Oakland Athletics - 2015 Most Optimal Lineups

There are many ways people attempt to determine the best batting order.  Some people try to arrange high on base percentage people at the top of the lineup followed by sluggers and then the rest of the crew and slap on a pitcher batting 9th (NL Team) and you are good to go.  Others try calculators where you enter in player stats and out pops the best lineups.  These are all fun and probably get you part of the way there but they are in NO WAY scientific enough to make much of a claim to what the top lineups might be.

The best way (in my opinion) to determine which lineup is the best is to actually trot out every single lineup and see which one wins the most games.  Of course you would have to hold the opponent constant, using the same starters, lineup and starting pitcher.  How hard could that be to do?  Let's see, with nine batters in a lineup you would only have 362,880 lineups to go through.  How long could that take to play over 350,000 baseball games.  You could probably filter those permutations down to 25,000 or so by limiting left handed batters from hitting back to back, limiting the pitcher (NL) to batting either 8th or 9th, plus a few other obvious things.  Even if you could narrow things down to just two lineups, you would still need to play more than one game to determine which one was better.  So how many samples/games would you need?  10? 20? or maybe an entire season (162) of games?  No.  You would need a lot more than that.  Try tens of thousands just to separate the bad lineups from the good and then try a million or two to compare the best lineups.

Well, I think it is pretty impossible to play thousands of games one time or a couple of games a million times.  Hypothetically, if one were able to play games with thousands of different lineups, thousands or millions of times against the exact same opponent then you would get a very good idea as to which lineups were the best.  This is where simulation comes in handy.  A simulator that could play actual baseball games, by actual baseball rules, taking into consideration the important facets of a real baseball game like speed, defense, pitchers getting tired the more pitches they throw, splits, hitter vs pitcher matchups and outcomes and throw in some good player projections like the ones available for free at Fangraphs and then maybe through the power of the computer you could plow through tens of thousands of likely lineups to determine the best ones.

Now, you would be able to test the synergy of batting certain groups of players next to each other.  You would be able to experiment with various types of hitters hitting in different spots.  In fact, you could just let the computer run through each lineup and let you know what the top ones were, even tabulating how many times each player showed up at each spot in the top lineups.  Now you would have a good list of top lineups and be able to measure how many wins a manager might be leaving on the table by using the lineup(s) that he used.  Wouldn't it be very worthwhile for a team to know what its best lineups might be?  Of course it would, especially considering that on the free agent market one win (or WAR) is typically worth around $7M.  For managers that might not want to hit certain batters in certain lineup spots for whatever reason, he could use the filtering option to only measure lineups that fit his parameters.

Well, now on to what my game simulator using Steamer hitting projections lists as the top fifty lineups for the 2015 Oakland Athletics.  I only looked at lineups against a right handed starting pitcher and took the nine most likely starters from the list at the Roster Resource website and I have of course used their most common lineup as one of the many (10,000+) lineups that I am looking at.  Along with the top fifty lineups, there is a table showing how many times each player shows up in each lineup spot and what the most common back to back slottings are as well as back to back to back slottings (think synergy here).

Summary:
The batting order frequency table tells a lot here.  If you were to piece together a lineup from only the frequency table you would likely come up with what is the second ranked optimal lineup.  The lineup listed from the Roster Resource website came in listed 34th.  I've ran a few of these now for various teams and the Athletics seem to have a very balanced lineup with no one player way better than the others not causing much of a spread between the top lineups.  I also purposely added a bad lineup to the bottom of the top 50 list just to see what the maximum spread for the team looks like.  According to the simulator, there are not a lot of wins for the Athletics manager to leave on the table vs RHP compared to some of the other teams.

                                          Top 50 Lineups (vs RHP)

Rank	Lineup	Wins/162 Games Behind
1	Zobrist-Reddick-Lawrie-Davis-Crisp-Semien-Vogt-Butler-Fuld	0
2	Crisp-Reddick-Lawrie-Davis-Zobrist-Vogt-Semien-Butler-Fuld	0.001
3	Reddick-Crisp-Lawrie-Davis-Zobrist-Vogt-Semien-Fuld-Butler	0.016
4	Crisp-Vogt-Lawrie-Davis-Zobrist-Reddick-Semien-Butler-Fuld	0.042
5	Zobrist-Fuld-Lawrie-Reddick-Crisp-Vogt-Semien-Butler-Davis	0.067
6	Crisp-Vogt-Lawrie-Davis-Zobrist-Semien-Fuld-Butler-Reddick	0.093
7	Fuld-Crisp-Butler-Davis-Zobrist-Reddick-Lawrie-Vogt-Semien	0.094
8	Davis-Zobrist-Lawrie-Reddick-Crisp-Vogt-Semien-Fuld-Butler	0.103
9	Reddick-Semien-Lawrie-Davis-Crisp-Fuld-Zobrist-Vogt-Butler	0.107
10	Zobrist-Reddick-Lawrie-Davis-Semien-Crisp-Vogt-Butler-Fuld	0.108
11	Lawrie-Crisp-Davis-Zobrist-Reddick-Semien-Vogt-Butler-Fuld	0.108
12	Semien-Davis-Lawrie-Reddick-Crisp-Fuld-Zobrist-Vogt-Butler	0.109
13	Zobrist-Reddick-Lawrie-Davis-Crisp-Fuld-Butler-Vogt-Semien	0.117
14	Crisp-Reddick-Lawrie-Davis-Semien-Zobrist-Fuld-Butler-Vogt	0.123
15	Zobrist-Crisp-Reddick-Lawrie-Davis-Semien-Fuld-Butler-Vogt	0.14
16	Semien-Davis-Lawrie-Reddick-Zobrist-Crisp-Vogt-Butler-Fuld	0.141
17	Semien-Fuld-Butler-Davis-Zobrist-Reddick-Lawrie-Crisp-Vogt	0.146
18	Zobrist-Reddick-Lawrie-Davis-Crisp-Vogt-Semien-Fuld-Butler	0.146
19	Reddick-Crisp-Davis-Zobrist-Lawrie-Vogt-Semien-Fuld-Butler	0.152
20	Fuld-Crisp-Butler-Davis-Semien-Reddick-Lawrie-Vogt-Zobrist	0.156
21	Zobrist-Fuld-Lawrie-Davis-Crisp-Vogt-Semien-Butler-Reddick	0.156
22	Reddick-Zobrist-Davis-Lawrie-Crisp-Vogt-Semien-Fuld-Butler	0.159
23	Crisp-Butler-Reddick-Lawrie-Davis-Semien-Vogt-Zobrist-Fuld	0.175
24	Reddick-Crisp-Davis-Lawrie-Zobrist-Vogt-Semien-Fuld-Butler	0.181
25	Fuld-Crisp-Lawrie-Davis-Zobrist-Reddick-Semien-Vogt-Butler	0.191
26	Reddick-Zobrist-Lawrie-Davis-Semien-Fuld-Crisp-Vogt-Butler	0.192
27	Crisp-Zobrist-Reddick-Lawrie-Davis-Semien-Fuld-Butler-Vogt	0.192
28	Crisp-Davis-Lawrie-Reddick-Zobrist-Vogt-Semien-Butler-Fuld	0.195
29	Crisp-Fuld-Lawrie-Reddick-Zobrist-Semien-Vogt-Butler-Davis	0.204
30	Crisp-Semien-Davis-Lawrie-Reddick-Zobrist-Fuld-Butler-Vogt	0.209
31	Crisp-Fuld-Lawrie-Davis-Zobrist-Reddick-Butler-Semien-Vogt	0.210
32	Crisp-Zobrist-Davis-Lawrie-Fuld-Semien-Vogt-Butler-Reddick	0.213
33	Semien-Fuld-Lawrie-Reddick-Zobrist-Crisp-Vogt-Butler-Davis	0.214
34	Reddick-Crisp-Davis-Zobrist-Vogt-Lawrie-Semien-Fuld-Butler	0.218
35	Crisp-Zobrist-Reddick-Butler-Davis-Lawrie-Vogt-Semien-Fuld	0.221
36	Reddick-Zobrist-Davis-Lawrie-Fuld-Semien-Crisp-Vogt-Butler	0.222
37	Crisp-Davis-Butler-Reddick-Zobrist-Lawrie-Vogt-Semien-Fuld	0.236
38	Semien-Davis-Zobrist-Reddick-Crisp-Vogt-Lawrie-Fuld-Butler	0.238
39	Crisp-Vogt-Lawrie-Davis-Semien-Butler-Reddick-Zobrist-Fuld	0.242
40	Crisp-Zobrist-Reddick-Lawrie-Vogt-Semien-Fuld-Butler-Davis	0.244
41	Zobrist-Reddick-Lawrie-Davis-Crisp-Fuld-Butler-Semien-Vogt	0.248
42	Crisp-Butler-Reddick-Lawrie-Davis-Zobrist-Vogt-Semien-Fuld	0.257
43	Lawrie-Fuld-Butler-Davis-Crisp-Zobrist-Vogt-Semien-Reddick	0.258
44	Crisp-Reddick-Lawrie-Davis-Semien-Fuld-Butler-Zobrist-Vogt	0.260
45	Davis-Zobrist-Reddick-Lawrie-Fuld-Crisp-Vogt-Semien-Butler	0.263
46	Crisp-Fuld-Butler-Davis-Lawrie-Reddick-Semien-Vogt-Zobrist	0.267
47	Crisp-Reddick-Butler-Davis-Semien-Zobrist-Fuld-Lawrie-Vogt	0.283
48	Fuld-Semien-Lawrie-Davis-Crisp-Reddick-Zobrist-Vogt-Butler	0.284
49	Crisp-Vogt-Zobrist-Davis-Lawrie-Reddick-Semien-Butler-Fuld	0.294
50	Crisp-Reddick-Butler-Davis-Zobrist-Lawrie-Vogt-Semien-Fuld	0.294
BAD	Vogt-Butler-Fuld-Crisp-Semien-Davis-Zobrist-Reddick-Lawrie	1.419

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             Batting Order Frequency Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Crisp	21	9	0	0	13	4	2	1	0
Reddick	8	10	7	9	2	9	1	0	4
Lawrie	2	0	25	11	3	4	4	1	0
Davis	2	5	8	26	5	0	0	0	4
Zobrist	8	9	2	3	15	5	3	3	2
Vogt	0	4	0	0	2	11	14	10	9
Semien	5	3	0	0	7	10	15	8	2
Butler	0	2	8	1	0	1	4	19	15
Fuld	4	8	0	0	3	6	7	8	14

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                    Synergy

   Back to Back Frequency Leaders

Rank	Occurrences	Player 1	Player 2
1	22	Lawrie	Davis
2	20	Vogt	Semien
3	20	Semien	Fuld
4	20	Fuld	Butler
5	18	Reddick	Lawrie
6	17	Crisp	Vogt
7	17	Davis	Zobrist
8	16	Zobrist	Reddick
9	15	Butler	Reddick
10	15	Fuld	Crisp
11	14	Butler	Davis
12	13	Vogt	Butler
13	12	Reddick	Zobrist
14	11	Davis	Lawrie
15	11	Davis	Semien
16	11	Zobrist	Vogt
17	10	Reddick	Crisp
18	10	Lawrie	Reddick
19	10	Davis	Crisp
20	10	Butler	Fuld

     Back To Back to Back Frequency Leaders

Rank	Occurrences	Player 1	Player 2	Player 3
1	13	Vogt	Semien	Fuld
2	13	Semien	Fuld	Butler
3	12	Reddick	Lawrie	Davis
4	10	Zobrist	Reddick	Lawrie
5	8	Lawrie	Davis	Semien
6	8	Davis	Zobrist	Reddick
7	7	Crisp	Vogt	Semien
8	7	Lawrie	Davis	Crisp
9	7	Lawrie	Davis	Zobrist
10	6	Lawrie	Vogt	Semien
11	6	Davis	Lawrie	Reddick
12	6	Zobrist	Vogt	Semien
13	6	Vogt	Butler	Fuld
14	6	Semien	Fuld	Crisp
15	6	Butler	Reddick	Crisp
16	6	Butler	Reddick	Zobrist
17	6	Butler	Davis	Zobrist
18	6	Fuld	Butler	Reddick
19	5	Crisp	Vogt	Butler
20	5	Lawrie	Reddick	Zobrist

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Miami Marlins - 2015 Most Optimal Lineups

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I am using my simulator that plays actual baseball games to find what it believes are the most optimal lineups for the 2015 Miami Marlins (vs RHP).  I started out with 15,000 almost random lineups and I slowly widdled it down to the top 50 lineups.  Almost random, because I filtered out some obvious things like batting Stanton 8th or 9th or Dee Gordon cleanup etc...  For the starting nine players, I used the projected starting lineup from MLB Depth Charts The top 50 lineups all came within 0.85 wins per 162 games which is a pretty wide range for a top 50 lineup list.  When you start nearing a full win differential with the top lineup then you definitely have problems and the projected lineup from MLB Depth Charts finished 1.65 wins/162 games behind (not good).  You will see a few lineups that may seem strange in a most optimal list but keep in mind the simulator does not take into account psychological factors, such as rookies not being allowed to hit near the top of the lineup.  The simulator just takes the Steamer projection inputs along with a speed rating for base running and gives you the best lineups.  The simulator is a program that I wrote in C/C++ and I have back tested it against Vegas odds in the past to make sure it is good.  The program plays actual games, with actual baseball rules and takes pretty much everything you can think of into consideration.  This exercise is NOT trying to project what lineups the Marlins manager might or will actually use it is just outputting what it thinks are the best lineups.  Once down to the top fifty lineups, I simulated each lineup 2 million times which eliminated a large portion of the random noise and I did so only against a right handed pitcher.  Below are a list of the top 50 lineups and a batting order spot frequency table, where you can see how many times each player appeared in each of the top 50 lineups.

                                                             Top 50 Lineups

Rank	Lineup	Wins/162 Games Behind
1	Yelich-Gordon-Prado-Stanton-Ozuna-Morse-Salta-Pitcher-Hechavarria	0
2	Gordon-Yelich-Prado-Stanton-Morse-Ozuna-Salta-Pitcher-Hechavarria	0.232
3	Yelich-Gordon-Prado-Stanton-Morse-Ozuna-Salta-Pitcher-Hechavarria	0.250
4	Gordon-Yelich-Prado-Stanton-Ozuna-Hechavarria-Morse-Salta-Pitcher	0.308
5	Gordon-Yelich-Prado-Ozuna-Stanton-Morse-Salta-Pitcher-Hechavarria	0.359
6	Yelich-Gordon-Prado-Stanton-Ozuna-Morse-Hechavarria-Salta-Pitcher	0.374
7	Yelich-Gordon-Prado-Stanton-Ozuna-Hechavarria-Salta-Pitcher-Morse	0.384
8	Yelich-Gordon-Prado-Ozuna-Stanton-Morse-Salta-Pitcher-Hechavarria	0.386
9	Gordon-Yelich-Stanton-Prado-Ozuna-Morse-Salta-Pitcher-Hechavarria	0.392
10	Yelich-Gordon-Stanton-Prado-Ozuna-Morse-Salta-Pitcher-Hechavarria	0.438
11	Gordon-Yelich-Prado-Stanton-Ozuna-Morse-Hechavarria-Salta-Pitcher	0.443
12	Gordon-Yelich-Prado-Stanton-Ozuna-Hechavarria-Salta-Pitcher-Morse	0.448
13	Ozuna-Gordon-Prado-Stanton-Yelich-Hechavarria-Morse-Salta-Pitcher	0.491
14	Yelich-Gordon-Prado-Morse-Stanton-Ozuna-Salta-Pitcher-Hechavarria	0.498
15	Gordon-Prado-Yelich-Stanton-Ozuna-Morse-Salta-Pitcher-Hechavarria	0.525
16	Gordon-Yelich-Prado-Morse-Stanton-Ozuna-Salta-Pitcher-Hechavarria	0.541
17	Yelich-Gordon-Stanton-Prado-Morse-Ozuna-Salta-Pitcher-Hechavarria	0.543
18	Gordon-Hechavarria-Prado-Stanton-Yelich-Ozuna-Morse-Salta-Pitcher	0.546
19	Yelich-Gordon-Stanton-Prado-Ozuna-Hechavarria-Morse-Salta-Pitcher	0.553
20	Yelich-Gordon-Stanton-Prado-Ozuna-Hechavarria-Salta-Pitcher-Morse	0.574
21	Gordon-Prado-Stanton-Yelich-Ozuna-Morse-Salta-Pitcher-Hechavarria	0.584
22	Gordon-Ozuna-Prado-Stanton-Yelich-Morse-Salta-Pitcher-Hechavarria	0.584
23	Yelich-Gordon-Prado-Stanton-Morse-Hechavarria-Ozuna-Salta-Pitcher	0.594
24	Gordon-Prado-Stanton-Ozuna-Yelich-Morse-Salta-Pitcher-Hechavarria	0.605
25	Gordon-Yelich-Ozuna-Prado-Stanton-Morse-Salta-Pitcher-Hechavarria	0.613
26	Gordon-Prado-Ozuna-Stanton-Yelich-Morse-Salta-Pitcher-Hechavarria	0.633
27	Gordon-Yelich-Stanton-Prado-Ozuna-Hechavarria-Morse-Salta-Pitcher	0.641
28	Gordon-Yelich-Morse-Prado-Stanton-Ozuna-Salta-Pitcher-Hechavarria	0.642
29	Gordon-Yelich-Prado-Stanton-Morse-Ozuna-Hechavarria-Salta-Pitcher	0.648
30	Gordon-Prado-Yelich-Stanton-Morse-Ozuna-Salta-Pitcher-Hechavarria	0.652
31	Ozuna-Gordon-Prado-Stanton-Yelich-Morse-Hechavarria-Salta-Pitcher	0.653
32	Prado-Gordon-Yelich-Stanton-Ozuna-Morse-Salta-Pitcher-Hechavarria	0.659
33	Gordon-Hechavarria-Prado-Stanton-Yelich-Ozuna-Salta-Pitcher-Morse	0.674
34	Yelich-Gordon-Stanton-Prado-Ozuna-Morse-Hechavarria-Salta-Pitcher	0.678
35	Yelich-Gordon-Ozuna-Prado-Stanton-Morse-Salta-Pitcher-Hechavarria	0.685
36	Gordon-Morse-Prado-Stanton-Yelich-Ozuna-Hechavarria-Salta-Pitcher	0.688
37	Gordon-Yelich-Prado-Stanton-Morse-Hechavarria-Salta-Pitcher-Ozuna	0.704
38	Ozuna-Gordon-Prado-Yelich-Stanton-Morse-Salta-Pitcher-Hechavarria	0.723
39	Ozuna-Hechavarria-Prado-Stanton-Yelich-Gordon-Morse-Salta-Pitcher	0.725
40	Gordon-Morse-Prado-Stanton-Yelich-Ozuna-Salta-Pitcher-Hechavarria	0.727
41	Gordon-Yelich-Stanton-Prado-Ozuna-Morse-Hechavarria-Salta-Pitcher	0.735
42	Yelich-Gordon-Prado-Stanton-Ozuna-Morse-Hechavarria-Pitcher-Salta	0.741
43	Gordon-Prado-Morse-Stanton-Yelich-Ozuna-Salta-Pitcher-Hechavarria	0.758
44	Gordon-Yelich-Stanton-Ozuna-Prado-Morse-Salta-Pitcher-Hechavarria	0.770
45	Yelich-Gordon-Prado-Ozuna-Morse-Stanton-Salta-Pitcher-Hechavarria	0.771
46	Prado-Gordon-Stanton-Ozuna-Yelich-Morse-Salta-Pitcher-Hechavarria	0.775
47	Yelich-Gordon-Morse-Stanton-Prado-Ozuna-Salta-Pitcher-Hechavarria	0.775
48	Yelich-Gordon-Prado-Stanton-Ozuna-Salta-Morse-Pitcher-Hechavarria	0.799
49	Gordon-Yelich-Prado-Morse-Ozuna-Stanton-Salta-Pitcher-Hechavarria	0.810
50	Gordon-Hechavarria-Prado-Stanton-Ozuna-Yelich-Morse-Salta-Pitcher	0.855
MLBDC	Gordon-Yelich-Stanton-Morse-Prado-Ozuna-Salta-Hechavarria-pitcher	1.648
BAD	Salta-Hechavarria-Morse-Gordon-Ozuna-Prado-Yelich-Stanton-Pitcher	4.400

                  Batting Order Frequency Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Gordon	27	22	0	0	0	1	0	0	0
Yelich	17	15	3	2	12	1	0	0	0
Prado	2	6	29	11	2	0	0	0	0
Stanton	0	0	12	28	8	2	0	0	0
Ozuna	4	1	3	6	20	14	1	0	1
Morse	0	2	3	3	8	22	8	0	4
Salta	0	0	0	0	0	1	33	15	1
Hechavarria	0	4	0	0	0	9	8	0	29
Pitcher	0	0	0	0	0	0	0	35	15

                    Synergy

20 Most Common Back to Back Occurrences

Rank	Occurrences	Player 1	Player 2
1	48	Salta	Pitcher
2	29	Pitcher	Hechavarria
3	27	Prado	Stanton
4	23	Morse	Salta
5	19	Gordon	Prado
6	18	Yelich	Gordon
7	17	Stanton	Ozuna
8	16	Gordon	Yelich
9	16	Hechavarria	Gordon
10	13	Ozuna	Morse
11	13	Ozuna	Salta
12	12	Prado	Ozuna
13	11	Stanton	Yelich
14	11	Stanton	Morse
15	11	Hechavarria	Salta
16	10	Hechavarria	Yelich
17	9	Yelich	Prado
18	9	Stanton	Prado
19	9	Ozuna	Hechavarria
20	8	Yelich	Stanton
20	8	Morse	Hechavarria
20	8	Pitcher	Gordon

20 Most Common Back to Back to Back Occurrences

Rank	Occurrences	Player 1	Player 2	Player 3
1	28	Salta	Pitcher	Hechavarria
2	23	Morse	Salta	Pitcher
3	16	Pitcher	Hechavarria	Gordon
4	12	Ozuna	Salta	Pitcher
5	11	Gordon	Prado	Stanton
6	11	Prado	Stanton	Ozuna
7	11	Hechavarria	Salta	Pitcher
8	10	Yelich	Gordon	Prado
9	10	Hechavarria	Yelich	Gordon
10	10	Pitcher	Hechavarria	Yelich
11	9	Gordon	Yelich	Prado
12	9	Prado	Stanton	Yelich
13	8	Stanton	Prado	Ozuna
14	8	Salta	Pitcher	Gordon
15	8	Hechavarria	Gordon	Yelich
16	7	Prado	Stanton	Morse
17	7	Ozuna	Morse	Salta
18	6	Yelich	Prado	Stanton
19	6	Stanton	Yelich	Ozuna
20	6	Stanton	Ozuna	Morse
20	6	Morse	Hechavarria	Salta
20	6	Hechavarria	Gordon	Prado

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Minnesota Twins - 2015 Most Optimal Lineups

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I am using my simulator that plays actual baseball games to find what it believes are the most optimal lineups for the 2015 Minnesota Twins.  I started out with 25,000 almost random lineups and I slowly widdled it down to the top 50 lineups.  Almost random, because I filtered out some obvious things like batting Joe Mauer 8th or 9th etc...  For the starting nine players, I used the projected starting lineup from MLB Depth Charts The top 50 lineups all came within 0.35 wins per 162 games so really anything in that range is pretty good.  When you start nearing a full win differential with the top lineup then you definitely have problems and the projected lineup from MLB Depth Charts finished 0.59 wins/162 games behind (not bad).  You will see a few lineups that may seem strange in a most optimal list but keep in mind the simulator does not take into account psychological factors, such as rookies not being allowed to hit near the top of the lineup.  The simulator just takes the Steamer projection inputs along with a speed rating for base running and gives you the best lineups. This exercise is NOT trying to project what lineups the Twins manager might or will actually use it is just outputting what it thinks are the best lineups.  I typically simulated each lineup over 1 million times which eliminated a large portion of the random noise and I did so only against a right handed pitcher.  Below are a list of the top 50 lineups and a batting order spot frequency table, where you can see how many times each player appeared in each of the top 50 lineups.  The Twins have two left handed batters in the starting nine that I used.  For this exercise, I assumed that there was no restriction on batting the two left handed batters (Mauer, Arcia) back to back.  Obviously, if you add that restriction then 33 of these 50 lineups would be different.

                                               Top 50 Lineups (vs RHP)

Rank	Lineup	Wins/162 Games Behind
1	Santana-Mauer-Arcia-Vargas-Hunter-Plouffe-Hicks-Suzuki-Dozier	0
2	Santana-Mauer-Vargas-Arcia-Hunter-Plouffe-Hicks-Suzuki-Dozier	0.013
3	Santana-Mauer-Arcia-Vargas-Hunter-Dozier-Plouffe-Suzuki-Hicks	0.018
4	Santana-Mauer-Vargas-Arcia-Dozier-Suzuki-Plouffe-Hicks-Hunter	0.027
5	Santana-Mauer-Vargas-Arcia-Plouffe-Hunter-Hicks-Suzuki-Dozier	0.033
6	Dozier-Santana-Mauer-Arcia-Vargas-Hicks-Suzuki-Plouffe-Hunter	0.048
7	Santana-Mauer-Arcia-Vargas-Dozier-Hunter-Plouffe-Hicks-Suzuki	0.055
8	Santana-Mauer-Arcia-Vargas-Plouffe-Hicks-Hunter-Dozier-Suzuki	0.061
9	Hunter-Santana-Mauer-Arcia-Vargas-Dozier-Plouffe-Suzuki-Hicks	0.087
10	Santana-Mauer-Arcia-Vargas-Hunter-Hicks-Plouffe-Suzuki-Dozier	0.097
11	Dozier-Santana-Mauer-Arcia-Vargas-Hunter-Hicks-Suzuki-Plouffe	0.101
12	Hicks-Santana-Mauer-Vargas-Arcia-Hunter-Plouffe-Dozier-Suzuki	0.105
13	Dozier-Santana-Mauer-Arcia-Vargas-Hicks-Hunter-Plouffe-Suzuki	0.106
14	Dozier-Santana-Mauer-Vargas-Arcia-Plouffe-Hunter-Suzuki-Hicks	0.109
15	Dozier-Mauer-Vargas-Arcia-Hicks-Santana-Suzuki-Hunter-Plouffe	0.113
16	Santana-Mauer-Arcia-Vargas-Plouffe-Dozier-Suzuki-Hicks-Hunter	0.117
17	Santana-Mauer-Vargas-Arcia-Hicks-Suzuki-Plouffe-Dozier-Hunter	0.119
18	Santana-Mauer-Arcia-Vargas-Hicks-Plouffe-Dozier-Suzuki-Hunter	0.120
19	Santana-Hunter-Vargas-Mauer-Arcia-Dozier-Plouffe-Suzuki-Hicks	0.123
20	Santana-Vargas-Mauer-Arcia-Hunter-Dozier-Suzuki-Plouffe-Hicks	0.136
21	Santana-Suzuki-Mauer-Arcia-Vargas-Plouffe-Hunter-Dozier-Hicks	0.143
22	Hunter-Santana-Mauer-Vargas-Arcia-Plouffe-Hicks-Dozier-Suzuki	0.147
23	Santana-Hunter-Vargas-Mauer-Arcia-Dozier-Plouffe-Hicks-Suzuki	0.151
24	Hunter-Santana-Vargas-Mauer-Arcia-Plouffe-Hicks-Suzuki-Dozier	0.155
25	Santana-Vargas-Mauer-Arcia-Hicks-Suzuki-Hunter-Plouffe-Dozier	0.160
26	Santana-Mauer-Vargas-Arcia-Hicks-Dozier-Suzuki-Plouffe-Hunter	0.161
27	Santana-Hunter-Vargas-Mauer-Arcia-Plouffe-Dozier-Suzuki-Hicks	0.183
28	Hicks-Mauer-Vargas-Arcia-Dozier-Santana-Suzuki-Plouffe-Hunter	0.186
29	Hunter-Santana-Vargas-Mauer-Arcia-Plouffe-Suzuki-Hicks-Dozier	0.193
30	Santana-Mauer-Vargas-Arcia-Dozier-Suzuki-Hicks-Plouffe-Hunter	0.197
31	Santana-Vargas-Mauer-Arcia-Dozier-Hicks-Hunter-Plouffe-Suzuki	0.200
32	Santana-Vargas-Mauer-Arcia-Dozier-Hunter-Suzuki-Hicks-Plouffe	0.209
33	Santana-Mauer-Vargas-Hunter-Arcia-Plouffe-Dozier-Suzuki-Hicks	0.211
34	Hicks-Santana-Hunter-Mauer-Arcia-Vargas-Dozier-Suzuki-Plouffe	0.222
35	Santana-Vargas-Mauer-Arcia-Hicks-Plouffe-Hunter-Dozier-Suzuki	0.226
36	Dozier-Santana-Vargas-Mauer-Arcia-Plouffe-Suzuki-Hunter-Hicks	0.229
37	Santana-Vargas-Hunter-Mauer-Arcia-Hicks-Plouffe-Dozier-Suzuki	0.230
38	Arcia-Santana-Vargas-Mauer-Dozier-Hunter-Hicks-Suzuki-Plouffe	0.231
39	Santana-Hunter-Mauer-Arcia-Vargas-Hicks-Plouffe-Dozier-Suzuki	0.236
40	Santana-Mauer-Vargas-Dozier-Arcia-Hunter-Plouffe-Suzuki-Hicks	0.242
41	Santana-Hunter-Mauer-Arcia-Dozier-Vargas-Hicks-Plouffe-Suzuki	0.242
42	Santana-Suzuki-Mauer-Arcia-Plouffe-Vargas-Hunter-Dozier-Hicks	0.247
43	Santana-Hunter-Arcia-Mauer-Vargas-Plouffe-Dozier-Hicks-Suzuki	0.254
44	Santana-Dozier-Vargas-Mauer-Arcia-Hunter-Plouffe-Hicks-Suzuki	0.263
45	Hunter-Dozier-Mauer-Arcia-Vargas-Santana-Suzuki-Plouffe-Hicks	0.272
46	Dozier-Mauer-Arcia-Vargas-Santana-Hunter-Suzuki-Plouffe-Hicks	0.277
47	Santana-Suzuki-Mauer-Vargas-Hicks-Arcia-Plouffe-Dozier-Hunter	0.282
48	Dozier-Hicks-Mauer-Vargas-Arcia-Santana-Hunter-Suzuki-Plouffe	0.290
49	Hicks-Dozier-Vargas-Mauer-Arcia-Santana-Hunter-Plouffe-Suzuki	0.299
50	Santana-Hunter-Mauer-Arcia-Dozier-Vargas-Suzuki-Hicks-Plouffe	0.345
..	..	..
MLB	Santana-Dozier-Mauer-Vargas-Hunter-Plouffe-Arcia-Suzuki-Hicks	0.587
BAD	Suzuki-Plouffe-Hicks-Dozier-Santana-Arcia-Vargas-Hunter-Mauer	1.714

.
.
           Batting Order Frequency Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Santana	32	12	0	0	1	5	0	0	0
Mauer	0	18	20	12	0	0	0	0	0
Arcia	1	0	9	23	16	1	0	0	0
Vargas	0	6	19	13	8	4	0	0	0
Hunter	5	7	2	1	5	9	10	2	9
Plouffe	0	0	0	0	4	13	14	12	7
Hicks	4	1	0	0	7	7	9	9	13
Suzuki	0	3	0	0	0	4	12	17	14
Dozier	8	3	0	1	9	7	5	10	7

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Synergy

20 Most Common Back to Back Occurrences

Rank	Occurrences	Player 1	Player 2
1	33	Mauer	Arcia
2	22	Santana	Mauer
3	16	Mauer	Vargas
4	16	Arcia	Vargas
5	15	Dozier	Suzuki
6	14	Vargas	Mauer
7	14	Plouffe	Dozier
8	14	Suzuki	Hicks
9	13	Plouffe	Hicks
10	13	Hicks	Suzuki
11	12	Vargas	Arcia
12	12	Suzuki	Plouffe
13	11	Santana	Hunter
14	11	Hunter	Santana
15	11	Hunter	Plouffe
16	11	Plouffe	Suzuki
17	11	Hicks	Santana
18	11	Dozier	Santana
19	10	Santana	Vargas
20	10	Arcia	Plouffe
20	10	Suzuki	Santana

20 Most Common Back to Back to Back Occurrences

Rank	Occurrences	Player 1	Player 2	Player 3
1	16	Mauer	Arcia	Vargas
2	13	Vargas	Mauer	Arcia
3	12	Mauer	Vargas	Arcia
4	11	Santana	Mauer	Arcia
5	11	Santana	Mauer	Vargas
6	9	Santana	Vargas	Mauer
7	8	Hunter	Santana	Mauer
8	8	Dozier	Santana	Mauer
9	7	Plouffe	Dozier	Suzuki
10	6	Mauer	Arcia	Dozier
11	6	Plouffe	Hicks	Suzuki
12	6	Plouffe	Suzuki	Hicks
13	6	Suzuki	Hicks	Santana
14	5	Mauer	Arcia	Plouffe
15	5	Hunter	Mauer	Arcia
16	5	Suzuki	Plouffe	Hicks
17	5	Suzuki	Dozier	Santana
18	5	Dozier	Suzuki	Hicks
19	4	Santana	Hunter	Mauer
20	4	Arcia	Vargas	Hunter

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.

Toronto Blue Jays - 2015 Most Optimal Lineups

.
I am using my simulator that plays actual baseball games to find what it believes are the most optimal lineups for the 2015 Toronto Blue Jays.  I started out with over 100,000 permutations of lineups and I slowly widdled it down to the top 50 lineups. For the starting nine players, I used the projected starting lineup from MLB Depth Charts The top 50 lineups all came within a half of a win per 162 games so really anything in that range is pretty good.  When you start nearing a full win differential with the top lineup then you definitely have problems.  You will see a few lineups that may seem strange in a most optimal list but keep in mind the simulator does not take into account psychological factors, such as rookies not being allowed to hit near the top of the lineup.bbThe simulator just takes the Steamer projection inputs along with a speed rating for base running and gives you the best lineups. Sure, the Blue Jays manager won't use many of these lineups because they might not be prototypical but that is not part of the exercise.  I typically simulated each lineup over 1 million times which eliminated a large portion of the random noise and I did so only against a right handed pitcher.  Below are a list of the top 50 lineups and a batting order spot frequency table, where you can see how many times each player appeared in each of the top 50 lineups.

                                                          Top 50 Lineups

Rank	Lineup	Wins/162 Behind
1	Saunders-Pompey-Encarnacion-Bautista-Reyes-Izturis-Donaldson-Navarro-Martin	0.000
2	Izturis-Reyes-Encarnacion-Bautista-Saunders-Martin-Donaldson-Pompey-Navarro	0.030
3	Reyes-Saunders-Bautista-Encarnacion-Martin-Donaldson-Navarro-Pompey-Izturis	0.038
4	Saunders-Reyes-Bautista-Encarnacion-Donaldson-Pompey-Navarro-Izturis-Martin	0.058
5	Reyes-Saunders-Encarnacion-Bautista-Donaldson-Pompey-Izturis-Navarro-Martin	0.070
6	Reyes-Saunders-Bautista-Encarnacion-Donaldson-Pompey-Izturis-Navarro-Martin	0.077
7	Saunders-Pompey-Encarnacion-Bautista-Reyes-Izturis-Donaldson-Martin-Navarro	0.079
8	Saunders-Reyes-Izturis-Bautista-Encarnacion-Donaldson-Navarro-Martin-Pompey	0.106
9	Martin-Reyes-Encarnacion-Bautista-Saunders-Donaldson-Navarro-Pompey-Izturis	0.125
10	Bautista-Reyes-Encarnacion-Donaldson-Saunders-Pompey-Izturis-Navarro-Martin	0.148
11	Saunders-Donaldson-Encarnacion-Bautista-Reyes-Izturis-Pompey-Navarro-Martin	0.158
12	Saunders-Reyes-Encarnacion-Bautista-Izturis-Pompey-Navarro-Donaldson-Martin	0.175
13	Saunders-Reyes-Bautista-Encarnacion-Donaldson-Navarro-Martin-Izturis-Pompey	0.181
14	Saunders-Reyes-Izturis-Encarnacion-Bautista-Pompey-Navarro-Donaldson-Martin	0.186
15	Reyes-Saunders-Donaldson-Encarnacion-Bautista-Pompey-Navarro-Martin-Izturis	0.197
16	Pompey-Reyes-Izturis-Bautista-Encarnacion-Saunders-Donaldson-Navarro-Martin	0.199
17	Reyes-Encarnacion-Martin-Bautista-Saunders-Navarro-Donaldson-Pompey-Izturis	0.201
18	Izturis-Reyes-Encarnacion-Bautista-Donaldson-Saunders-Martin-Pompey-Navarro	0.212
19	Reyes-Izturis-Encarnacion-Bautista-Saunders-Navarro-Martin-Donaldson-Pompey	0.214
20	Saunders-Izturis-Martin-Bautista-Reyes-Encarnacion-Donaldson-Pompey-Navarro	0.222
21	Saunders-Reyes-Encarnacion-Bautista-Donaldson-Navarro-Martin-Izturis-Pompey	0.226
22	Reyes-Saunders-Bautista-Encarnacion-Donaldson-Pompey-Martin-Izturis-Navarro	0.228
23	Saunders-Reyes-Bautista-Encarnacion-Donaldson-Pompey-Izturis-Martin-Navarro	0.239
24	Reyes-Saunders-Encarnacion-Bautista-Martin-Donaldson-Izturis-Pompey-Navarro	0.240
25	Reyes-Saunders-Bautista-Encarnacion-Donaldson-Navarro-Pompey-Izturis-Martin	0.240
26	Reyes-Encarnacion-Martin-Bautista-Donaldson-Pompey-Navarro-Saunders-Izturis	0.249
27	Saunders-Pompey-Bautista-Encarnacion-Reyes-Izturis-Navarro-Donaldson-Martin	0.254
28	Reyes-Saunders-Martin-Encarnacion-Bautista-Navarro-Donaldson-Pompey-Izturis	0.265
29	Saunders-Reyes-Martin-Encarnacion-Bautista-Pompey-Donaldson-Izturis-Navarro	0.275
30	Pompey-Reyes-Bautista-Encarnacion-Donaldson-Martin-Saunders-Navarro-Izturis	0.281
31	Saunders-Martin-Encarnacion-Bautista-Reyes-Donaldson-Izturis-Navarro-Pompey	0.291
32	Donaldson-Pompey-Bautista-Saunders-Reyes-Izturis-Encarnacion-Navarro-Martin	0.293
33	Reyes-Saunders-Encarnacion-Donaldson-Bautista-Pompey-Izturis-Navarro-Martin	0.296
34	Saunders-Reyes-Izturis-Encarnacion-Donaldson-Martin-Bautista-Pompey-Navarro	0.300
35	Saunders-Pompey-Bautista-Encarnacion-Martin-Donaldson-Reyes-Navarro-Izturis	0.301
36	Bautista-Saunders-Encarnacion-Donaldson-Reyes-Martin-Pompey-Navarro-Izturis	0.309
37	Reyes-Saunders-Bautista-Donaldson-Encarnacion-Pompey-Izturis-Navarro-Martin	0.310
38	Donaldson-Reyes-Bautista-Encarnacion-Saunders-Pompey-Navarro-Martin-Izturis	0.313
39	Encarnacion-Donaldson-Martin-Bautista-Reyes-Saunders-Izturis-Navarro-Pompey	0.333
40	Pompey-Martin-Encarnacion-Bautista-Saunders-Reyes-Izturis-Donaldson-Navarro	0.338
41	Bautista-Saunders-Martin-Encarnacion-Reyes-Donaldson-Pompey-Navarro-Izturis	0.338
42	Izturis-Saunders-Encarnacion-Bautista-Reyes-Pompey-Donaldson-Navarro-Martin	0.340
43	Reyes-Martin-Encarnacion-Bautista-Donaldson-Pompey-Saunders-Navarro-Izturis	0.352
44	Encarnacion-Reyes-Bautista-Donaldson-Saunders-Pompey-Navarro-Martin-Izturis	0.362
45	Reyes-Navarro-Encarnacion-Bautista-Saunders-Donaldson-Izturis-Pompey-Martin	0.362
46	Encarnacion-Saunders-Donaldson-Bautista-Reyes-Martin-Izturis-Navarro-Pompey	0.363
47	Donaldson-Saunders-Izturis-Bautista-Reyes-Pompey-Encarnacion-Navarro-Martin	0.367
48	Donaldson-Saunders-Izturis-Bautista-Reyes-Encarnacion-Pompey-Navarro-Martin	0.394
49	Reyes-Donaldson-Martin-Bautista-Saunders-Encarnacion-Pompey-Navarro-Izturis	0.417
50	Bautista-Reyes-Izturis-Encarnacion-Donaldson-Saunders-Martin-Navarro-Pompey	0.442
..
BAD	Navarro-Izturis-Reyes-Saunders-Pompey-Martin-Donaldson-Encarnacion-Bautista	2.125

              Batting Order Frequency Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Saunders	16	16	0	1	10	4	2	1	0
Reyes	16	18	0	0	14	1	1	0	0
Encarnacion	3	2	19	18	3	3	2	0	0
Bautista	4	0	14	26	5	0	1	0	0
Donaldson	4	3	2	5	14	8	9	5	0
Pompey	3	5	0	0	0	18	6	10	8
Izturis	3	2	7	0	1	5	12	6	14
Navarro	0	1	0	0	0	6	11	22	10
Martin	1	3	8	0	3	5	6	6	18

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Synergy

20 Most Common Back to Back Occurrences

Rank	Occurrences	Player 1	Player 2
1	20	Encarnacion	Bautista
2	19	Navarro	Martin
3	17	Pompey	Navarro
4	15	Encarnacion	Donaldson
5	14	Donaldson	Pompey
6	13	Bautista	Encarnacion
7	12	Reyes	Izturis
8	12	Bautista	Reyes
9	11	Saunders	Reyes
10	11	Reyes	Saunders
11	11	Pompey	Izturis
12	11	Izturis	Navarro
13	10	Reyes	Encarnacion
14	10	Bautista	Saunders
15	10	Donaldson	Navarro
16	9	Izturis	Reyes
17	9	Navarro	Izturis
18	8	Donaldson	Martin
19	8	Navarro	Pompey
20	8	Martin	Donaldson
20	8	Martin	Izturis

20 Most Common Back to Back to Back Occurrences

Rank	Occurrences	Player 1	Player 2	Player 3
1	8	Bautista	Encarnacion	Donaldson
2	6	Donaldson	Navarro	Martin
3	6	Pompey	Navarro	Izturis
4	6	Navarro	Martin	Izturis
5	5	Saunders	Reyes	Izturis
6	5	Reyes	Saunders	Bautista
7	5	Reyes	Encarnacion	Bautista
8	5	Reyes	Bautista	Encarnacion
9	5	Reyes	Izturis	Encarnacion
10	5	Encarnacion	Bautista	Saunders
11	5	Encarnacion	Bautista	Reyes
12	5	Encarnacion	Donaldson	Pompey
13	5	Donaldson	Pompey	Izturis
14	5	Donaldson	Pompey	Navarro
15	5	Pompey	Izturis	Navarro
16	5	Pompey	Navarro	Martin
17	5	Izturis	Navarro	Martin
18	5	Martin	Reyes	Saunders
19	5	Martin	Encarnacion	Bautista
20	4	Saunders	Bautista	Encarnacion

                                 Analysis

Saunders:  Hits left-handed and does well against RHP.  Has decent speed and power and the simulator likes him hitting either first or second and can live with him hitting fifth as well.

Reyes:  Switch hitter with good speed and doesn't strike-out a lot.  Has a nearly identical lineup profile as Saunders does.

Encarnacion:  Good power and draws a nice amount of walks.  The simulator likes him hitting either third or fourth but also makes an appearance at each lineup spot 1 thru 7.

Bautista:  Great power and draws a lot of walks.  Here is your cleanup hitter.  If not cleanup can also fit in as the #3 hitter.

Donaldson:  Like many of the players to follow, the simulator shows Donaldson with a lot of lineup position diversity.  His best spot is fifth but also shines at 6th and 7th.  Donaldson hits for good power but doesn't hit RHP real well (compared to LHP).

Pompey:  Good speed and will clear the bases with an occasional triple.  The simulator likes him hitting 6th the most but can find a good lineup to fit in at any of the non traditional power spots (3-4-5).

Izturis:  Doesn't do anything real well on the offensive side but the simulator find him doing the least amount of damage batting either ninth or seventh and surprisingly finds his way to the third spot in seven of the top fifty lineups.

Navarro:  Definitely the turtle of the team, the simulator likes him batting 8th though it can live with him in the sixth thru ninth spots.  While a switch hitter, Navarro does much better historically against LHP.

Martin:  Makes three appearances in seven different lineup spots on the top fifty lineup chart.  Though the simulator does like Martin hitting 9th, almost as a second leadoff hitter.

............................................................................................................................

San Francisco Giants - 2015 Most Optimal Lineup

.
I am using my simulator that plays actual baseball games to find what it believes are the most optimal lineups for the 2015 San Francisco Giants.  I started out with over 2,000 permutations of lineups, a group that was filtered to remove such things as left handed hitters hitting back to back or the pitcher not hitting 8th or 9th etc... and I slowly widdled it down to the top 25 lineups.  I also compared the best lineup against the lineup that MLB Depth Charts (0.40 wins/162 games worse) shows as a likely lineup and also one of the worst lineups to see what kind of spread there is.  The top 25 lineups all came within a half of a win per 162 games so really anything in that range is pretty good.  When you start nearing a full win differential with the top lineup then you definitely have problems.  You will see a few lineups that may seem strange in a most optimal list but keep in mind the simulator does not take into account psychological factors.  It is not like Joe Panik's mom is going to be calling up Giants management in tears asking for Joe not to bat 4th because he is going to have to change his approach at the plate.  The simulator just takes the Steamer projection inputs along with a speed rating for base running and gives you the best lineups.  Sure, the Giants manager won't use many of these lineups because they might not be prototypical but that is not part of the exercise.  I typically simulated each lineup over 1 million times which eliminated a large portion of the random noise and I did so only against a right handed pitcher.  Below are a list of the top 25 lineups and a batting order spot frequency table, where you can see how many times each player appeared in each of the top 50 lineups.

                                          Top 25 Optimal Lineups

Rank	Lineup	Wins behind/162 games
1	Pagan-Aoki-Posey-Panik-Pence-Belt-McGeHee-Pitcher-Crawford	0.000
2	Pagan-Aoki-Posey-Belt-Pence-Panik-McGeHee-Pitcher-Crawford	0.027
3	Pagan-Aoki-Posey-Belt-Pence-Crawford-McGeHee-Pitcher-Panik	0.060
4	Aoki-Pagan-Panik-Posey-Belt-Pence-Crawford-McGeHee-Pitcher	0.116
5	Crawford-Pagan-Belt-Posey-Aoki-Pence-Panik-McGeHee-Pitcher	0.239
6	Pence-Aoki-Posey-Belt-Pagan-Panik-McGeHee-Pitcher-Crawford	0.247
7	Aoki-Pagan-Belt-Posey-Panik-Pence-Crawford-McGeHee-Pitcher	0.258
8	Aoki-Pagan-Belt-Posey-Crawford-Pence-Panik-McGeHee-Pitcher	0.263
9	Belt-Pagan-Panik-Posey-Aoki-Pence-Crawford-McGeHee-Pitcher	0.277
10	Pence-Aoki-Posey-Panik-Pagan-Belt-McGeHee-Pitcher-Crawford	0.286
11	Pagan-Aoki-Posey-Panik-Pence-Crawford-McGeHee-Pitcher-Belt	0.294
12	Aoki-Pagan-Panik-Posey-Crawford-Pence-Belt-McGeHee-Pitcher	0.296
13	Crawford-Pagan-Panik-Posey-Aoki-Pence-Belt-McGeHee-Pitcher	0.301
14	Aoki-Pence-Panik-Posey-Belt-Pagan-Crawford-McGeHee-Pitcher	0.321
15	Pagan-Aoki-Posey-Crawford-Pence-Belt-McGeHee-Pitcher-Panik	0.325
16	Pagan-Aoki-Pence-Panik-Posey-Belt-McGeHee-Pitcher-Crawford	0.327
17	Pagan-Panik-Posey-Belt-Pence-Aoki-McGeHee-Pitcher-Crawford	0.357
18	Belt-Pence-Crawford-Posey-Aoki-Pagan-Panik-McGeHee-Pitcher	0.360
19	Aoki-Pagan-Panik-Pence-Belt-Posey-Crawford-McGeHee-Pitcher	0.361
20	Crawford-Pagan-Panik-Pence-Aoki-Posey-Belt-McGeHee-Pitcher	0.393
21	Pagan-Aoki-Posey-Belt-Pence-Panik-McGeHee-Crawford-Pitcher	0.400
22	Pence-Aoki-Posey-Belt-Pagan-Crawford-McGeHee-Pitcher-Panik	0.407
23	Pagan-Panik-Posey-Belt-Pence-Crawford-McGeHee-Pitcher-Aoki	0.428
24	Crawford-Pence-Panik-Posey-Aoki-Pagan-Belt-McGeHee-Pitcher	0.442
25	Pagan-Aoki-Posey-Belt-Pence-Panik-McGeHee-Crawford-Pitcher	0.442
..
Bad	McGeHee-Belt-Pagan-Aoki-Pence-Panik-Crawford-Posey-Pitcher	2.438

             Batting Order Frequency Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Pagan	19	17	0	0	5	9	0	0	0
Aoki	17	17	0	1	9	3	1	0	2
Posey	0	3	24	17	2	4	0	0	0
Belt	3	1	8	14	10	8	4	0	2
Pence	4	7	2	8	18	11	0	0	0
Panik	0	5	14	7	2	8	8	0	6
McGeHee	0	0	0	1	0	3	24	21	1
Crawford	7	0	2	2	4	4	13	8	10
Pitcher	0	0	0	0	0	0	0	21	29

Synergy
20 Most Common Back to Back Occurrences in Lineup:

Rank	Occurrences	Player 1	Player 2
1	37	McGeHee	Pitcher
2	22	Pagan	Panik
3	22	Posey	Belt
4	19	Aoki	Posey
5	18	Pitcher	Aoki
6	17	Aoki	Pagan
7	17	Belt	Pence
8	17	Pitcher	Crawford
9	16	Pence	Belt
10	16	Panik	McGeHee
11	15	Pagan	Aoki
12	15	Panik	Posey
13	13	Belt	McGeHee
14	13	Crawford	Pagan
15	13	Crawford	Pitcher
16	12	Pence	Panik
17	12	Panik	Pence
18	12	Crawford	McGeHee
19	11	McGeHee	Crawford
20	10	Aoki	Pence
20	10	Posey	Panik
20	10	Belt	Pagan
20	10	Belt	Posey

10 Most Common Back to Back to Back Occurrences in Lineup:

Rank	Occurrences	Player 1	Player 2	Player 3
1	17	McGeHee	Pitcher	Crawford
2	15	Posey	Belt	Pence
3	12	Pagan	Aoki	Posey
4	12	Panik	McGeHee	Pitcher
5	12	McGeHee	Pitcher	Aoki
6	12	Crawford	McGeHee	Pitcher
7	12	Pitcher	Aoki	Pagan
8	11	Pagan	Panik	Posey
9	11	Aoki	Pagan	Panik
10	11	McGeHee	Crawford	Pitcher
10	11	Pitcher	Crawford	Pagan

                                      Analysis

Pagan:  The simulator likes the switch hitting Pagan batting first or second in the lineup.  There are a couple of permutations where the simulator likes Pagan batting sixth but he seems best suited for the top of the lineup which is where he is likely to bat in reality.

Aoki:  Like Pagan, the simulator pegs Aoki at one of the first two spots in the lineup with the occasional drop to fifth.  There are even two appearances in the top 50 lineups with Aoki batting 9th.

Posey:  The Giants will get the most bang for their buck with Posey hitting third or fourth which is really the only two spots the Giants would ever hit him in.

Belt:  Shows up in many different lineup spots with second thru sixth being the best spots for him.  Belt finds himself paired either in front of or immediately after Pence in the lineup in 33 of the top 50 lineups.  The threesome of Posey-Belt-Pence show up as the second most likely consecutive trio in the top lineups.

Pence:  Finds himself all over the radar on the first six lineup spots with fifth and sixth being his best spots.  Which is pretty much in line where Pence expects to bat in the 2015 lineup.

Panik:  Appears all over the place in the top 50 lineups.  The only two spots he doesn't appear in is leadoff and eighth.  Panik makes more than a few surprise appearances in the middle of the lineup in the top list which may come as a surprise.  The Giants won't likely bat him in the middle of the lineup but the simulator does not take into account psychological reasons (like experience) for not batting a player in a certain spot.  With the L-R-L requirement Panik ends up all over the place.

McGeHee:  Looks to be a solid fit at the seventh and eighth spots as a late source of power in the lineup before the black hole of the pitcher bats.  McGeHee-Pitcher is the number one most common consecutive batting duo in the lineup and McGeHee-Pitcher-Crawford is the number one most common threesome.

Crawford:  He shows up all over the place but is a best fit hitting seventh, eighth, ninth or leadoff.

Pitcher: The pitcher shows up hitting eighth in 42% of the top lineups.  Most MLB managers will not hit the pitcher eighth for psychological reasons but if done correctly with the right personnel and lineup construction it is a good thing.

St Louis Cardinals - Most Optimal 2015 Lineups

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I am using my simulator that plays actual baseball games to find what it believes are the most optimal lineups for the 2015 St Louis Cardinals.  I started out with over 10,000 permutations of lineups, a group that was filtered to remove such things as too many left handed hitters hitting back to back or the pitcher not hitting 8th or 9th etc... and I slowly widdled it down to the top 50 lineups.  I also compared the best lineup against the lineup that MLB Depth Charts shows as a likely lineup and also one of the worst lineups to see what kind of spread there is.  The top 50 lineups all came within a half of a win per 162 games so really anything in that range is pretty good.  When you start nearing a full win differential with the top lineup then you definitely have problems.  I typically simulated each lineup over 1 million times which eliminated a large portion of the random noise and I did so only against a right handed pitcher.  Below are a list of the top 50 lineups and a batting order spot frequence table, where you can see how many times each player appeared in each of the top 50 lineup.

                                       Top 50 Most Optimal Lineups

Rank	Lineup	Wins/162 Behind
1	Heyward-Carpenter-Holliday-Adams-Wong-Molina-Peralta-Pitcher-Jay	0
2	Carpenter-Heyward-Holliday-Adams-Wong-Molina-Peralta-Pitcher-Jay	0.014
3	Wong-Jay-Holliday-Carpenter-Heyward-Peralta-Adams-Molina-Pitcher	0.015
4	Carpenter-Heyward-Holliday-Adams-Wong-Peralta-Molina-Pitcher-Jay	0.032
5	Heyward-Jay-Holliday-Carpenter-Adams-Peralta-Molina-Pitcher-Wong	0.066
6	Wong-Heyward-Holliday-Carpenter-Adams-Peralta-Molina-Pitcher-Jay	0.086
7	Heyward-Jay-Holliday-Carpenter-Adams-Molina-Peralta-Pitcher-Wong	0.102
8	Heyward-Jay-Holliday-Carpenter-Adams-Peralta-Wong-Molina-Pitcher	0.120
9	Jay-Heyward-Holliday-Carpenter-Adams-Peralta-Molina-Pitcher-Wong	0.131
10	Wong-Jay-Holliday-Carpenter-Heyward-Molina-Adams-Peralta-Pitcher	0.151
11	Jay-Heyward-Holliday-Carpenter-Adams-Peralta-Wong-Molina-Pitcher	0.167
12	Jay-Heyward-Holliday-Carpenter-Wong-Peralta-Adams-Molina-Pitcher	0.171
13	Jay-Heyward-Holliday-Carpenter-Adams-Molina-Peralta-Pitcher-Wong	0.175
14	Heyward-Carpenter-Holliday-Adams-Wong-Peralta-Jay-Molina-Pitcher	0.184
15	Carpenter-Jay-Holliday-Adams-Heyward-Peralta-Wong-Molina-Pitcher	0.199
16	Jay-Heyward-Holliday-Adams-Carpenter-Peralta-Wong-Molina-Pitcher	0.200
17	Heyward-Jay-Holliday-Adams-Carpenter-Peralta-Wong-Molina-Pitcher	0.209
18	Wong-Jay-Peralta-Carpenter-Heyward-Holliday-Adams-Molina-Pitcher	0.243
19	Heyward-Adams-Holliday-Carpenter-Wong-Molina-Peralta-Pitcher-Jay	0.247
20	Carpenter-Heyward-Holliday-Adams-Jay-Peralta-Molina-Pitcher-Wong	0.253
21	Heyward-Wong-Holliday-Adams-Carpenter-Peralta-Molina-Pitcher-Jay	0.257
22	Jay-Wong-Holliday-Carpenter-Heyward-Molina-Adams-Peralta-Pitcher	0.266
23	Heyward-Adams-Holliday-Carpenter-Wong-Peralta-Molina-Pitcher-Jay	0.269
24	Heyward-Jay-Holliday-Carpenter-Adams-Molina-Wong-Peralta-Pitcher	0.269
25	Wong-Jay-Molina-Carpenter-Heyward-Holliday-Adams-Peralta-Pitcher	0.284
26	Jay-Carpenter-Holliday-Heyward-Adams-Peralta-Molina-Pitcher-Wong	0.286
27	Heyward-Carpenter-Holliday-Adams-Molina-Wong-Peralta-Pitcher-Jay	0.286
28	Jay-Carpenter-Holliday-Adams-Heyward-Peralta-Molina-Pitcher-Wong	0.288
29	Heyward-Holliday-Carpenter-Adams-Peralta-Wong-Molina-Pitcher-Jay	0.289
30	Carpenter-Heyward-Holliday-Adams-Wong-Molina-Jay-Peralta-Pitcher	0.292
31	Jay-Adams-Holliday-Carpenter-Heyward-Peralta-Wong-Molina-Pitcher	0.299
32	Heyward-Wong-Holliday-Carpenter-Adams-Peralta-Jay-Molina-Pitcher	0.305
33	Wong-Carpenter-Holliday-Adams-Heyward-Peralta-Molina-Pitcher-Jay	0.310
34	Carpenter-Jay-Holliday-Adams-Heyward-Peralta-Molina-Pitcher-Wong	0.310
35	Peralta-Heyward-Carpenter-Holliday-Adams-Wong-Molina-Pitcher-Jay	0.317
36	Jay-Carpenter-Holliday-Heyward-Adams-Peralta-Wong-Molina-Pitcher	0.326
37	Jay-Wong-Molina-Carpenter-Heyward-Holliday-Adams-Peralta-Pitcher	0.327
38	Carpenter-Heyward-Holliday-Adams-Jay-Peralta-Wong-Molina-Pitcher	0.327
39	Wong-Carpenter-Holliday-Adams-Heyward-Peralta-Jay-Molina-Pitcher	0.343
40	Jay-Carpenter-Holliday-Adams-Heyward-Molina-Wong-Peralta-Pitcher	0.348
41	Jay-Heyward-Holliday-Adams-Carpenter-Molina-Peralta-Pitcher-Wong	0.353
42	Wong-Adams-Holliday-Carpenter-Heyward-Peralta-Molina-Pitcher-Jay	0.372
43	Heyward-Holliday-Adams-Carpenter-Peralta-Wong-Molina-Pitcher-Jay	0.382
44	Carpenter-Heyward-Holliday-Adams-Wong-Peralta-Jay-Molina-Pitcher	0.388
45	Carpenter-Wong-Holliday-Adams-Heyward-Molina-Peralta-Pitcher-Jay	0.403
46	Peralta-Heyward-Adams-Holliday-Carpenter-Wong-Molina-Pitcher-Jay	0.433
47	Heyward-Carpenter-Holliday-Adams-Wong-Molina-Jay-Pitcher-Peralta	0.439
48	Heyward-Peralta-Carpenter-Adams-Holliday-Wong-Molina-Pitcher-Jay	0.469
49	Heyward-Wong-Holliday-Adams-Carpenter-Molina-Peralta-Pitcher-Jay	0.477
50	Carpenter-Heyward-Holliday-Adams-Molina-Wong-Peralta-Pitcher-Jay	0.485
..	..	..
MLBDC	Carpenter-Heyward-Holliday-Adams-Molina-Peralta-Jay-Wong-Pitcher	1.090
BAD	Adams-Peralta-Jay-Wong-Molina-Carpenter-Holliday-Heyward-Pitcher	3.661

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              Batting Order Frequence Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Heyward	17	16	0	2	15	0	0	0	0
Carpenter	10	10	3	21	6	0	0	0	0
Holliday	0	2	42	2	1	3	0	0	0
Adams	0	4	2	25	12	0	7	0	0
Wong	8	6	0	0	10	7	10	0	9
Peralta	2	1	1	0	2	26	10	7	1
Molina	0	0	2	0	2	14	17	15	0
Jay	13	11	0	0	2	0	6	0	18
Pitcher	0	0	0	0	0	0	0	28	22

Synergy:
10 most common back to back occurrences in lineup.

Rank	Occurrences	Player 1	Player 2
1	32	Molina	Pitcher
2	28	Holliday	Adams
3	26	Pitcher	Jay
4	19	Heyward	Holliday
5	19	Holliday	Carpenter
6	19	Wong	Molina
7	17	Peralta	Pitcher
8	15	Carpenter	Heyward
9	15	Jay	Heyward
10	14	Pitcher	Wong

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10 most common back to back to back occurrences in lineup.

Rank	Occurrences	Player 1	Player 2	Player 3
1	16	Molina	Pitcher	Jay
2	13	Heyward	Holliday	Adams
3	13	Wong	Molina	Pitcher
4	12	Peralta	Molina	Pitcher
5	12	Pitcher	Jay	Heyward
6	10	Carpenter	Heyward	Holliday
7	10	Holliday	Carpenter	Adams
8	10	Peralta	Wong	Molina
9	10	Peralta	Pitcher	Jay
10t	9	Carpenter	Holliday	Adams
10t	9	Molina	Pitcher	Wong
10t	9	Pitcher	Wong	Jay

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                                         Analysis

Heyward:  Slots well at the top of the lineup as either the leadoff or second hitter.  With others better suited for the 3rd or 4th spot in the lineup, Heyward also slots in well as the 5th hitter but should not hit in the 6th thru 9th slots.

Carpenter: Surpsingly does well at cleanup along with Adams but also does well among the 1-2-5 spots like Heyward.  Carpenter shows a lot of lineup versatility if there is such a thing.

Holliday:  Should hit third.

Adams: The teams best cleanup hitter but can also move down to fifth in the lineup if need be.

Wong: No one great spot for Wong but he probably shows the most "lineup versatility" as anyone on the team  There are 6 lineup slots where Wong appeared at least 6 times.  When it comes to batting order spots, Wong is definitely the Nomad of the team.  Wong even makes 9 appearances hitting 9th, one spot behind the pitcher.

Peralta:  The simulator likes Peralta hitting 6th as he provides some pretty decent back of the lineup power.  He should definitely stay out of the top five spots in the lineup.

Molina:  6-7-8 are the best spots for Molina as only Heyward and Molina have a 14 or higher on their third most frequented lineup spot.

Jay:  Looks like the simulator likes Jay setting the table whether it be as the leadoff hitter or the quasi-leadoff hitter batting 9th.

Pitcher:  With Jay and Wong making appearances in the 9th spot the simulator pretty much calls it a crapshoot at hitting the pitcher 8th or 9th.  The pitcher should bat immediately after Molina does.

How Optimal Was The Royals 2014 Lineup

My simulator is a good tool for looking at optimal batting orders.  It can play tens of thousands and even millions (with a smaller set of games) of games while keeping the opponent static which can give you a good idea which lineups win more games.  I am using the Royals most common lineup that they used in the 2014 season and seeing how optimal the simulator thought it was.  After doing this for the Dodgers and Cardinals, I decided to move on to the Kansas City Royals.  I put in some filters to cut down on the number of lineup permutations.  I tried to mimic what the Royals manager tended to do in not hitting lefties back to back other than the 9th then leadoff hitter etc...    Of course you can't expect any manager to be implementing the most optimal lineup but you also don't want him to be giving away fractions of wins each game that can add up over a 162 game season.  So down below, I list the Top 10 lineups along with the most common 2014 lineup in a table sorted by Wins/162 games.  I also list a table showing the frequency of where each player batted in each of the top 50 lineups.  I love the batting order frequency tables as they show you which players have a few dedicated positions in the order they should be hitting in and which players are more versatile in giving you an optimal lineup.  I used 2014 final season stats as the input projections for each hitter because that is for the most part similar to the data that the manager went by.  And I did all the simulations with the opposing team using a right handed starting pitcher.

Before we begin, here is the common Royals lineup that I compared against

Aoki-Infante-Hosmer-Butler-Gordon-Perez-Moustakas-Escobar-Dyson

                                        Top 10 Lineups

Lineup	Wins/162 GB
Aoki-Infante-Moustakas-Perez-Gordon-Escobar-Hosmer-Butler-Dyson	0.000
Aoki-Escobar-Moustakas-Perez-Gordon-Infante-Dyson-Butler-Hosmer	0.065
Aoki-Escobar-Moustakas-Perez-Gordon-Butler-Dyson-Infante-Hosmer	0.111
Aoki-Infante-Moustakas-Perez-Gordon-Escobar-Dyson-Butler-Hosmer	0.143
Aoki-Escobar-Moustakas-Perez-Gordon-Butler-Hosmer-Infante-Dyson	0.164
Aoki-Escobar-Hosmer-Perez-Gordon-Infante-Moustakas-Butler-Dyson	0.166
Aoki-Escobar-Moustakas-Perez-Gordon-Infante-Hosmer-Butler-Dyson	0.207
Dyson-Infante-Moustakas-Perez-Gordon-Escobar-Aoki-Butler-Hosmer	0.238
Aoki-Escobar-Moustakas-Perez-Hosmer-Butler-Gordon-Infante-Dyson	0.308
Moustakas-Escobar-Hosmer-Perez-Gordon-Butler-Aoki-Infante-Dyson	0.354

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             Batting Order Frequency Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Aoki	30	0	0	0	9	0	5	0	6
Escobar	0	32	0	0	0	11	0	7	0
Moustakas	4	0	26	0	5	0	10	0	5
Perez	0	3	0	39	0	5	0	3	0
Gordon	9	0	0	0	30	0	10	0	1
Infante	0	14	0	0	0	19	0	17	0
Hosmer	2	0	24	0	6	0	10	0	8
Butler	0	1	0	11	0	15	0	23	0
Dyson	5	0	0	0	0	0	15	0	30

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Note:  The most common lineup ended up 0.56 wins per 162 games behind the most optimal lineup.  Not too bad.  The simulator liked batting Perez cleanup which the Royals did not do.  The simulator also liked hitting Escobar second instead of Infante (basically switching them).
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How Optimal Was The Cardinals 2014 Lineup

My simulator is a good tool for looking at optimal batting orders.  It can play tens of thousands and even millions (with a smaller set of games) of games while keeping the opponent static which can give you a good idea which lineups win more games.  Originally, taking an idea from the "straight arrows" over at The Book Blog, I decided to take the 2014 Dodgers most common lineup, that one can find over at the Baseball Reference website and compare how well that lineup does against tens of thousands of other possible lineup permutations.  Now I decided to move on to the St. Louis Cardinals mostly because I like doing this and the Cardinals have a very analytical friendly blog not one that spends endless hours discussing TV shows, taco trucks and their personal problems.  I did set some filters to cut down on the permutations, like I only looked at lineups where the pitcher hit 8th or 9th and where the best hitters didn't hit 8th or 9th and the worst hitters didn't hit 3rd or 4th etc...  I wanted to see how well one of the Cardinals most common lineups compared to what the simulator thought was the most optimal lineup.  Of course you can't expect any manager to be implementing the most optimal lineup but you also don't want him to be giving away fractions of wins each game that can add up over a 162 game season.  So down below, I list the Top 10 lineups along with the most common 2014 lineup in a table sorted by Wins/162 games.  I also list a table showing the frequency of where each player batted in each of the top 50 lineups.  I love the batting order frequency tables as they show you which players have a few dedicated positions in the order they should be hitting in and which players are more versatile in giving you an optimal lineup.  I used 2014 final season stats as the input projections for each hitter because that is for the most part similar to the data that the manager went by.  And I did all the simulations with the opposing team using a right handed starting pitcher.

Before we begin, here is the common Cardinals lineup that I compared against

Carpenter-Wong-Holliday-Craig-Molina-Adams-Peralta-Bourjos-Pitcher.


                                                      Top 10 Lineups

Rank	Lineup	Wins/162 Behind
1	Peralta-Carpenter-Holliday-Adams-Wong-Bourjos-Molina-Craig-Pitcher	0.000
2	Peralta-Carpenter-Adams-Holliday-Wong-Molina-Craig-Pitcher-Bourjos	0.081
3	Bourjos-Carpenter-Holliday-Adams-Peralta-Wong-Molina-Craig-Pitcher	0.092
4	Peralta-Carpenter-Adams-Holliday-Wong-Bourjos-Molina-Craig-Pitcher	0.118
5	Holliday-Carpenter-Peralta-Adams-Wong-Molina-Craig-Pitcher-Bourjos	0.119
6	Holliday-Carpenter-Adams-Peralta-Wong-Molina-Craig-Pitcher-Bourjos	0.158
7	Bourjos-Carpenter-Peralta-Holliday-Adams-Wong-Molina-Craig-Pitcher	0.161
8	Carpenter-Holliday-Adams-Peralta-Wong-Molina-Craig-Pitcher-Bourjos	0.162
9	Carpenter-Wong-Peralta-Holliday-Adams-Bourjos-Molina-Craig-Pitcher	0.206
10	Wong-Carpenter-Holliday-Peralta-Adams-Bourjos-Molina-Craig-Pitcher	0.216

And how did the Cardinals common lineup fair? Wel, it finished 2.07 wins/162 games worse than the top ranking lineup according to the simulator. Good thing that lineup wasn't actually used 162 times but it kind of gives you a general idea of how many wins the Cardinals manager may have been leaving off the table during the entire season if you trust the output of the simulator. When I did this for the Dodgers, Mattingly's most common lineup was only 0.84 wins/162 games off from the most optimal.

           Batting Order Frequency Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Carpenter	15	19	7	6	3	0	0	0	0
Peralta	5	5	11	9	7	10	3	0	0
Holliday	3	6	17	17	6	1	0	0	0
Adams	0	5	12	18	10	3	2	0	0
Wong	9	5	0	0	22	12	2	0	0
Molina	0	3	3	0	0	12	31	0	1
Bourjos	18	7	0	0	2	12	4	0	7
Craig	0	0	0	0	0	0	8	42	0
Pitcher	0	0	0	0	0	0	0	8	42

Player by Player Analysis

Leadoff:  Matt Carpenter.  Matheny bats Carpenter leadoff and the simulator agrees that that is a good spot for him but he would be a little better off hitting second not first.  Good

Second:  Kolten Wong.  Matheny bats Wong second and the simulator doesn't think that is one of the top three places for him to hit.  It is not a totally glaring mistake as the simulator does have Wong hitting second in the 9th most optimal lineup but a better selection can be made here.  Below Average

Third:  Matt Holliday.  Matheny bats Holliday third and pretty much nails this one.  The simulator thinks that Holliday is a good third or fourth hitter in this lineup.  While Holliday does have some batting order versatility and could even slot into the second and fifth slots without deserving to be taken out behind the shed.  Great

Cleanup:  Allen Craig.  Matheny bats Craig cleanup and as you know Craig had an awful season.  To Matheny's credit he probably didn't know Craig was going to be this bad and stay this bad as long as he did but nonetheless batting Craig cleanup was not a good choice and if done enough times would be extremely costly.  The simulator liked Craig batting 8th, in front of the pitcher.  Bad

Fifth:  Yadier Molina:  Matheny bats Molina fifth and the simulator does not like him there at all.  The simulator likes the slow legged and power hitting catcher batting seventh or sixth at best where he can knock in runs and not get knocked in himself.  Bad

Sixth:  Matt Adams.  Matheny bats Adams sixth and the simulator thinks that is too low.  Mostly because the simulator already knows how bad Craig is.  The simulator likes Craig batting cleanup or in one of the other traditional power hitting slots (third or fifth).  Bad

Seventh:  Jhonny Peralta.  Matheny bats Peralta seventh and maybe he didn't know at the time he was building these lineups that Peralta would be one of his better hitters in 2014.  A look up above at the 'Batting Order Frequency' table and you really see how versatile Peralta is in this lineup.  Peralta hits leadoff in three of the four top optimal lineups and the simulator think the third, fourth and fifth slots are his best.  Good

Eighth:  Peter Bourjos.  Matheny puts the all glove not stick Bourjos eighth.  From above the simulator thinks that spot should be reserved for Allen Craig.  The simulator touts leadoff or ninth, which in and of itself is a second leadoff spot, as the most optimal placement for Bourjos.  Bourjos is leadoff in 2 of the top ten lineups and bats ninth in 4 of the top ten lineups.  There is often efficiency in batting the pitcher 8th if you have the right personnel to do it.  Bad

Ninth:  Pitcher.  The simulator has the pitcher batting 9th in 84% of the Top 50 lineups.  Good

Overall:
So overall Matheny did not do a very good job with this lineup but to his credit the simulator is using end of season (or after the fact) input projections.  If Matheny was hitting Craig cleanup when it was obvious he wasn't very good then he is greatly to blame, otherwise he gets some slack.  How much slack is up for debate.
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How Good Is Don Mattingly At Setting The Batting Order

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My simulator is a good tool for looking at optimal batting orders.  It can play tens of thousands and even millions (with a smaller set of games) of games while keeping the opponent static which can give you a good idea which lineups win more games.  Taking an idea from the "straight arrows" over at The Book Blog, I decided to take the 2014 Dodgers most common lineup, that one can find over at the Baseball Reference website and compare how well that lineup does against tens of thousands of other possible lineup permutations.  I did set some filters to cut down on the permutations, like I only looked at lineups where the pitcher hit 8th or 9th and where the best hitters didn't hit 8th or 9th and the worst hitters didn't hit 3rd or 4th etc...  I wanted to see how well Mattingly's most common lineup compared to what the simulator thought was the most optimal lineup.  Of course you can't expect any manager to be implementing the most optimal lineup but you also don't want him to be giving away fractions of wins each game that can add up over a 162 game season.  So down below, I list the Top 25 lineups along with the most common 2014 lineup in a table sorted by Wins/162 games.  I also list a table showing the frequency of where each player batted in each of the top 25 lineups.  I love the batting order frequency tables as they show you which players have a few dedicated positions in the order they should be hitting in and which players are more versatile in giving you an optimal lineup.  I used 2014 final season stats as the input projections for each hitter because that is for the most part what Don Mattingly went by.  And I did all the simulations with the opposing team using a right handed starting pitcher.

Before we begin, here is the most common 2014 Dodgers lineup.

Gordon-Puig-Gonzales-Kemp-Ramirez-Crawford-Uribe-Ellis-Pitcher

                                                       Top 25 Lineups

Rank	Lineup	Wins/162 Behind
1	Kemp-Gordon-Ramirez-Gonzalez-Puig-Crawford-Ellis-Uribe-Pitcher	0.000
2	Gordon-Ellis-Ramirez-Gonzalez-Puig-Crawford-Kemp-Uribe-Pitcher	0.059
3	Gordon-Ellis-Crawford-Ramirez-Puig-Gonzalez-Kemp-Uribe-Pitcher	0.060
4	Gordon-Puig-Gonzalez-Ramirez-Crawford-Kemp-Uribe-Pitcher-Ellis	0.090
5	Gordon-Ellis-Gonzalez-Ramirez-Puig-Crawford-Kemp-Uribe-Pitcher	0.108
6	Puig-Crawford-Ramirez-Gonzalez-Kemp-Gordon-Ellis-Uribe-Pitcher	0.163
7	Ellis-Gordon-Ramirez-Gonzalez-Puig-Crawford-Kemp-Uribe-Pitcher	0.218
8	Ellis-Puig-Gonzalez-Ramirez-Crawford-Kemp-Uribe-Pitcher-Gordon	0.226
9	Gordon-Puig-Gonzalez-Ramirez-Kemp-Crawford-Uribe-Pitcher-Ellis	0.243
10	Puig-Crawford-Ramirez-Gonzalez-Kemp-Gordon-Uribe-Pitcher-Ellis	0.261
11	Crawford-Kemp-Gonzalez-Ramirez-Puig-Gordon-Ellis-Uribe-Pitcher	0.307
12	Gordon-Puig-Ramirez-Gonzalez-Kemp-Crawford-Uribe-Pitcher-Ellis	0.316
13	Ellis-Gordon-Ramirez-Gonzalez-Puig-Kemp-Crawford-Uribe-Pitcher	0.326
14	Crawford-Puig-Gonzalez-Ramirez-Kemp-Gordon-Uribe-Pitcher-Ellis	0.348
15	Crawford-Puig-Ramirez-Gonzalez-Kemp-Gordon-Uribe-Ellis-Pitcher	0.379
16	Kemp-Crawford-Ramirez-Gonzalez-Puig-Gordon-Uribe-Ellis-Pitcher	0.402
17	Ellis-Puig-Gonzalez-Ramirez-Crawford-Kemp-Gordon-Uribe-Pitcher	0.412
18	Gordon-Ramirez-Kemp-Gonzalez-Puig-Crawford-Ellis-Uribe-Pitcher	0.430
19	Gordon-Kemp-Gonzalez-Ramirez-Puig-Crawford-Uribe-Ellis-Pitcher	0.430
20	Gordon-Ramirez-Kemp-Gonzalez-Puig-Crawford-Uribe-Ellis-Pitcher	0.456
21	Gordon-Puig-Gonzalez-Ramirez-Kemp-Crawford-Ellis-Pitcher-Uribe	0.513
22	Ramirez-Gordon-Kemp-Gonzalez-Puig-Crawford-Uribe-Ellis-Pitcher	0.551
23	Gordon-Ellis-Crawford-Ramirez-Puig-Gonzalez-Kemp-Pitcher-Uribe	0.580
24	Puig-Crawford-Kemp-Gonzalez-Ramirez-Gordon-Ellis-Uribe-Pitcher	0.604
25	Gordon-Ramirez-Kemp-Gonzalez-Puig-Ellis-Crawford-Urube-Pitcher	0.613
55	Gordon-Puig-Gonzalez-Kemp-Ramirez-Crawford-Uribe-Ellis-Pitcher	0.837

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And for comparison sake the worst lineup was 3.55 wins/162 games worst than the best lineup.
Worst: Ellis-Uribe-Puig-Kemp-Gonzalez-Ramirez-Crawford-Pitcher-Gordon
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           Batting Order Frequency Table

	1st	2nd	3rd	4th	5th	6th	7th	8th	9th
Gordon	12	4	0	0	0	7	1	0	1
Puig	3	8	0	0	14	0	0	0	0
Gonzalez	0	0	9	14	0	2	0	0	0
Kemp	2	2	5	0	7	4	5	0	0
Ramirez	1	3	9	11	1	0	0	0	0
Crawford	3	4	2	0	3	11	2	0	0
Uribe	0	0	0	0	0	0	11	11	2
Ellis	4	4	0	0	0	1	6	5	5
Pitcher	0	0	0	0	0	0	0	8	17

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Analysis:
So the simulator thinks that Mattingly left around 0.84 wins off the table over 162 games with his most common lineup.  I will leave that up to the reader if they think that is significant.  Keep in mind that one win (or WAR) is going for about $7 million dollars on the free agent market.

I think Mattingly's lineup is an above average one when using common and traditional methods for constructing a lineup.  A look at the Batting Order Frequency Table (BOF Table) does show what the simulator thinks are a few glaring mistakes though.  When we look at the BOF Table you will notice that a few players are more versatile in the lineup slot that they give the most value in.  Puig for example only slots into three locations... leadoff(3), second(8) and fifth(14) while players like Kemp and Ellis slot in to many more locations but not as frequently.  Let's walk through each player in Mattingly's lineup one at a time and look for strengths and weaknesses.

Player by Player Analysis:
Leadoff - Dee Gordon:  Mattingly has Dee Gordon in the leadoff spot and according to the simulator that is the best spot for him, though hitting 6th can be good as well as batting 2nd depending of course on the rest of the batting order makeup.  Nails it.

Second - Yasiel Puig:  Puig's best spot is hitting fifth where he can help rack up the RBIs but hitting second is also a good spot for Puig.  I think Mattingly does well on Puig's position in the order.  Good

Third - Adrian Gonzalez:  Just like with Puig, Mattingly has slotted Gonzalez into his second best spot in the batting order.  The simulator prefers Gonzalez hitting one spot lower which wouldn't ordinarily be that big of a deal as long as the person batting fourth in his place belonged there too.  Good

Cleanup - Matt Kemp:  The simulator thinks Kemp is many things but a cleanup hitter on this team is not one of them.  According to the simulator it is a big mistake to bat Kemp fourth.  There are many lineup positions that Kemp can slot into and you can still have a highly optimal lineup but fourth is not one of them.  Stink

Fifth - Hanley Ramirez:  Out of the top 25 lineups the simulator has Ramirez hitting fifth only once.  The simulator seems to like Gonzalez and Ramirez hitting in the 3rd/4th slots in either order making this another glaring mistake according to the sim.  The 4th and 5th spots in the lineup are probably not good spots to be making mistakes at.  Stink

Sixth - Carl Crawford:  Crawford also is pretty versatile in where he slots in to the top 25 lineups and Mattingly gets back on the winning track as he nails this one.  Crawford hits 6th in 11 out of the top 25 most optimal lineups including three of the top five.  Nails it.

Seventh - Juan Uribe:  The simulator thinks that Uribe should hit seventh or eighth and that is exactly where Mattingly puts him.  Slow, low on base second tier power hitters are good for the bottom of the lineup as they tend to give you that last good chance to clear the bases before the black hole of the pitcher batting.  Nails it.

Eighth - A.J. Ellis:  Ellis like Kemp is able to slot in to many different lineup spots without causing carnage to the optimization of it.  Ellis is not a very good hitter but he still does bring a decent skill of drawing walks to the table, that is why you see him hitting at the top of the lineup a few times and ninth.  His poor hitting but good on base skills are a good fit for hitting 9th and moving the pitcher to 8th as he turns in to a quasi leadoff hitter in the 9th spot.  Good.

Ninth - Pitchers Spot:  The simulator has the pitcher hitting 9th in 17 out of 25 (68%) of the top lineups.  So there can be some value to hitting the pitcher eighth but you have to have the right personnel and batting order mix to make it work.  Nails it.
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Overall, Mattingly does a pretty good job but the simulator thinks he made big mistakes in hitting Matt Kemp cleanup and Hanley Ramirez fifth and really with the tools that Mattingly has (instincts) you really wouldn't know this was a mistake.

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