2012 Organizational Rankings

It is time to unviel the Dodger Sims 2012 Organizational Rankings. Here is the link to last years rankings. And below is a brief description of the methodology used. I think of these rankings as the MARCEL of rankings. To borrow from Tangotiger who puts out the MARCEL player projections as a bare minimum methodology that any other system should be able to beat, I put out my organizational rankings in the same light. Mine are automated and take in to account the bare minimum data that one should be using to rank the top MLB organizations.

Methodology
1. Recent success and future forecasted success (winning games, making playoffs). This takes into consideration the number of games the team won over the past three years, along with their expected win total from Vegas for the 2012 season, plus a multiplier for number of times the team has made the playoffs over the past three years.
2. Minor league system (Baseball Prospectus)
3. Market Cap / Value of team (Forbes)
4. $Win efficiency (2012 Expected Wins above replacment per millions in projected payroll). The number of wins above 48 that the team is expected to have in the 2012 season (Vegas over/under) divided by its expected 2012 payroll.

Weights
1. 50%
2. 25%
3. 10%
4. 15%


The list will come out in three groups (1 - 10, 11 - 20 and 21 - 30). Here is the grouping of 21-30.

2012 Rank	2011 Rank	Team	Skinny
30	T29	Astros	No recent playoff appearances, poor minor league system and little hope on the horizon.
29	T29	Orioles	A very small step ahead of the Astros. Minor league system has signs of life.
28	20	Indians	2007 was last playoff appearance. Minor league system is poor, but makes decent use out of small payroll.
27	28	Pirates	They don't win much, but average minor league system keeps them out of the cellar.
T25	18	Marlins	Big playoff drought but team is getting better. Minor league system is very bad.
T25	23	Nationals	Team is making strides. Need to start winning more to move up in rankings.
24	17	White Sox	Three year playoff drought and likely worst minor league system.
23	21	Cubs	The only MLB team with a downward trend in wins from 2007 through 2011 (97, 83, 75, 71).
22	22	Mariners	Stuck in the mud but decent minor league system raises hopes.
21	19	Brewers	Decent recent success but trending down with loss of Fielder and below average minor league depth.

Sources:
Fangraphs
Baseball Prospectus
Forbes

The 2011 All-Flub Teams

Recently, Steve Slowinski of Fangraphs authored an article where he listed the 2011 All-Flub Teams. Steve included both an NL and AL team of players who are the kings of underachievers from both leagues. In the article Steve posed the question of which of these two teams would win a head to head matchup. I volunteered to run the two teams through my baseball simulator to find out. I ran two simulations, one with each league's flops as the home team. I selected Sean O'Sullivan as the American League's top flop pitcher and Armando Galarraga as the National League's starting pitcher. Each matchup was simulated 100K times. I also used neutral park factors for all of the simulated games. Below are the results as well as some of the stats.

Away	Home	Starting Pitchers	Favorite	Win Probability	Total Runs
American	National	S.O'Sullivan vs A.Galarraga	National	51.567%	9.69
National	American	A.Galarraga vs S.O'Sullivan	American	56.979%	9.71

Results: The simulator thinks that the American League team is slightly better. If you average the win probability of the away/home series the American League wins 52.706% of the time, with right around 9.7 runs scoring on average. Below are some of the box score stats taken from the matchup where the National League was the home team.

Pitchers Boxscores

Name	IP	SO	BB	Hits	HR	PC	FIP
Sean O'Sullivan	5.697	3.025	1.982	6.841	0.748	91.555	4.888
Jon Rauch	0.445	0.343	0.101	0.465	0.051	6.82	3.819
Mike Gonzalez	0.413	0.364	0.168	0.432	0.063	6.791	4.641
Daniel Schlereth	0.67	0.573	0.352	0.721	0.108	11.458	5.158
Jason Berken	0.419	0.317	0.144	0.471	0.071	6.775	4.912
Adam Russell	0.699	0.365	0.324	0.805	0.058	11.53	4.632
Joe Nathan	0.343	0.298	0.119	0.365	0.052	5.551	4.484
Andy Sonnanstine	0.018	0.009	0.006	0.022	0.003	0.286	5.647
Armando Galarraga	5.808	3.164	2.146	7.073	0.621	94.435	4.609
Ryan Franklin	0.409	0.3	0.085	0.467	0.061	6.337	4.298
Michael Dunn	0.429	0.445	0.193	0.414	0.044	7.149	3.8
Pat Neshek	0.712	0.564	0.367	0.784	0.087	12.131	4.745
Sean Burnett	0.483	0.351	0.168	0.535	0.045	7.771	3.996
Miguel Batista	0.97	0.637	0.46	1.122	0.086	16.277	4.461
John Grabow	0.354	0.228	0.158	0.42	0.044	5.916	4.856
Aneury Rodriguez	0.007	0.005	0.002	0.008	0.001	0.114	4.366

Hitters Boxscore

Name	AB	Hits	H1	H2	H3	HR	RBI	BB	SO	wOBA	BABIP
Juan Pierre	4.57	1.406	1.138	0.17	0.039	0.06	0.399	0.42	0.371	0.343	0.325
Chone Figgins	4.389	1.368	0.959	0.324	0.02	0.064	0.466	0.406	0.607	0.357	0.351
Alex Rios	4.457	1.374	0.918	0.323	0.019	0.114	0.59	0.279	0.54	0.354	0.331
Justin Morneau	4.2	1.27	0.773	0.327	0.011	0.16	0.673	0.481	0.588	0.379	0.322
Adam Dunn	3.923	1.013	0.497	0.29	0.004	0.221	0.694	0.651	1.178	0.375	0.314
David Murphy	3.962	1.218	0.813	0.237	0.02	0.148	0.601	0.491	0.589	0.382	0.332
Orlando Cabrera	4.08	1.168	0.778	0.297	0.013	0.081	0.503	0.221	0.47	0.327	0.308
Cliff Pennington	3.866	1.092	0.754	0.239	0.036	0.063	0.449	0.325	0.64	0.331	0.325
Jeff Mathis	3.785	0.915	0.594	0.238	0.006	0.077	0.429	0.305	0.711	0.295	0.28
Mike Cameron	4.31	1.245	0.694	0.375	0.043	0.133	0.497	0.381	0.615	0.36	0.312
Ryan Spilborghs	4.129	1.226	0.76	0.293	0.017	0.157	0.538	0.442	0.671	0.371	0.324
Aubrey Huff	4.032	1.151	0.719	0.246	0.029	0.158	0.56	0.509	0.549	0.368	0.299
Chris Johnson	4.157	1.231	0.766	0.317	0.031	0.118	0.589	0.23	0.733	0.348	0.337
Raul Ibanez	3.88	1.132	0.672	0.272	0.028	0.16	0.614	0.447	0.623	0.374	0.314
Casey McGehee	3.832	1.113	0.69	0.28	0.004	0.139	0.561	0.331	0.548	0.356	0.31
Bill Hall	3.761	1.037	0.59	0.298	0.034	0.115	0.498	0.296	0.697	0.343	0.313
Yuniesky Betancourt	3.761	1.067	0.699	0.242	0.034	0.093	0.464	0.187	0.364	0.329	0.295
Dioner Navarro	3.473	0.918	0.592	0.227	0.019	0.081	0.398	0.373	0.493	0.33	0.289

And here is a bar chart showing how the run totals are distributed. The x-axis the total runs scored in the game and the y-axis is how many occurrences this happened. As you can tell from the chart a run total of 7 and 9 runs is the most common occurrence.

Organizational Rankings

Inspired by the second annual MLB Organization Rankings done by Fangraphs, I have decided to calculate my own. Last year famously, Fangraphs took a lot of deserved heat for ranking the Seattle Mariners, the laughing stock of baseball in 2010, as the #6Org. There was much uproar about the validity of a system that could rank them so high. While the 2011 list from Fangraphs is just underway, they have already raised my eyebrows by ranking the Dodgers as the #23Org. So I took it upon myself to see how difficult it would be do come up with a more sensible organizational ranking system. Not only that, but I've made my spreadsheet used to come up with my rankings a public google document.

Of course I don't have the hoards of databases and "expert" writers at my disposal to create such a ranking, but I feel I have done as good of job in the 60 minutes time it took me to slap together this ranking system. Below are the inputs and weightings used.

Methodology
1. Recent success and future forecasted success (winning games, making playoffs). This takes into consideration the number of games the team won over the past three years, along with their expected win total from Vegas for the 2011 season, plus a multiplier for number of times the team has made the playoffs over the past three years.
2. Minor league system (Baseball Prospectus)
3. Market Cap / Value of team (Forbes)
4. $Win efficiency (2011 Expected Wins above replacment per millions in projected payroll). The number of wins above 48 that the team is expected to have in the 2011 season (Vegas over/under) divided by its expected 2011 payroll.

Weights
1. 50%
2. 25%
3. 10%
4. 15%

With that, here is the list...

Team	Recent Success	Minors	Market Cap	Efficiency Rank	Weighted Final Average	Final Rank
PHI	1	8	6	20	6.1	1
TB	4	2	28	6	6.2	2
NYY	2	4	1	29	6.45	3
TEX	6	10	8	4	6.9	4
ATL	8	3	13	10	7.55	5
MIN	5	15	12	14	9.55	6
SF	7	22	7	1	9.85	7
LAA	9	6	9	21	10.05	8
BOS	3	19	2	26	10.35	9
CIN	13	9	23	8	12.25	10
COL	11	14	17	11	12.35	11
LAN	12	18	3	17	13.35	12
STL	10	21	11	16	13.75	13
TOR	20	5	27	9	15.3	14
SD	21	11	19	2	15.45	15
OAK	18	16	29	3	16.35	16
CHA	14	25	10	22	17.55	17
FLA	15	29	24	5	17.9	18
MIL	16	30	22	12	19.5	19
CLE	26	7	25	19	20.1	20
CHN	19	23	4	30	20.15	21
SEA	24	13	15	23	20.2	22
WAS	28	12	16	15	20.85	23
DET	17	27	21	25	21.1	24
KC	29	1	26	27	21.4	25
NYN	22	24	5	28	21.7	26
ARI	27	20	20	13	22.45	27
PIT	30	17	30	7	23.3	28
HOU	23	28	14	24	23.5	29
BAL	25	26	18	18	23.5	29

Sources:
Baseball Prosectus
Forbes
MLB Contracts

AL Zero to Three VS NL Zero to Three

In case you haven't been following the series over at FanGraphs, Jonah Keri has put together an AL and NL All-Star team from players with less than three years of major league service, who are making at or near league minimum. The NL Zero to Three team was posted first, followed by the AL Zero to Three team. I volunteered to run a simulation between the two rosters to see which was the stronger team.

Here are the results...

Away	Home	Away SP	Home SP	Favorite	Win Probability
NL	AL	Clayton Kershaw	Clay Buccholz	AL	50.509
AL	NL	Clay Buchholz	Clayton Kershaw	NL	61.487
NL	AL	Tommy Hanson	Justin Masterson	AL	52.045
AL	NL	Justin Masterson	Tommy Hanson	NL	58.186
NL	AL	Mat Latos	Kyle Drabek	NL	57.194
AL	NL	Kyle Drabek	Mat Latos	NL	67.676
NL	AL	Travis Wood	Gio Gonzalez	AL	53.619
AL	NL	Gio Gonzalez	Travis Wood	NL	55.999
NL	AL	Jaime Garcia	Doug Fister	AL	52.062
AL	NL	Doug Fister	Jaime Garcia	NL	57.789

As expected the NL team dominates this simulation. Their average win probability over the 10 games is 55.01%. If you put that in per 162 games, the NL team would win 89.12 games.