Tigers vs Dodgers - Simulation Results 4/9

                                 Top 100 Most Likely Final Scores

Rank	Tigers	Dodgers	Occurrences		Rank	Tigers	Dodgers	Occurrences
1	2	3	47257		51	1	7	5750
2	1	2	42579		52	0	6	5728
3	3	4	38070		53	8	2	5455
4	3	2	33824		54	8	3	5137
5	2	1	30881		55	7	6	5116
6	4	3	26697		56	5	7	5089
7	1	3	25711		57	7	0	5039
8	3	1	25550		58	8	1	4961
9	4	5	25331		59	8	4	4356
10	4	2	23971		60	2	8	4175
11	2	4	23770		61	3	8	3891
12	0	1	22718		62	0	7	3789
13	4	1	19590		63	1	8	3780
14	1	4	19130		64	8	5	3532
15	0	2	18203		65	7	8	3453
16	5	3	17740		66	9	2	3415
17	5	4	17599		67	4	8	3377
18	5	2	17236		68	8	0	3217
19	2	0	16920		69	9	3	3189
20	1	0	16441		70	9	1	3016
21	2	5	16405		71	5	8	2963
22	3	5	16317		72	8	6	2891
23	3	0	15502		73	9	4	2672
24	0	3	15314		74	2	9	2568
25	5	1	14729		75	8	7	2426
26	5	6	14623		76	0	8	2418
27	1	5	13349		77	6	8	2406
28	4	0	12850		78	1	9	2399
29	6	2	12128		79	3	9	2312
30	6	3	11869		80	9	5	2094
31	0	4	11737		81	9	0	2076
32	6	1	10692		82	4	9	2053
33	6	4	10617		83	10	2	1993
34	2	6	10561		84	10	3	1975
35	3	6	10516		85	10	1	1892
36	5	0	9848		86	10	4	1618
37	4	6	9717		87	2	10	1601
38	6	5	9638		88	3	10	1570
39	1	6	8934		89	5	9	1539
40	0	5	8367		90	9	6	1538
41	7	2	8319		91	8	9	1480
42	7	3	7775		92	0	9	1424
43	6	7	7415		93	1	10	1391
44	7	1	7362		94	6	9	1306
45	6	0	7203		95	10	0	1253
46	7	4	6756		96	9	7	1249
47	2	7	6677		97	10	5	1240
48	3	7	6353		98	11	2	1193
49	4	7	5948		99	4	10	1186
50	7	5	5861		100	11	3	1058

Note: One million games simulated.

Tigers vs Dodgers - Simulation Results 4/8

                              Top 100 Most Likely Final Scores

Rank	Tigers	Dodgers	Occurrences		Rank	Tigers	Dodgers	Occurrences
1	2	3	48012		51	4	7	5576
2	1	2	43460		52	0	6	5302
3	3	4	38546		53	7	0	5291
4	3	2	35562		54	1	7	5156
5	2	1	32697		55	8	3	5000
6	4	3	27493		56	8	1	4935
7	3	1	27158		57	7	6	4886
8	1	3	25485		58	5	7	4693
9	4	2	25459		59	8	4	4196
10	4	5	24943		60	2	8	3869
11	2	4	23322		61	3	8	3578
12	0	1	23312		62	8	0	3546
13	4	1	20890		63	9	2	3437
14	2	0	18633		64	8	5	3389
15	1	4	18339		65	0	7	3368
16	5	3	18100		66	1	8	3350
17	5	2	18021		67	7	8	3169
18	1	0	17678		68	9	1	3081
19	0	2	17649		69	9	3	3064
20	5	4	17437		70	4	8	3035
21	3	0	17091		71	5	8	2714
22	3	5	16356		72	8	6	2699
23	2	5	16002		73	9	4	2573
24	5	1	15881		74	2	9	2300
25	0	3	14399		75	8	7	2264
26	4	0	14041		76	3	9	2200
27	5	6	13836		77	6	8	2187
28	6	2	12682		78	9	0	2169
29	1	5	12326		79	10	2	2096
30	6	3	11899		80	0	8	2035
31	6	1	11280		81	1	9	2029
32	0	4	10780		82	10	3	1947
33	5	0	10743		83	9	5	1915
34	6	4	10591		84	10	1	1858
35	3	6	10117		85	4	9	1821
36	2	6	9875		86	10	4	1523
37	6	5	9631		87	9	6	1480
38	4	6	9201		88	5	9	1414
39	7	2	8472		89	2	10	1383
40	1	6	8235		90	8	9	1321
41	7	3	7880		91	3	10	1266
42	6	0	7745		92	0	9	1250
43	7	1	7683		93	10	0	1246
44	0	5	7536		94	10	5	1224
45	7	4	6915		95	1	10	1190
46	6	7	6887		96	6	9	1176
47	2	7	6079		97	9	7	1158
48	3	7	5904		98	11	1	1134
49	7	5	5682		99	11	2	1113
50	8	2	5601		100	4	10	1089

Giants vs Dodgers - Simulation Results 4/6

                             Top 100 Most Likely Final Scores

Rank	Giants	Dodgers	Occurrences		Rank	Giants	Dodgers	Occurrences
1	1	2	52839		51	7	4	5130
2	2	3	51453		52	4	7	5049
3	3	4	37005		53	2	8	4671
4	2	1	35829		54	1	8	4606
5	3	2	34064		55	7	5	4153
6	0	1	33119		56	5	7	3903
7	1	3	31010		57	3	8	3811
8	3	1	26146		58	8	2	3752
9	0	2	26014		59	7	0	3729
10	2	4	24733		60	0	8	3674
11	4	3	24388		61	7	6	3510
12	1	4	23220		62	8	3	3430
13	0	3	22384		63	8	1	3388
14	4	2	22305		64	1	9	3094
15	1	0	22104		65	4	8	2981
16	4	5	22023		66	2	9	2915
17	2	0	20215		67	8	4	2881
18	4	1	18551		68	3	9	2385
19	2	5	17154		69	0	9	2358
20	0	4	16496		70	7	8	2355
21	3	0	16192		71	8	5	2321
22	1	5	15956		72	5	8	2320
23	3	5	15348		73	8	0	2303
24	5	2	14746		74	9	2	2185
25	5	3	14713		75	9	1	2124
26	5	4	14423		76	9	3	2023
27	5	1	12571		77	8	6	1913
28	0	5	12096		78	4	9	1908
29	4	0	11756		79	2	10	1893
30	5	6	11511		80	1	10	1884
31	2	6	11025		81	6	8	1707
32	1	6	10871		82	9	4	1696
33	3	6	10274		83	8	7	1541
34	6	2	9445		84	3	10	1517
35	6	3	9424		85	0	10	1490
36	6	1	8463		86	9	0	1398
37	5	0	8385		87	9	5	1385
38	0	6	8353		88	10	2	1308
39	4	6	8331		89	5	9	1293
40	6	4	8278		90	10	3	1182
41	6	5	7489		91	10	1	1167
42	1	7	7369		92	2	11	1136
43	2	7	7247		93	4	10	1110
44	3	7	6226		94	1	11	1093
45	7	2	6080		95	6	9	1058
46	7	3	5838		96	10	4	1008
47	6	0	5742		97	8	9	984
48	0	7	5677		98	9	6	974
49	7	1	5373		99	3	11	916
50	6	7	5307		100	0	11	837

Note: 1 million games simulated.

Giants vs Dodgers - Simulation Results 4/5

                                   Top 100 Most Likely Final Scores

Rank	Giants	Dodgers	Occurrences		Rank	Giants	Dodgers	Occurrences
1	2	3	47994		51	4	7	5272
2	1	2	47935		52	7	0	4953
3	3	4	36343		53	8	2	4695
4	2	1	35710		54	7	5	4624
5	3	2	35507		55	8	1	4419
6	0	1	28997		56	5	7	4326
7	3	1	28394		57	0	7	4297
8	1	3	26786		58	2	8	4254
9	4	3	25616		59	8	3	4075
10	4	2	24350		60	7	6	4070
11	2	4	22646		61	1	8	3933
12	4	5	22516		62	3	8	3796
13	1	0	22011		63	8	4	3432
14	0	2	21569		64	8	0	3236
15	4	1	21268		65	4	8	3024
16	2	0	20832		66	0	8	2868
17	1	4	19469		67	9	2	2837
18	3	0	18253		68	8	5	2798
19	0	3	17072		69	7	8	2790
20	5	2	16690		70	2	9	2729
21	5	3	16041		71	1	9	2689
22	2	5	15620		72	9	1	2636
23	5	4	15364		73	5	8	2547
24	3	5	15115		74	9	3	2482
25	5	1	15105		75	3	9	2420
26	4	0	14164		76	8	6	2276
27	1	5	13045		77	6	8	1997
28	0	4	12419		78	9	4	1984
29	5	6	12340		79	4	9	1970
30	6	2	11145		80	9	0	1936
31	6	3	10798		81	8	7	1816
32	5	0	10606		82	0	9	1773
33	6	1	10404		83	2	10	1707
34	3	6	9847		84	10	2	1686
35	2	6	9647		85	1	10	1676
36	6	4	9198		86	10	1	1580
37	0	5	9117		87	9	5	1542
38	1	6	8995		88	3	10	1531
39	4	6	8648		89	5	9	1489
40	6	5	8262		90	10	3	1440
41	7	2	7503		91	4	10	1241
42	6	0	7486		92	9	6	1217
43	7	1	7039		93	10	4	1166
44	7	3	6767		94	8	9	1146
45	2	7	6564		95	6	9	1137
46	0	6	6129		96	2	11	1125
47	1	7	6110		97	10	0	1118
48	6	7	6068		98	0	10	1103
49	7	4	5893		99	1	11	998
50	3	7	5870		100	3	11	979

Note: 1 million games simulated.

Dodgers vs Padres - Simulation Results 4/2

Away	Home	Starter	Starter	Favorite	Dodgers Runs	Padres Runs	Win %	Total Runs
Dodgers	Padres	Dan Haren	Tyson Ross	Padres	2.902	3.106	54.6%	6.01

                                Top 100 Most Likely Final Scores

Rank	Dodgers	Padres	Occurrences		Rank	Dodgers	Padres	Occurrences
1	1	2	5697		51	6	7	417
2	2	3	5091		52	7	5	391
3	2	1	4290		53	7	0	385
4	0	1	4086		54	8	2	383
5	3	2	3846		55	1	8	382
6	3	4	3402		56	2	8	379
7	1	3	3280		57	4	7	358
8	0	2	3002		58	8	1	326
9	1	0	2947		59	7	6	319
10	3	1	2811		60	3	8	308
11	4	3	2557		61	8	3	308
12	0	3	2482		62	0	8	296
13	2	4	2392		63	5	7	293
14	2	0	2347		64	8	4	269
15	1	4	2337		65	8	0	231
16	4	2	2319		66	1	9	227
17	4	1	1957		67	4	8	225
18	4	5	1894		68	8	5	211
19	0	4	1858		69	9	1	205
20	3	0	1701		70	2	9	200
21	1	5	1579		71	9	2	196
22	5	3	1531		72	0	9	194
23	2	5	1511		73	7	8	184
24	5	2	1461		74	9	3	182
25	3	5	1406		75	3	9	178
26	5	4	1399		76	8	6	163
27	5	1	1299		77	1	10	154
28	0	5	1283		78	10	2	153
29	4	0	1257		79	5	8	152
30	2	6	1007		80	9	0	152
31	1	6	959		81	9	4	151
32	6	2	958		82	8	7	129
33	5	6	918		83	10	1	129
34	6	3	908		84	4	9	119
35	6	1	850		85	9	5	117
36	5	0	842		86	10	3	112
37	0	6	837		87	6	8	109
38	3	6	835		88	2	10	108
39	6	4	770		89	0	10	95
40	4	6	700		90	3	10	95
41	6	5	691		91	5	9	87
42	1	7	664		92	1	11	82
43	2	7	618		93	9	6	82
44	7	2	589		94	10	0	78
45	6	0	584		95	4	10	71
46	7	1	553		96	10	5	71
47	7	3	550		97	10	4	68
48	0	7	538		98	11	2	66
49	3	7	514		99	2	11	63
50	7	4	494		100	11	1	63

Dodgers vs Padres - Simulation Results 4/1

                            Top 100 Most Likely Final Scores

Rank	Dodgers	Padres	Count		Rank	Dodgers	Padres	Count
1	1	2	56076		51	8	1	4259
2	2	3	50167		52	6	7	4171
3	2	1	44810		53	0	7	4118
4	3	2	38920		54	8	2	4040
5	0	1	38344		55	4	7	3693
6	3	4	33286		56	7	5	3660
7	1	0	31779		57	8	3	3417
8	3	1	31755		58	8	0	3345
9	1	3	29255		59	1	8	3229
10	2	0	28165		60	2	8	3213
11	0	2	26066		61	7	6	3151
12	4	3	25388		62	5	7	2978
13	4	2	24535		63	3	8	2679
14	2	4	22347		64	9	1	2583
15	3	0	22016		65	8	4	2580
16	4	1	21791		66	0	8	2514
17	0	3	20385		67	9	2	2432
18	1	4	20329		68	4	8	2171
19	4	5	18871		69	9	0	2101
20	4	0	16015		70	8	5	2063
21	5	2	15775		71	9	3	1973
22	5	1	15072		72	1	9	1966
23	5	3	14976		73	2	9	1964
24	0	4	14814		74	7	8	1742
25	2	5	14510		75	3	9	1597
26	5	4	14122		76	8	6	1587
27	1	5	13397		77	5	8	1573
28	3	5	13094		78	9	4	1547
29	5	0	11382		79	10	1	1497
30	6	2	10137		80	10	2	1444
31	6	1	9912		81	0	9	1422
32	0	5	9896		82	8	7	1313
33	5	6	9485		83	6	8	1279
34	6	3	9188		84	10	0	1257
35	2	6	8861		85	10	3	1173
36	1	6	8614		86	4	9	1163
37	3	6	7912		87	2	10	1154
38	6	4	7797		88	9	5	1150
39	6	0	7743		89	1	10	1118
40	6	5	6941		90	3	10	941
41	4	6	6638		91	5	9	911
42	7	1	6478		92	11	1	888
43	0	6	6382		93	0	10	845
44	7	2	6379		94	9	6	835
45	7	3	5638		95	10	4	829
46	2	7	5389		96	11	2	817
47	1	7	5358		97	4	10	759
48	7	0	5114		98	6	9	675
49	3	7	4589		99	1	11	670
50	7	4	4562		100	10	5	663

Notes: 1 million games were simulated.

Dodgers vs Padres - Simulation Results 3/30

                                             Top 100 Most Likely Final Scores

Rank	LAN	SD	Count		Rank	LAN	SD	Count
1	1	2	63372		51	7	4	3538
2	2	3	50442		52	8	1	3329
3	2	1	50127		53	1	8	3232
4	0	1	49629		54	8	2	3187
5	1	0	41080		55	6	7	2959
6	3	2	38846		56	4	7	2914
7	0	2	33625		57	7	5	2820
8	1	3	32681		58	8	0	2766
9	2	0	32648		59	2	8	2751
10	3	1	32492		60	0	8	2678
11	3	4	30468		61	8	3	2662
12	0	3	26056		62	7	6	2331
13	4	3	23522		63	5	7	2247
14	3	0	23089		64	3	8	2153
15	4	2	22796		65	9	1	2023
16	2	4	22177		66	8	4	1957
17	1	4	22148		67	9	2	1851
18	4	1	21139		68	1	9	1736
19	0	4	18623		69	2	9	1640
20	4	5	15670		70	9	0	1617
21	4	0	15404		71	4	8	1561
22	1	5	14530		72	8	5	1548
23	5	2	13876		73	9	3	1539
24	2	5	13777		74	0	9	1518
25	5	1	13404		75	3	9	1242
26	5	3	13113		76	5	8	1175
27	0	5	12291		77	10	1	1170
28	5	4	12112		78	8	6	1130
29	3	5	11725		79	7	8	1114
30	5	0	10538		80	9	4	1075
31	1	6	8849		81	10	2	1071
32	6	2	8717		82	1	10	975
33	6	1	8420		83	8	7	949
34	2	6	8242		84	2	10	929
35	0	6	7868		85	10	0	926
36	6	3	7620		86	0	10	877
37	6	0	7040		87	4	9	870
38	5	6	6993		88	9	5	867
39	3	6	6850		89	6	8	864
40	6	4	6337		90	10	3	848
41	6	5	5554		91	3	10	647
42	4	6	5519		92	9	6	623
43	7	1	5468		93	10	4	617
44	1	7	5440		94	11	1	605
45	7	2	5281		95	5	9	600
46	0	7	4752		96	11	2	567
47	2	7	4733		97	11	0	536
48	7	3	4570		98	1	11	535
49	7	0	4384		99	11	3	531
50	3	7	3839		100	2	11	509

Notes: Game simulated one million times.

Best Lineup - Pittsburgh Pirates

Next up on my look at each teams most efficient lineup is the Pittsburgh Pirates.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using ZIPs projections which are available on Fangraphs. The lineup results will only be as good as the projections.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.  In fact, I know that many of the results will not happen due to manager philosophies and veteran favoritism but these are the most optimal lineups that the simulator spit out.  In some cases the top couple of lineups had very small differences in results. The starting nine players were taken from MLBDepthCharts website.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays | Indians | Athletics | Astros | Red Sox
NL: Mets | Cubs | Padres | Marlins | Reds | Giants | Brewers | Dodgers

                   Pirates 2014 ZIPS Hitting Projections

	K%	BB%	AVE	OBP	SLG	OPS	wOBA
Jose Tabata	14.9%	7.4%	0.265	0.329	0.381	0.710	0.312
Starling Marte	23.8%	4.3%	0.265	0.318	0.430	0.748	0.324
Neil Walker	17.7%	8.3%	0.261	0.330	0.418	0.748	0.324
Gaby Sanchez	15.7%	10.0%	0.252	0.333	0.401	0.734	0.322
Andrew McCutchen	17.5%	11.3%	0.288	0.375	0.484	0.859	0.367
Pedro Alvarez	32.0%	8.9%	0.234	0.303	0.465	0.768	0.328
Russell Martin	19.4%	10.5%	0.234	0.326	0.384	0.710	0.315
Jordy Mercer	17.6%	5.7%	0.252	0.302	0.385	0.687	0.295

See the results after the jump

MLB 2014 Odds To Win World Series

World Series Odds as of 3/1/2014

Rank	Team	Odds to 1
1	Dodgers	6.5
2	Tigers	9
3	Cardinals	10
4	Red Sox	12
5	Yankees	12
6	Rays	12
7	Nationals	12
8	Giants	16
9	Rangers	16
10	Athletics	18
11	Braves	20
12	Angels	20
13	Reds	22
14	Pirates	28
15	Mariners	28
16	Orioles	33
17	Royals	33
18	Phillies	33
19	Blue Jays	33
20	Indians	40
21	Diamondbacks	50
22	Cubs	50
23	White Sox	50
24	Brewers	50
25	Padres	50
26	Mets	66
27	Rockies	75
28	Marlins	100
29	Twins	100
30	Astros	200

Source: Bovada

Best Lineup - Boston Red Sox

Next up on my look at each teams most efficient lineup is the Boston Red Sox.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using ZIPs projections which are available on Fangraphs. The lineup results will only be as good as the projections.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.  In fact, I know that many of the results will not happen due to manager philosophies and veteran favoritism but these are the most optimal lineups that the simulator spit out.  In some cases the top couple of lineups had very small differences in results. The starting nine players were taken from MLBDepthCharts website.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays | Indians | Athletics | Astros
NL: Mets | Cubs | Padres | Marlins | Reds | Giants | Brewers | Dodgers

                 Red Sox 2014 ZIPS Hitting Projections

	K%	BB%	AVE	OBP	SLG	OPS	wOBA
Shane Victorino	13.4%	6.6%	0.269	0.331	0.420	0.751	0.331
Dustin Pedroia	11.1%	9.3%	0.285	0.352	0.425	0.777	0.340
David Ortiz	15.5%	12.9%	0.296	0.386	0.552	0.938	0.377
Mike Napoli	30.5%	12.2%	0.241	0.342	0.466	0.808	0.350
Xander Bogaerts	23.6%	8.0%	0.267	0.331	0.429	0.760	0.333
Daniel Nava	20.8%	8.8%	0.257	0.344	0.384	0.728	0.322
Jonny Gomes	26.9%	11.4%	0.236	0.335	0.408	0.743	0.327
Jackie Bradley	23.3%	8.4%	0.245	0.322	0.375	0.697	0.308
Will Middlebrooks	26.0%	5.2%	0.249	0.291	0.425	0.716	0.312
A.J. Pierzynski	15.4%	2.9%	0.267	0.297	0.430	0.727	0.312

See the results after the jump

Best Lineup - Houston Astros

Next up on my look at each teams most efficient lineup is the Houston Astros.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using ZIPs projections which are available on Fangraphs. The lineup results will only be as good as the projections.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.  In fact, I know that many of the results will not happen due to manager philosophies and veteran favoritism but these are the most optimal lineups that the simulator spit out.  In some cases the top couple of lineups had very small differences in results. The starting nine players were taken from MLBDepthCharts website.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays | Indians | Athletics
NL: Mets | Cubs | Padres | Marlins | Reds | Giants | Brewers | Dodgers

                 Astros 2014 ZIPS Hitting Projections

	K%	BB%	AVE	OBP	SLG	OPS	wOBA
Dexter Fowler	28.3%	13.7%	0.247	0.354	0.382	0.736	0.328
L.J. Hoes	16.7%	8.4%	0.271	0.337	0.366	0.703	0.313
Jason Castro	24.9%	10.8%	0.256	0.337	0.439	0.776	0.335
Chris Carter	33.8%	11.4%	0.228	0.319	0.461	0.780	0.340
Marc Krauss	30.1%	10.3%	0.213	0.298	0.369	0.667	0.296
Robbie Grossman	27.1%	11.2%	0.228	0.321	0.330	0.651	0.289
Jonathan Villar	27.8%	7.3%	0.235	0.295	0.356	0.651	0.287
Jose Altuve	12.5%	5.1%	0.285	0.323	0.384	0.707	0.308
Matt Dominguez	15.8%	5.3%	0.249	0.294	0.395	0.689	0.300
Jesus Guzman	22.8%	8.5%	0.249	0.318	0.416	0.734	0.320
George Springer	31.1%	9.9%	0.237	0.319	0.439	0.758	0.335

See the results after the jump

Best Lineup - Los Angeles Dodgers

Next up on my look at each teams most efficient lineup is the Los Angeles Dodgers.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using ZIPs projections which are available on Fangraphs. The lineup results will only be as good as the projections.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.  In fact, I know that many of the results will not happen due to manager philosophies but these are the most optimal lineups that the simulator spit out.  In some cases the top couple of lineups had very small differences in results.  For NL teams it is always better to bat your pitcher eighth but I am not even going to go there in this exercise as I know there is a 0% chance of a MLB manager actually doing that.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays | Indians | Athletics
NL: Mets | Cubs | Padres | Marlins | Reds | Giants | Brewers

Dodgers 2014 Hitting ZIPS Projections:

	K%	BB%	AVE	OBP	SLG	OPS	wOBA
Matt Kemp	24.8%	8.6%	0.274	0.339	0.473	0.812	0.342
Yasiel Puig	19.7%	8.5%	0.284	0.354	0.485	0.839	0.359
Hanley Ramirez	17.3%	8.6%	0.276	0.342	0.486	0.828	0.357
Adrian Gonzalez	17.0%	8.0%	0.282	0.339	0.453	0.792	0.335
Carl Crawford	16.5%	5.2%	0.266	0.308	0.412	0.720	0.314
Alexander Guerrero	14.4%	7.5%	0.259	0.324	0.386	0.710	0.313
Juan Uribe	20.5%	6.1%	0.242	0.293	0.390	0.683	0.297
A.J. Ellis	18.3%	11.2%	0.244	0.337	0.367	0.704	0.306
Andre Ethier	19.5%	9.8%	0.261	0.340	0.405	0.745	0.319
Scott Van Slyke	25.1%	9.7%	0.249	0.325	0.420	0.745	0.325

See the results after the jump

Exportable 2014 MLB Schedule

Something that is difficult to find on the internet is an excel download file of the 2014 MLB Schedule.  I have taken the time to write a script that takes the 2014 MLB Schedule and converts it to an easy to use CSV file and uploaded it to google docs.  The file is free to download and can be found at the following link.

Here is the format that the file is in

Game ID,Year,Month,Day,Time,Away,Home

1,2014,3,22,4:00am,Dodgers,D-backs
2,2014,3,22,10:00pm,Dodgers,D-backs
3,2014,3,30,8:05pm,Dodgers,Padres
4,2014,3,31,1:05pm,Cubs,Pirates
5,2014,3,31,1:08pm,Royals,Tigers
6,2014,3,31,1:10pm,Nationals,Mets
7,2014,3,31,2:05pm,Phillies,Rangers
8,2014,3,31,2:10pm,Braves,Brewers
9,2014,3,31,3:05pm,Red Sox,Orioles
10,2014,3,31,4:10pm,Twins,White Sox
.
.
.
2428,2014,9,28,4:10pm,Angels,Mariners
2429,2014,9,28,4:10pm,Rockies,Dodgers
2430,2014,9,28,4:10pm,Cardinals,D-backs

Please keep in mind that game times and dates will be changing due to rain and I will not be maintaining these changes.

Best Lineup - Milwaukee Brewers

Next up on my look at each teams most efficient lineup is the Milwaukee Brewers.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using ZIPs projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays | Indians | Athletics
NL: Mets | Cubs | Padres | Marlins | Reds | Giants

See the results after the jump

Best Lineup - Oakland Athletics

Next up on my look at each teams most efficient lineup is the Oakland Athletics.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using ZIPs projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays | Indians
NL: Mets | Cubs | Padres | Marlins | Reds | Giants

See the results after the jump

Best Lineup - San Francisco Giants

Next up on my look at each teams most efficient lineup is the San Francisco Giants.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays | Indians
NL: Mets | Cubs | Padres | Marlins | Reds

See the results after the jump

Best Lineup - Cleveland Indians

Next up on my look at each teams most efficient lineup is the Cleveland Indians.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays
NL: Mets | Cubs | Padres | Marlins | Reds

See the results after the jump

Best Lineup - Cincinnati Reds

Next up on my look at each teams most efficient lineup is the Cincinnati Reds.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible, therefore I didn't attempt to let Joey Votto and Jay Bruce hit back to back.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays | Twins | Blue Jays
NL: Mets | Cubs | Padres | Marlins

See the results after the jump

Best Lineup - Toronto Blue Jays

Next up on my look at each teams most efficient lineup is the Toronto Blue Jays.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays | Twins
NL: Mets | Cubs | Padres | Marlins

See the results after the jump...

Best Lineup - Miami Marlins

Next up on my look at each teams most efficient lineup is the Miami Marlins.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays | Twins
NL: Mets | Cubs | Padres

See the results after the jump...

Best Lineup - Minnesota Twins

Next up on my look at each teams most efficient lineup is the Minnesota Twins.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays
NL: Mets | Cubs | Padres

See the results after the jump...

Best Lineup - San Diego Padres

Next up on my look at each teams most efficient lineup is the San Diego Padres.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest.  For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers | Rays
NL: Mets | Cubs

See the results after the jump...

Best Lineup - Tampa Bay Rays

Next up on my look at each teams most efficient lineup is the Tampa Bay Rays.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest. For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting too many LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers
NL: Mets | Cubs

See the results after the jump...

Best Lineup - Chicago Cubs

Next up on my look at each teams most efficient lineup is the Chicago Cubs.  In this exercise the methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers.  I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest. For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting LH back to back when reasonably possible.  Keep in mind, the results are not intended to match what a certain teams manager is most likely to do during the season.

Previous teams:
AL: Angels | Rangers
NL: Mets

See the results after the break.

Best Lineup - Texas Rangers

Next up on my look at most efficient lineups is the Texas Rangers.  I used my baseball simulator to run millions of games through various different possible lineup scenarios to see which lineup it spit out as the most likely to win a game vs a RH and LH pitcher.  Each lineup was simulated in 2.5 million games.

Please keep in mind that 2014 Steamer Projections were used as input, so if you don't like some of the results take it up with them.

Previous teams:
AL: Angels
NL: Mets

See the results after the break.

Best Lineup - New York Mets

Next up on my look at most efficient lineups is the New York Mets.  I used my baseball simulator to run millions of games through various different possible lineup scenarios to see which lineup it spit out as the most likely to win a game vs a RH and LH pitcher.  I tried my best to not stack left handed hitters and I always batted the pitcher 9th because no MLB manager will bat his pitcher 8th which is where most should hit.

Please keep in mind that 2014 Steamer Projections were used as input, so if you don't like some of the results take it up with them.

Previous teams:
AL: Angels
NL: None

See the results after the break.

Best Lineup - Los Angeles Angels

Not sure if this is going to be a series for all teams or just some teams, but I am going to kick things off with the best lineup for the Los Angeles Angels. The methodology is to use my simulator to find out which lineup wins the most games vs RH and LH pitchers. I do this by making the team of interest the "away" team, playing against a "make believe" team whose stats don't change from one sim to the next. In fact no stats (or input projections) change for either team, the only difference from one simulation to the next is the lineup of the team of interest. For player projections, I am using Steamer projections which are available on Fangraphs. The lineup results will only be as good as the projections.  I am not a subject matter expert on every teams personnel but I try to use MLBDepthcharts as a guidance as to which players are starters and I tend to avoid hitting LH back to back when reasonably possible.  Two million simulations make up the sample size.

See the results after the break.

Battle Of The Gold Gloves

One of the benefits of having a program that can accurately simulate a baseball game is that you can pretty much model anything and you can use the law of large numbers (or samples) to do the dirty work for you.  In my latest exercise, I decided to take the 2013 Gold Glove winners from both the National and American leagues and have them play against each other.  In order to make it fair, I ran sets of the simulation with each team being away/home and facing both a LH and RH starting pitcher.  I gave both teams the exact identical starting pitcher, bench and bullpen so that the only difference were the starting players.  I played by NL rules with no DH and gave both teams the same hitting skill for their pitcher.  And afterwards, I did the same thing but this time made all the players league average fielders to see which side was better solely on offense.

The simulator also allows me to determine the most efficient lineup for both teams (facing RHP and LHP).  The lineups that you see for both teams were the highest scoring lineups according to the simulator.  I put in a limitation of not batting any left handed hitters back to back as this seems to be something that most MLB managers follow and I always batted the pitcher ninth.

Here are the lineups

vs RHP	GGNL	GGAL		vs LHP	GGNL	GGAL
1	G.Parra	D.Pedroia		1	G.Parra	D.Pedroia
2	Y.Molina	A.Gordon		2	Y.Molina	S.Victorino
3	P.Goldschmidt	S.Victorino		3	P.Goldschmidt	E.Hosmer
4	C.Gonzalez	E.Hosmer		4	C.Gonzalez	A.Jones
5	N.Arenado	A.Jones		5	N.Arenado	A.Gordon
6	C.Gomez	S.Perez		6	A.Simmons	S.Perez
7	A.Simmons	M.Machado		7	B.Phillips	M.Machado
8	B.Phillips	J.Hardy		8	C.Gomez	J.Hardy
9	Pitcher	Pitcher		9	Pitcher	Pitcher

(GGNL - Gold Glove NL, GGAL - Gold Glove AL)

And here are the results

This table has all the players set to their defensive values.

Description	Away	Home	Winner	Away RS	Home RS	Win %	Total Runs
vs RHP	GGNL	GGAL	GGAL	3.43	3.32	50.42	6.75
vs RHP	GGAL	GGNL	GGNL	3.14	3.61	57.72	6.75
vs LHP	GGNL	GGAL	GGAL	3.48	3.36	50.37	6.84
vs LHP	GGAL	GGNL	GGNL	3.16	3.67	58.13	6.83

... and this table has all the players set to league average defensive values.

Description	Away	Home	Winner	Away RS	Home RS	Win %	Total Runs
vs RHP	GGNL	GGAL	GGAL	3.77	3.80	52.19	7.57
vs RHP	GGAL	GGNL	GGNL	3.60	3.97	56.33	7.57
vs LHP	GGNL	GGAL	GGAL	3.82	3.84	52.10	7.66
vs LHP	GGAL	GGNL	GGNL	3.63	4.04	56.69	7.67

Back Napkin Analysis:
It looks like the National League team is better both defensively and offensively.  Now keep in mind that the results will reflect the input data or player projections both offensively and defensively.  Not wanting to be biased, I used 2014 Steamer projections for the offense and I eye-balled the defensive values for each player from a mixture of UZR, FSR and Zips (if available).  I tended not to go above 15 runs saved per 150 games for any player.  Below are the defensive numbers I used for each player.

	NL	AL
C	Y.Molina (15)	S.Perez (13)
1B	P.Goldschmidt (4)	E.Hosmer (3)
2B	B.Phillips (8)	D.Pedroia (10)
3B	N.Arenado (12)	M.Machado (15)
SS	A.Simmons (15)	J.Hardy (10)
LF	C.Gonzalez (10)	A.Gordon (7)
CF	C.Gomez (13)	A.Jones (-3)
RF	G.Parra (12)	S.Victorino (15)

How Does BABIP Effect Run Scoring

It is pretty obvious, the higher a teams batting average on balls in play (BABIP) is the more runs they will score.  But the million dollar question is what is the relationship between BABIP and runs scored.  How many more or less runs can a team expect to score based on an increase or decrease in their BABIP.

When I posed this question to subject matter expert Tom Tango, he gave me the following answer.

You get +.75 runs for turning a sure out into a sure hit.

If you change BABIP from .300 to .301, you will get an extra .001 x .75 runs per ball in play.

If you assume that 70% of PA are balls in play, then changing BABIP from .300 to .301, you will get an extra .70 x .001 x .75 runs per PA.

If you have say 38 PA per game, then changing BABIP from .300 to .301 will get you an extra 38 x .70 x .001 x .75 runs per game.

So, 1 point in BABIP is .02 runs per game.

Naturally, this only works at very modest changes. If you go from .300 to .400, well, that 38 PA won’t hold. On top of which, you have compounding effects, so runs are not linear any more.

          *****     *****     *****     *****     *****
My intention all along was to use my simulator to figure this out but now I had a baseline to compare my results against.  Would the simulator come up with something close to the "1 point in BABIP is .02 runs per game"?

Where the power of the simulator comes in, it allows you to pick and choose your run environment and to change the BABIP of all pitcher/hitter matchups to any value all the while leaving all other variables the same.  Maybe the 0.02 runs per game only holds for a certain BABIP value?  By using BABIP numbers all the way from 0.000 to 1.000 the simulator should be able to show what kind of relationship BABIP and runs scores has on a basic x/y-line graph.  It can also zero in on specific ranges of BABIP that are more common in the major leagues.

Methodology
I don't want to overload this post with all the boring details (tldr) so I will give you the basics.  I created two teams Team A(way) and Team (H)ome making the teams fairly even and making their run environment at right around 8.2 combined runs (Away team = 4.4 rpg, 0.300 BABIP).  Since the away team bats in the 9th inning every game, I used them as the guinea pigs.  I hard-coded every single pitcher/hitter matchup for their team to have the same BABIP no matter what.  All other variables were held the same.  I would simulate 2.5 million games with the away team having a BABIP of 0.300 in one trial and then turn around and simulate 2.5 million games with the away team having a BABIP of 0.301 etc... then look at the results and see how the change in BABIP effected the total runs scored of the away team.  Now, I didn't simulate every single BABIP from 0.000 to 1.000 but I did simulate every BABIP from 0.300 to 0.340 and many of the points in between there and 0.000 and 1.000 in order to get a good graph of the relationship.

The graph above shows the runs scored for the Away team on the y-axis and their BABIP on the x-axis. This graph gives you a good look at how the run totals change for all values of team BABIP from 0.000 to 1.000. When looking at the entire BABIP spectrum the plot looks non-linear.

Next up (below) is a graph showing the same thing but zooming in on the more common BABIP range (from 0.290 to 0.350) and as you can tell the plot now becomes linear for all practical purposes.

Now let's take a look at which BABIP total Tom Tango's 0.02 run/game for a 0.001 of BABIP comes in at. The plot below gives you a pretty good idea.

You can tell from the plot that the 0.02 (run per game, for 1 point of BABIP) is somewhere in between 0.326 and 0.336. Anything below this range and you are looking at a number less than 0.02 for what 1 point of BABIP is worth and anything greater than 0.336 you are looking at a number greater than 0.02 for what 1 point of BABIP is worth.  This graph does have some noise in it, but you can still get a good idea of the trend.

So there is no one right answer without knowing the run environment you are in and what original BABIP you are using as a baseline.  If you use a run environment of around 4.4 runs per game (for the Away team) and a BABIP of 0.300 then one point (0.001) of BABIP is worth 0.0175 runs per game.  You don't see the 0.02 value until you raise the BABIP to over 0.326.

For the extremes you will see a runs/game value of around 0.01 when the BABIP is pegged at 0.150.  A BABIP of 0.400 will make one extra point of BABIP worth 0.025 runs per game.  A BABIP of 0.900 will make one extra point of BABIP worth 0.07 runs per game.

When you get to the extremes the type of hitters and pitchers you have plays a bigger role in what a point of BABIP is worth.  When you use a very small BABIP number, hitters who hit a lot of HRs become more important to offense as almost any ball put into play will become an out.  The defense will want a pitcher who does not have a tendancy to give up HRs.  When you use a very large BABIP number, hitters who do not strike-out often become very valuable as not many outs are made on balls in play and of course the defense will want a pitcher who strikes out a lot of hitters.

And finally, here is a table showing how often the Away team won the game based on what their BABIP was pegged to.

BABIP	Away Runs	Win %
0.000	1.3881	16.29%
0.100	1.9519	24.59%
0.200	2.9111	37.45%
0.300	4.3959	54.38%
0.400	6.5478	72.20%
0.500	9.5178	86.66%
0.600	13.4578	95.28%
0.700	18.5295	98.8766%
0.800	24.6065	99.8373%
0.900	31.3568	99.9871%
1.000	38.8769	99.9997%