9	-19	704	682	4.16	4.02	3.80	655	-21	-6	0.301
CIN	660	642	0.311	0.318	0.394	651	-9	-19	709	730	4.18	4.50	4.51	789	46	13	0.288
CLE	773	778	0.334	0.339	0.417	781	-3	-40	865	850	5.07	4.81	4.76	819	-33	2	0.312
COL	738	755	0.331	0.343	0.441	748	7	-39	656	608	4.24	3.84	3.56	614	9	-4	0.307
DET	743	742	0.329	0.331	0.416	746	-3	11	745	759	4.34	4.54	4.60	798	25	14	0.298
FLA	788	794	0.337	0.340	0.416	793	1	15	782	785	4.32	4.17	4.38	760	-23	-3	0.309
HOU	649	662	0.318	0.319	0.400	662	-1	29	778	778	4.54	4.24	4.41	756	-25	4	0.318
KCR	686	667	0.318	0.318	0.405	676	-9	-21	842	793	4.82	4.50	4.20	719	-58	-16	0.315
LAA	892	860	0.349	0.350	0.441	859	1	26	769	816	4.45	4.53	4.65	806	0	-10	0.307
LAD	796	798	0.336	0.346	0.412	803	-5	-26	623	654	3.41	3.73	3.82	675	25	-3	0.283
MIL	785	784	0.335	0.341	0.426	794	-9	16	818	824	4.84	4.78	4.74	815	-4	-5	0.305
MIN	817	817	0.340	0.344	0.429	813	4	-22	765	816	4.50	4.51	4.50	784	-31	-1	0.308
NYM	671	697	0.322	0.335	0.394	695	2	-9	757	776	4.46	4.46	4.40	752	-31	8	0.302
NYY	915	961	0.363	0.362	0.478	967	-6	-4	753	749	4.28	4.34	4.32	752	5	-3	0.299
OAK	774	732	0.327	0.328	0.397	720	12	-31	777	721	4.29	4.18	4.09	710	-17	6	0.310
PHI	804	803	0.338	0.334	0.447	802	0	27	695	722	4.16	4.24	4.31	752	28	3	0.304
PIT	649	639	0.314	0.318	0.387	647	-8	-18	784	775	4.59	4.59	4.60	782	14	-6	0.303
SDP	693	694	0.322	0.321	0.381	701	-6	30	836	811	4.37	4.42	4.63	805	-7	1	0.302
SEA	660	672	0.319	0.314	0.402	674	-2	25	713	731	3.87	4.48	4.66	811	73	7	0.280
SFG	650	610	0.308	0.309	0.389	600	10	29	605	652	3.55	3.93	4.00	693	46	-6	0.289
STL	745	745	0.331	0.332	0.415	740	5	9	653	683	3.66	3.92	3.98	687	-3	8	0.299
TBR	811	835	0.346	0.343	0.439	831	4	-23	762	709	4.36	4.40	4.43	758	47	2	0.299
TEX	761	728	0.329	0.320	0.445	731	-2	-16	718	704	4.38	4.48	4.33	745	38	4	0.294
TOR	782	798	0.337	0.333	0.440	788	10	-75	756	751	4.47	4.26	4.25	740	-19	8	0.313
WSN	703	728	0.326	0.337	0.406	740	-12	-35	865	875	5.02	4.81	4.95	845	-21	-9	0.305

RS = Actual Runs Scored
eRS = Estimated Runs Scored: wRC + EqBRR
wOBA = The Book's statistic, but park adjusted, and using data from both wRC and EqBRR
OBP = On Base Percentage (times on base / PA)
SLG = Slugging Percentage (total bases / PA)
wRC = From FanGraphs, with baserunning removed, after park adjustments
EqBRR = Dan Fox's fielding composite fielding statistics from Baseball Prospectus
Clutch = "Clutchiness" measure from fangraphs; difference between actual WPA and expected WPA based on component statistics.

RA = Actual Runs Allowed, after park adjustment
eRA = Estimated Runs Allowed: tRuns - Field - Catch
ERA = Straight-up Earned Run Average
FIP = Fielding-Independent Runs, based strictly on K-, BB-, and HR-rates. I do park-adjust HR rates.
tERA = Estimated Earned Run Average, a home brew version of Graham McAree's statistic)
tRuns = Pitching Runs Allowed, based on tERA
Field = An average of bUZR from FanGraphs and THT's team fielding statistic (the latter is converted to runs).
Catch = Catcher Fielding Runs, based on SB's, CS's, WP's, PB's, and E's, described here.
BABIP = Straight-up Batting Average on Balls In Play.

COMING UP: Division-by-Division reviews of each team, through the lens of the power rankings!

Team by Team reviews: AL East | AL Central | AL West | NL East | NL Central | NL West

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Comments

SIXTH PLACE, BABY

They should just take the top 8 teams from this ranking and put them in the playoffs.

SteakGrowsOnDmitri: /crams win into mouth
SteakGrowsOnDmitri: mmm, these s**ts taste like unfair an outmoded pitchin metrics

by natalys26 on Oct 11, 2009 9:12 PM EDT reply actions 0 recs

I'm open to year-to-year division restructuring based on TQI. :)

I write at:
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by JinAZ on Oct 11, 2009 10:10 PM EDT up reply actions 0 recs

ROYALS! ROYALS! ROOOOOOOYAAAAALLLLS!

"What we do in life, echoes in eternity!"

by Justin Bopp on Oct 11, 2009 9:28 PM EDT reply actions 0 recs

It's sort of funny to see them ahead of the Giants

I know TQI bumped them for being in the American League, but I honestly felt like that team would be pretty much as bad as they were in the AL if they took on NL competition. At least the Orioles had some excuse, they were mostly running young players in the pitching staff and got one of their key offensive cogs hurt. The Royals were consistent of a bunch of veterans/bad players (a few breached “mediocre”), Zack Greinke, and Billy Butler for most of the year.

Sorry to pile on Justin.

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by SFiercex4 on Oct 11, 2009 10:58 PM EDT up reply actions 0 recs

:(

The NL thing is an old excuse, and I’m not havin’ it here either.

See Data Differently.
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by Justin Bopp on Oct 12, 2009 2:02 AM EDT up reply actions 0 recs

Agreed.

Not that I don’t believe in the obvious difference between the leagues over the last few seasons, but if you’re going to bother making what amounts to a strength of schedule adjustment, it should really be done properly. Lots of work obviously went into setting up the basics of the power ranking system, it’s unfortunate that it’s spoiled by using a simple NL/AL split instead of a more detailed consideration of differing opponent strength due to the unbalanced schedule and inter-league play. Would it really be that shocking if teams playing in the AL central (aggregate .470 W%) actually faced equal or even lesser opposition on the balance than teams in the NL West (aggregate .519 W%)? And yes, I realize that I just used some very simplistic analysis myself, and that developing more sophisticated strength of schedule adjustments is a massive project.

VAE PVTO DEVS FIO

by Bhaakon on Oct 12, 2009 3:05 AM EDT up reply actions 0 recs

bingo

And yes, I realize that I just used some very simplistic analysis myself, and that developing more sophisticated strength of schedule adjustments is a massive project.

I disagree that the effort is “wasted.” My feeling is that we’re probably going to see pretty similar outcomes at the end of a project like you mentioned. If anything, it will probably increase the ridiculousness of the AL East. I can imagine that the AL Central might get a downgrade, but probably not so much as to obliterate the league disparity. That’s my guess, anyway.

But I certainly would love to have complete strength of schedule modifiers. The problem is that you have to do strength of schedule not only based on team winning percentages, but also on W% in interleague. It’s not an easy thing to do. But I may give it a shot for next year, time permitting.
-j

I write at:
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by JinAZ on Oct 12, 2009 9:30 AM EDT up reply actions 0 recs

“Spoiled” or “wasted” may be strong words, but there’s really no way to tell until someone actually does the le work to figure out a strength of schedule more accurately. The difference between actual and estimate run differential is less than 20 runs for some teams, so a flat adjustment might be countering much of the advantage gained by using eW% over pW%

But, like I said, I can’t exactly blame you for not tackling a fairly massive project. Hopefully you’ll have something inplace for next season, though I think it would probably be easier (and more legit) to do a SOS adjustment for a complete season than for one still in progress.

VAE PVTO DEVS FIO

by Bhaakon on Oct 12, 2009 8:49 PM EDT up reply actions 0 recs

There is really no way to do this correctly

The AL and NL might as well be playing different sports, as the difference in rules has such a rippling dynamic effect on everything, and not all in the way people may think. Have you seen the movie π? This reminds me of the attempt to predict the stock market.

"The BB's are out. The BB's are being arseholes to me." - Brian Wilson.

by hairball on Oct 13, 2009 1:11 PM EDT up reply actions 0 recs

Disagree

It helps a lot that the teams from the two leagues play each other, under both sets of rules, many times each year.

It also helps that players move from league to league a lot. MGL’s stuff on that from a few years back is still the best I’ve seen, but I’d love to see that study repeated.
-j

I write at:
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by JinAZ on Oct 13, 2009 10:30 PM EDT up reply actions 0 recs

There's no way to do it perfectly.

That doesn’t mean it can’t be done within a reasonable degree of accuracy.

VAE PVTO DEVS FIO

by Bhaakon on Oct 14, 2009 2:43 AM EDT up reply actions 0 recs

Care you expand on that?

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by Sky Kalkman on Oct 14, 2009 10:15 AM EDT up reply actions 0 recs

Royals have a quality pitching staff.

It’s just that they sort of forgot about the position players…

I mentioned this in my recap of the Orioles (appearing in about 50 min), but they are the biggest reason that I’m thinking about instituting a strength of schedule adjustment. They play four of the top six teams in, what, half of their games? -j

I write at:
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by JinAZ on Oct 12, 2009 9:12 AM EDT up reply actions 0 recs

Interesting, but...

Very flawed. I’m not sure of the exact formula used, but strength of division seems to be HEAVILY over-compensated. Particularly the AL East.

Tampa Bay Rays are at number 2 and they have a losing record vs opponents outside the AL East.

Blue Jays in 6th place, ahead of the rest of the AL. The Jays lost the season series against many teams below them. And while they are above .500 outside the AL East, there are a ton of other clubs with better records outside the AL East.

Also, Royals ahead of Giants? Orioles competing for the NL Central crown? I don’t know.

In MLB, the better team does not win every game. The best team in a division rarely wins more than 2/3rds of the games vs. the worst teams in their division. It seems that teams as bad as the Nationals and Pirates drag the rest of the division down about 5 spots in the rankings.

Still, was interesting.

by YourHero on Oct 12, 2009 6:42 AM EDT reply actions 0 recs

A solution to that last part?

Omit the Nationals and Pirates from all calculations.

Consider them outliers. LOL.

Actually,
Could try omitting the Yankees as well. I bet things would shake up more pleasingly for the rest of the teams. Just auto lock them in at #1.

by YourHero on Oct 12, 2009 6:50 AM EDT up reply actions 0 recs

Actually, Justin does not account for strength of schedule beyond AL/NL.

Playing in the AL East does not give any of those teams a bonus. In fact, if the AL East were the best division in baseball (and the AL West might have something to say about that) then Justin’s method would hurt those five teams.

I’m all for doing “actual” strength of schedule adjustments, but it’s not the easiest thing in the world. Would you account for opposing starting pitchers? The lineups used by the opposition?

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by Sky Kalkman on Oct 12, 2009 7:43 AM EDT up reply actions 0 recs

SoS

I think the most basic way—and probably the purest way—is going to just look at the average W% (or cW%? Or maybe TQI to account for the AL East bias) of a team’s opposition. Then, run it through pythagenpat to estimate their W% against true 0.500 team opposition.
-j

I write at:
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by JinAZ on Oct 12, 2009 9:15 AM EDT up reply actions 0 recs

Yup, I agree.

And I’d use TQI.

Data source for schedules? (That would include rainouts and stuff, too?)

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by Sky Kalkman on Oct 12, 2009 2:35 PM EDT up reply actions 0 recs

Bref has some good pages showing games played vs. each team

You linked to them before. That’d probably be good enough to get games played vs. each team. Then find mean raw TQI for each schedule and use that as the basis for the adjusted TQI. Vlookups & hlookups everywhere…
-j

I write at:
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Twitter: @jinazreds

by JinAZ on Oct 12, 2009 2:54 PM EDT up reply actions 0 recs

HLOOKUPS are the closest statgeeks can get to hooking up. Ba-dum ching!

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by Sky Kalkman on Oct 12, 2009 3:02 PM EDT up reply actions 0 recs

I hate the AL East

So I can assure you that I’m doing nothing to up-weight their ratings. It’s just straight up PythagenPat on estimated runs scored and runs allowed.

The league adjustment is done across the board. At some point I will probably do a breakdown of good/average/bad teams in the AL and NL and see how they compare in interleague to see if your point (which has been echoed before) is right about bad teams pulling down the NL (or, conversely, good teams pulling up the AL). The size of the difference between the leagues is large enough that I’m actually skeptical it could be caused by a few teams in one of the two leagues. But I could be wrong about that.

As for your other critiques—they’re based on actual wins and losses. As you said, there’s a lot of chance that goes into wins and losses. We’re estimating wins and losses based on component statistics. There’s a lot going into those estimates at this point. Offense, baserunning, fielding-independent pitching (tRA, not FIP), two fielding estimates, catcher defense. I’m pretty happy with it right now, frankly.

I’m doing a team-by-team review looking at a) why there are differences between actual and estimated w%, and b) what makes a team tick, which starts today (goes live in ~40 min). It might address some of your concerns.
-j

I write at:
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by JinAZ on Oct 12, 2009 9:24 AM EDT up reply actions 0 recs

Wrote this on the Royals:

cW% (component winning percentage) is the most comparable measure to true team winning percentage. It’s quite simply the pythagorean record based on estimated runs scored & allowed, rather than actual runs scored and allowed.

cW% for the Giants is 0.470. cW% for the Royals is 0.414. So, if you just look at the team or individual numbers, the Giants clearly look like a much better team. The thing is, though, the Royals faced a higher level of competition playing in the AL. So when you adjust for league quality, an adjustment that is based on several convergent lines of data, you end up ballparking that the Royals are roughly an equivalent team to the Giants (there is absolutely no meaningful difference between a TQI of 0.445 and 0.441.)

The Nationals would look awesome if they were playing AAA teams all the time. The NL isn’t as far below the AL as that, but it’s very clearly inferior. Most people acknowledge that qualitatively, but then get upset when we start quantitatively accounting for the league disparity.
http://www.royalsreview.com/2009/10/12/1081386/btb-power-rankings-or-were-no-22#22712012
-j

I write at:
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by JinAZ on Oct 12, 2009 5:02 PM EDT reply actions 0 recs

There is NO way to quantitatively account for league disparity

The rules are different in a super significant way, which essentially makes it a different sport. I fail to see how stats will ever be able to contain all the rippling effects of such a dynamic difference.

"The BB's are out. The BB's are being arseholes to me." - Brian Wilson.

by hairball on Oct 13, 2009 1:16 PM EDT up reply actions 0 recs

My goodness

1. Have you looked at how we’re assessing league disparities? The two main ways are interleague records and studies of what happens when teams change leagues. Both approaches point to roughly the same disparity—about a half win per player season, or about 8 wins team season.

2. I am always amazed at how upset people get over this stuff.
-j

I write at:
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by JinAZ on Oct 13, 2009 10:15 PM EDT up reply actions 0 recs

The study when players change leagues would seem to avoid the issues presented by "rippling DH rule effects".

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by Sky Kalkman on Oct 14, 2009 10:17 AM EDT up reply actions 0 recs

Ugh.....

As if I needed any more proof that we (the Dbacks) were incredibly unlucky and unclutch this season.

I have a shrine dedicated to Mark Reynolds, wherein I keep his bobblehead, signed baseball, and jersey T-shirt.

by DbacksSkins on Oct 13, 2009 11:47 AM EDT reply actions 0 recs

This is where stat analysis gets ridiculous

I mean, sky kalkman mentioned over at McCovey Chronicles that Justin is going to be issuing some more commentary on this, and that he’ll probably address the Giants thing, but it’s obvious as the sky being blue that these rankings leave a lot to be desired. If on TQI-tinted paper, the Giants are this bad, then TQI clearly does not give enough weight to pitching.

The Giants pitchers had a team ERA+ of 120, and a team WHIP under 1.30. They led the league in K, CG, SHO, and runs allowed. That is dominance on a fairly epic level, and when you look at the pitching stats (choose your favorite- they’ll all show the same thing), it’s easy to see why the Giants only narrowly missed the postseason. Anyone who watched them all year could see that while they were a frustrating mess on offense, they were a powerful force on the mound. That is why they finished with the 8th best record in baseball, and why, even adjusted for the AL/NL discrepancy, they were, at the very least, an average or better team, as opposed to the label of “8th worst” that this methodology has resulted in.

"The BB's are out. The BB's are being arseholes to me." - Brian Wilson.

by hairball on Oct 13, 2009 1:04 PM EDT reply actions 0 recs

Giants pitching

You already saw my comments on Cain on the Royals site, I think. His peripherals indicate that he was not as good as his ERA indicates.

Overall, as I calculate the stats, the Giant’s team FIP was 3.93 and their tERA was 4.00. Those measures are independent of fielding and (for the most part) park, and are less volatile to timing-based disruptions than ERA is. Everything you mentioned, with the exception of strikeouts, are subject at least subject to the effects of fielding.

The Giant’s team ERA was 3.53. A 0.5 run per game disparity over 162 games amounts to ~80 runs. About half of that can be explained by their excellent fielding (+40 runs). But the other half of it may just be luck/timing/clutchiness. And those kinds of things tend to not be very repeatable.

Yes, Giants pitching was excellent. But it probably wasn’t as excellent, at least in terms of underlying talent, as some of those other data would suggest.
-j

I write at:
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Red Reporter
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Twitter: @jinazreds

by JinAZ on Oct 13, 2009 10:24 PM EDT up reply actions 0 recs

A quick question, you mention you park-adjust HR rates, what do mean by that? Do you take the HR rate and make an adjustment for park effects (similar to OPS+, etc) or do you regress the HR rate to that expected for that park (similar to xFIP, etc)?

Proud parent of Waldis Joaquin!

by GiantFan on Oct 14, 2009 8:11 AM EDT up reply actions 0 recs

I use Patriot's HR park factors (not runs park factor) to adjust HR totals

And then I plug those adjusted HR totals into the FIP formula. His HR park factors are regressed in a similar manner to his runs park factors, based on the number of years of data we have for a park (up to 5 years, less regression per year of data).

For the Giants it is (looking it up) 0.93, which indicates that the park tends to depress HR totals. So, I’m artificially adding HR’s to the Giant pitchers totals in recognition that they pitch half of their games in a park that depresses HR numbers.

This is only what I do in the FIP numbers I post, which are NOT part of the calculation for estimated runs allowed.

I use a HR park factor correction for tRuns as well, but in that case it’s a park factor generated by David Gassko in his batted balls park factors. That one in particular is based on HR-per-OF-Fly-Ball rates over three years (a much more specific adjustment), and it is regressed “appropriately” (though I’m not aware of how he determined his regression coefficients—sort of have to take his word on it). For the Giants, it’s 0.86, again implying that their park depresses HR numbers.
-j

I write at:
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Twitter: @jinazreds

by JinAZ on Oct 14, 2009 12:38 PM EDT up reply actions 0 recs