Phillies vs Dodgers - Top 200 Most Likely Final Scores

Note: Out of one million games simulated.

Rank	PHI	LAN	Count		Rank	PHI	LAN	Count
1	1	2	57816		101	3	11	817
2	2	3	51597		102	11	2	784
3	0	1	40798		103	5	10	722
4	3	4	36095		104	10	5	718
5	2	1	35952		105	7	9	707
6	3	2	32823		106	9	7	701
7	1	3	31649		107	1	12	693
8	0	2	30175		108	11	1	668
9	3	1	25278		109	11	3	644
10	0	3	24892		110	4	11	625
11	1	0	24598		111	2	12	618
12	2	4	24485		112	9	8	603
13	1	4	23463		113	10	6	558
14	4	3	22525		114	0	12	551
15	4	5	20932		115	6	10	520
16	4	2	20851		116	11	4	504
17	2	0	20407		117	3	12	496
18	0	4	18529		118	11	0	472
19	4	1	17523		119	10	7	435
20	2	5	16927		120	5	11	415
21	1	5	16446		121	11	5	404
22	3	0	16308		122	12	2	404
23	3	5	14817		123	12	3	370
24	5	2	13601		124	9	10	368
25	0	5	13536		125	1	13	364
26	5	3	13198		126	12	1	364
27	5	4	12780		127	4	12	354
28	5	1	12088		128	7	10	354
29	4	0	11871		129	0	13	322
30	1	6	11136		130	2	13	319
31	5	6	10883		131	11	6	297
32	2	6	10719		132	10	8	293
33	3	6	9783		133	6	11	292
34	0	6	9120		134	3	13	286
35	6	2	8791		135	12	4	276
36	6	3	8454		136	8	10	271
37	5	0	8403		137	12	0	260
38	6	1	7931		138	5	12	255
39	4	6	7763		139	12	5	234
40	6	4	7347		140	13	2	229
41	1	7	7247		141	13	1	225
42	2	7	6923		142	10	9	224
43	6	5	6646		143	11	7	218
44	0	7	6236		144	13	3	214
45	3	7	5767		145	1	14	205
46	7	2	5579		146	4	13	205
47	6	0	5566		147	2	14	192
48	7	1	5240		148	7	11	187
49	7	3	5181		149	0	14	174
50	6	7	5025		150	11	8	174
51	4	7	4801		151	13	4	174
52	1	8	4723		152	8	11	169
53	2	8	4468		153	13	0	155
54	7	4	4368		154	12	6	153
55	0	8	3958		155	5	13	149
56	5	7	3718		156	3	14	147
57	7	0	3678		157	6	12	132
58	7	5	3629		158	10	11	127
59	8	2	3624		159	13	5	124
60	3	8	3442		160	7	12	123
61	8	1	3287		161	11	9	121
62	8	3	3225		162	14	1	115
63	7	6	3214		163	14	3	112
64	1	9	3032		164	1	15	109
65	2	9	2790		165	9	11	107
66	4	8	2744		166	14	2	107
67	8	4	2695		167	2	15	106
68	0	9	2588		168	4	14	106
69	8	0	2352		169	0	15	102
70	3	9	2281		170	12	7	102
71	9	2	2201		171	14	4	102
72	7	8	2151		172	11	10	93
73	5	8	2144		173	6	13	86
74	8	5	2065		174	3	15	85
75	9	1	2001		175	14	5	84
76	9	3	1967		176	5	14	82
77	2	10	1836		177	13	6	81
78	1	10	1781		178	14	0	80
79	8	6	1747		179	8	12	79
80	4	9	1662		180	4	15	72
81	6	8	1658		181	12	8	72
82	9	4	1582		182	1	16	69
83	0	10	1558		183	13	7	69
84	3	10	1469		184	12	9	66
85	9	0	1452		185	6	14	64
86	8	7	1427		186	14	6	64
87	10	2	1265		187	15	3	64
88	9	5	1222		188	7	13	63
89	5	9	1158		189	15	1	58
90	10	1	1129		190	15	2	56
91	1	11	1118		191	9	12	52
92	10	3	1080		192	3	16	51
93	9	6	1006		193	5	15	51
94	2	11	992		194	2	17	48
95	4	10	981		195	0	16	47
96	8	9	960		196	11	12	45
97	10	4	919		197	13	8	45
98	0	11	918		198	2	16	44
99	6	9	904		199	12	10	44
100	10	0	837		200	15	0	42

Phillies vs Dodgers Simulation Results - June 29th

Introducing Win Probability Added Over Expectation OR WPAOE

This conversation over at The Book Blog about why Fangraphs, who posts live Win Expectancy does not take into consideration the actual players who are playing in the game got me thinking. Fangraphs uses a one sized fits all chart for things like Win Expectancy (WE), Win Probability Added (WPA) and Leverage Index (LI). The problem is you are getting the exact same WE for a 0-0 first inning game with the Astros (Dallas Keuchel) facing the Tigers in Detroit with Justin Verlander or Max Scherzer pitching as you would for two evenly pitted teams.  You are also getting incorrect WPA's for each game event.  These discrepancies last throughout the entire game. For those wanting to get a true in game Win Expectancy or Win Probability Added you aren't going to get one all the time.  To others it might not be a big deal as the one size fits all chart is somewhat reasonable.  But it is uninteresting when you can get close to the real thing. Why not shoot for the real thing, when it can be done? Why use two sticks to make fire when you have a lighter?

While I think it should be a goal to do better on the in game Win Expectancies, I can see some usefullness of the WPA as it tells you a story of what players earned what portions of a win or a loss.  I do however find it a little troubling that players are receiving too much or too little their fair share of WPA due to WPA not adjusting for strength/weakness of opponent.  If a game that should be a 75/25 WE game gets calculated as a 50/50 WE game then the stronger team is more likely than not going to be gaining 0.25 points of WPA that they shouldn't be getting.  That is why I am proposing a new variation of WPA, called Win Probability Added Over Expectation (WPAOE) in which a player will exceed expectation if he scores a WPAOE of over 0.0 (and less than 0.0 for not meeting expectation).  WPAOE will use a "more correct" version of Win Expectancy to calculate how much each game event (out, hit, stolen base etc...) changes the contribution each player made to the win or loss.

So how can you calculate the true in game win expectancy?  Well, this isn't something the average person can do.  The pre-game win expectancy can easily be calculated from Vegas odds.  But how do you adjust this in game WE as the game goes on?  You basically need a "good" baseball game simulator.  Something that sites like Fangraphs do not have.  Even if they had one, you would run in to the problem of how quickly could it compute a new in game Win Expectancy after every game event and for multiple games.  So there are some difficult technical hurdles, but I am laying the groundwork for how this could and should eventually be done.

In the example below, I took the 2013 game with the highest pre-game win expectancy and went through and calculated all the win expectancies and WPAOEs after each game event as an example of how I imagine this to look somewhere down the road.  As you see, the game starts out with the Tigers as a 78.23% favorite (SIM WE = Tigers WE), while Fangraphs starts the Tigers WE at 50.0%.  So in a nutshell, what I did here is simulate this game 1 million times at every new game state.  All win expectancies (FG, Sim) are listed with the Tigers percent chance of winning.  And yes, my simulator accounts for pretty much everything meaningful you can think of.

Pitcher	Player	Inn	Outs	Base	HOU Runs	DET Runs	Play	FG WE	SIM WE	FG WPA	SIM WPA
NA	NA	-	0	0	0	0		50	78.23	0	0
M.Scherzer	R.Grossman	T1	0	0	0	0	BB	46.5	75.93	0.035	0.023
M.Scherzer	J.Elmore	T1	0	1	0	0	KS	49.8	78.13	-0.033	-0.022
M.Scherzer	R.Grossman	T1	1	1	0	0	CS	53.8	80.48	-0.04	-0.0235
M.Scherzer	C.Pena	T1	2	0	0	0	F-8	54.8	81.26	-0.01	-0.0078
D.Keuchel	O.Infante	B1	0	0	0	0	KS	52.6	79.72	-0.022	-0.0154
D.Keuchel	T.Hunter	B1	1	0	0	0	F-9	51	78.29	-0.016	-0.0143
D.Keuchel	M.Cabrera	B1	2	0	0	0	F-9	50	76.97	-0.01	-0.0132
M.Scherzer	C.Carter	T2	0	0	0	0	F-3	52.4	78.11	-0.024	-0.0114
M.Scherzer	J.Martinez	T2	1	0	0	0	F-9	54	78.93	-0.016	-0.0082
M.Scherzer	C.Corporan	T2	1	0	0	0	HR	43.1	68.21	0.109	0.1072
M.Scherzer	J.Paredes	T2	2	0	1	0	KS	44.1	68.68	-0.01	-0.0047
D.Keuchel	P.Fielder	B2	0	0	1	0	4-3	41.6	66.49	-0.025	-0.0219
D.Keuchel	V.Martinez	B2	1	0	1	0	6-3	39.8	64.81	-0.018	-0.0168
D.Keuchel	J.Peralta	B2	2	0	1	0	BB	41.2	66.04	0.014	0.0123
D.Keuchel	M.Tuiasosopo	B2	2	1	1	0	1B	44.1	69.1	0.029	0.0306
D.Keuchel	B.Pena	B2	2	5	1	1	1B	54.5	79.42	0.104	0.1032
D.Keuchel	A.Garcia	B2	2	3	1	4	HR	80.3	95.61	0.258	0.1619
D.Keuchel	O.Infante	B2	2	0	1	4	6-3	79.7	95.43	-0.006	-0.0018
M.Scherzer	M.Dominguez	T3	0	0	1	4	KS	81.9	95.99	-0.022	-0.0056
M.Scherzer	M.Gonzalez	T3	1	0	1	4	4-3	83.3	96.31	-0.014	-0.0032
M.Scherzer	R.Grossman	T3	2	0	1	4	KC	84.2	96.51	-0.009	-0.002
D.Keuchel	T.Hunter	B3	0	0	1	4	F-9	83	96.16	-0.012	-0.0035
D.Keuchel	M.Cabrera	B3	1	0	1	4	1B	84.3	96.36	0.013	0.002
D.Keuchel	P.Fielder	B3	1	1	1	4	4-6-3	81.7	95.48	-0.026	-0.0088
M.Scherzer	J.Elmore	T4	0	0	1	4	2B	76	93.78	0.057	0.017
M.Scherzer	J.Elmore	T4	0	2	1	4	Balk	73.6	93.28	0.024	0.005
M.Scherzer	C.Pena	T4	0	4	2	4	1B	68.8	89.81	0.048	0.0347
M.Scherzer	C.Carter	T4	0	1	2	4	BB	61.8	86.08	0.07	0.0373
M.Scherzer	J.Martinez	T4	0	3	5	4	HR	37.5	59.19	0.243	0.2689
M.Scherzer	C.Corporan	T4	0	0	5	4	F-7	39.8	60.54	-0.023	-0.0135
M.Scherzer	J.Paredes	T4	1	0	5	4	2B	35.6	57.17	0.042	0.0337
M.Scherzer	J.Paredes	T4	1	2	5	4	CS	41.5	61.46	-0.059	-0.0429
M.Scherzer	M.Dominguez	T4	2	0	5	4	F-5	42.6	62.2	-0.011	-0.0074
D.Keuchel	V.Martinez	B4	0	0	5	4	KS	39.6	59.57	-0.03	-0.0263
D.Keuchel	J.Peralta	B4	1	0	5	4	BB	42.9	62.06	0.033	0.0249
D.Keuchel	M.Tuiasosopo	B4	1	1	5	4	1B	47.8	66.37	0.049	0.0431
D.Keuchel	B.Pena	B4	1	3	5	4	5-4-3	36	55.82	-0.118	-0.1055
M.Scherzer	M.Gonzalez	T5	0	0	5	4	3-1	38.4	57.18	-0.024	-0.0136
M.Scherzer	R.Grossman	T5	1	0	5	4	F-9	40.1	58.28	-0.017	-0.011
M.Scherzer	J.Elmore	T5	2	0	5	4	KS	41.3	58.94	-0.012	-0.0066
D.Keuchel	A.Garcia	B5	0	0	5	4	KS	37.9	55.28	-0.034	-0.0366
D.Keuchel	O.Infante	B5	1	0	5	4	F-8	35.5	52.14	-0.024	-0.0314
D.Keuchel	T.Hunter	B5	2	0	5	4	F-8	33.8	50	-0.017	-0.0214
M.Scherzer	C.Pena	T6	0	0	5	4	KC	36.3	51.51	-0.025	-0.0151
M.Scherzer	C.Carter	T6	1	0	5	4	KS	38.1	52.72	-0.018	-0.0121
M.Scherzer	J.Martinez	T6	2	0	5	4	6-3	39.4	53.43	-0.013	-0.0071
D.Keuchel	M.Cabrera	B6	0	0	5	4	F-8	35.4	52.57	-0.04	-0.0086
D.Keuchel	P.Fielder	B6	1	0	5	4	4-3	32.5	56.18	-0.029	0.0361
D.Keuchel	V.Martinez	B6	2	0	5	4	E-6	34.7	53.27	0.022	-0.0291
D.Keuchel	J.Peralta	B6	2	1	5	4	1B	38.2	52.01	0.035	-0.0126
D.Keuchel	M.Tuiasosopo	B6	2	3	5	5	1B	56.4	75.1	0.182	0.2309
T.Blackley	B.Pena	B6	2	3	5	5	F-4	50	69.53	-0.064	-0.0557
M.Scherzer	C.Corporan	T7	0	0	5	5	BB	44.1	65.45	0.059	0.0408
M.Scherzer	J.Paredes	T7	0	1	5	5	FC	49.7	69.18	-0.056	-0.0373
M.Scherzer	M.Dominguez	T7	1	1	5	5	F-8	54.6	71.75	-0.049	-0.0257
M.Scherzer	J.Paredes	T7	2	1	5	5	CS	58.8	73.51	-0.042	-0.0176
T.Blackley	A.Garcia	B7	0	0	5	5	F-5	54.9	72.2	-0.039	-0.0131
E.Gonzalez	O.Infante	B7	1	0	5	5	1B	59	74.95	0.041	0.0275
E.Gonzalez	O.Infante	B7	1	1	5	5	Pick Off	52.1	69.34	-0.069	-0.0561
E.Gonzalez	T.Hunter	B7	2	0	5	5	BB	54.2	70.48	0.021	0.0114
E.Gonzalez	M.Cabrera	B7	2	1	5	5	KS	50	65.44	-0.042	-0.0504
D.Smyly	M.Gonzalez	T8	0	0	5	5	KS	54.7	67.57	-0.047	-0.0213
D.Smyly	R.Grossman	T8	1	0	5	5	1B	49.8	65.64	0.049	0.0193
D.Smyly	J.Elmore	T8	1	1	5	5	1B	43.1	59.67	0.067	0.0597
D.Smyly	C.Pena	T8	1	3	5	5	KC	51.7	65.57	-0.086	-0.059
A.Alburquerque	R.Grossman	T8	2	3	5	5	WP	48.8	64.3	0.029	0.0127
A.Alburquerque	C.Carter	T8	2	6	5	5	KS	60.7	73.25	-0.119	-0.0895
W.Wright	P.Fielder	B8	0	0	5	5	KS	56.1	68.95	-0.046	-0.043
W.Wright	V.Martinez	B8	1	0	5	5	F-8	52.6	65.31	-0.035	-0.0364
H.Ambriz	J.Peralta	B8	2	0	5	5	KS	50	63.43	-0.026	-0.0188
A.Alburquerque	J.Martinez	T9	0	0	5	5	BB	41.8	61.48	0.082	0.0195
P.Coke	C.Corporan	T9	0	1	6	5	2B	11.6	13.43	0.302	0.4805
P.Coke	J.Paredes	T9	0	2	6	5	SAC 5-3	11.9	13.29	-0.003	0.0014
P.Coke	M.Dominguez	T9	1	4	7	5	SAC F-9	8.8	9.16	0.031	0.0413
P.Coke	M.Gonzalez	T9	2	0	7	5	1-3	9.2	11.67	-0.004	-0.0251
J.Veras	D.Kelly	B9	0	0	7	5	3-1	4.5	5.75	-0.047	-0.0592
J.Veras	B.Pena	B9	1	0	7	5	BB	10.7	13.43	0.062	0.0768
J.Veras	A.Dirks	B9	1	1	7	5	F-6	4.5	5.73	-0.062	-0.077
J.Veras	B.Pena	B9	2	1	7	5	DI	4.8	6.02	0.003	0.0029
J.Veras	O.Infante	B9	2	2	7	5	BB	9.1	11.87	0.043	0.0585
J.Veras	T.Hunter	B9	2	3	7	5	HBP	17.5	27.73	0.084	0.1586
J.Veras	M.Cabrera	B9	2	7	7	5	F-8	0	0	-0.175	-0.2773

Note: My post over at The Book Blog on this subject.

I’m still not convinced we need to treat each game with a 50/50 starting point for WPA, especially when you have a game where a team is a lopsided 78% favorite to win that game.  Why not give the favored team 22% of positive WPA to play with.  If Kershaw starts the game with the Dodgers having a 78% chance of winning and leaves the game with the Dodgers having a 78% chance of winning then he has “done his job” WPA-wise.  Why should he and the rest of his team get extra added WPA just because they are playing against a bad team and they have a great pitcher starting for them?  This seems worse.

0.0 WPA should be the baseline of expected WPA for each player and giving a team that is a 78% favorite 22% of positive WPA to play with does just that.  If you start them out at 50/50 and don’t take into consideration the likelihood of one team winning over the other then you are padding their positive WPA with an extra of 28% of positive WPA points on average.  This just doesn’t seem right.  So what if the winning team only accumulates 0.22 points of WPA and the losing team accumulates -0.22 points of WPA hen the favorite wins.  You are incorrectly assigning WPA to individual players by assuming every game is a 50/50 game.

If the initial Win Expectancy was correct and everything played out perfectly with the season simulated billions of times you’d expect to see everyone’s WPA converge to zero.  But the season is one sample size (162 games) and WPA is pretty much a “story” stat so what you will see are players who exceeded expecations with a WPA over zero and players who did not exceed expecations with a WPA of under zero and players who met expectations with a WPA of right around zero.

Maybe a new stat?  Win Probability Added Over Expectation (WPAOE) ??

Tangotiger Response:
Everything you are saying is correct.  If Kershaw does his job exactly as intended, such that they had a 78% chance of winning entering the game and 78% chance of winning when he leaves, then he’s accumulated 0.0 wins IN-GAME.

MLB World Series Odds - June 19th

Team	12/15/12 Odds	12/29/12 Odds	5/22/2013	6/20/2013
Tigers	7	7	7	6
Cardinals	14	14	11	6.5
Braves	16	16	9	7.5
Athletics	40	30	25	10
Rangers	10	14	7.25	11
Reds	10	12	13.5	11
Red Sox	30	30	14	12
Nationals	8	8	9.5	16
Orioles	50	50	25	16
Giants	14	14	13.5	18
Yankees	10	12	13.5	20
Diamdonbacks	40	50	35	20
Pirates	50	50	40	20
Blue Jays	10	10	35	22
Rays	20	16	25	25
Dodgers	7	7	27	33
Indians	60	80	30	33
Angels	7	7	33	33
Royals	30	40	40	33
Phillies	16	14	66	40
Rockies	100	100	66	40
Padres	100	100	150	40
White Sox	40	40	175	150
Twins	100	100	200	150
Mariners	60	80	175	200
Mets	60	100	200	250
Brewers	30	30	125	500
Cubs	60	50	300	500
Astros	100	100	2500	2500
Marlins	100	100	2500	5000

Dodgers vs Pirates - June 16th Simulation Results

Dodgers vs Pirates - June 15th Simulation Results

Dodgers vs Pirates - June 14th Simulation Results

Bullpen Win Probability Added Leaders by Team

Team	WPA
Rangers	5.62
Pirates	5.53
Yankees	3.75
Rockies	3.71
Athletics	3.58
Twins	3.50
Blue Jays	3.16
Diamondbacks	3.06
White Sox	2.92
Braves	2.51
Padres	1.89
Red Sox	1.87
Brewers	1.41
Angels	1.33
Nationals	0.63
Cardinals	0.39
Marlins	0.27
Royals	0.20
Giants	0.17
Reds	0.09
Tigers	0.00
Indians	-0.52
Orioles	-0.80
Phillies	-1.78
Rays	-2.32
Mariners	-2.66
Astros	-2.69
Mets	-2.91
Dodgers	-3.54
Cubs	-3.77

Source: Fangraphs

Diamondbacks vs Dodgers - June 11th Simulation Results

Diamondbacks vs Dodgers - June 10th Simulation Results

Braves vs Dodgers - June 8th Simulation Results

Braves vs Dodgers - June 7th Simulation Results

Braves vs Dodgers - June 6th Simulation Results

Padres vs Dodgers - June 5th Simulation Results

The simulator has the Dodgers as a heavy favorite to win the game tonight. Vegas odds are right in line with the simulation results as Vegas gives the Dodgers right around a 67% chance of winning this game.

Dodgers vs Angels - May 30th Simulation Results

Angels vs Dodgers - May 28th Simulation Results

Angels vs Dodgers - Simulation Results May 27th

Time For Matt Kemp To Take A Break?

Coming in to the 2013 Dodgers season, as most Dodgers fans were busy ordering playoff tickets and getting fitted for Championship t-shirts, I was saying to myself that I didn't want to "win this way" and more importantly I didn't want to "lose this way".  Well, here we are only at the end of May but the Dodgers are definitely "losing this way".  Yeah, there are lots of problems, too many for me to cover but the one that really sticks out is Matt Kemp being a shell of his former self at the plate.

2013 Matt Kemp
- wOBA 0.285
- OPS 0.656

Those are awful numbers.  Those are the numbers are number four hitter has been putting up for the first two months of the season.  And apparently Matt Kemp is not in a slump.  He is not unlucky.  He is just not good at hitting for the time being.

What's wrong with Matt Kemp?  Who knows.  I will leave that for the medical staff, hitting coaches and sports psychologists out there.  But what I do know, is that you cannot have your number three hitter putting up an OPS of 0.656 over a two month period with NO sign that a turn around is on the horizon.

Here are some options, and there are likely a few more.  In draconian order.

1. Put Matt Kemp on the DL and send him to the minors to rehab.

Pros: Matt Kemp is "hurting" this team and the team with its payroll is in a win now mode.  Matt Kemp is weighting down this ship.  Call up Joc Pederson who is tearing up AA and let Skip Schumaker back him up.

Cons: Nobody seems to know for sure what is causing Matt Kemp not to hit and nobody seems to know what course of action will help him rebound.  Matt's ego.

2. Bench Matt Kemp

Pros: He can rest a little bit and still see the occasional major league pitcher while working on his swing in the cages with hitting coach Mark McGwire.  He won't be making as many outs.  Joc Pederson or someone from the minors gets a chance.

Cons:  Nobody seems to know for sure what is causing Matt Kemp not to hit and nobody seems to know what course of action will help him rebound.  Matt's ego.

3. Drop Matt Kemp in the batting order.

Pros: Matt Kemp still gets to see major league pitching but perhaps does less damage to the Dodgers offense batting 7th or 8th in the order.

Cons:  Nobody seems to know for sure what is causing Matt Kemp not to hit and nobody seems to know what course of action will help him rebound. Matt's ego.

The biggest problem to me is the fact that nobody seems to know for sure what is Matt Kemp's problem at the plate.  Is it physical or is it mental or somewhere in between.  And if so, what is the right path to take.  This team was built to win NOW.  The payroll is enormous.  The pressure to win is enormous.  This team already has enough dead weight.  We have two very good outfielders in the minor leagues.  There would obviously be some risk in calling them up to play a tough defensive position and Carl Crawford can probably no longer play CF due to his weak throwing arm.  I would start at #3 on my list and every 7-10 days move up a notch.  Moving him down in the lineup will not help him get better but if you are going to play him every day just because he supposedly needs to play to get better/feel better (w/e that means) then for the love of God drop in the batting order.  If no improvement is made (which there likely won't be) then bench him and let him rest and work out the kinks out of the batting order where he cannot hurt this team.  If that doesn't work then send his butt down to the minors to work it out.  I wish him the best, but this team needs to win now and it isn't likely to happen with Matt Kemp playing every day and batting fourth in the lineup.

Cardinals vs Dodgers - Simulation Results May 26th

Cardinals vs Dodgers - Simulation Results May 25th

Cardinals vs Dodgers - May 24th Simultion Results

2013 World Series Odds

Team	12/15/12 Odds	12/29/12 Odds	5/22/2013
Tigers	7	7	7
Rangers	10	14	7.25
Braves	16	16	9
Nationals	8	8	9.5
Cardinals	14	14	11
Yankees	10	12	13.5
Reds	10	12	13.5
Giants	14	14	13.5
Red Sox	30	30	14
Rays	20	16	25
Athletics	40	30	25
Orioles	50	50	25
Dodgers	7	7	27
Indians	60	80	30
Angels	7	7	33
Blue Jays	10	10	35
Diamdonbacks	40	50	35
Royals	30	40	40
Pirates	50	50	40
Phillies	16	14	66
Rockies	100	100	66
Brewers	30	30	125
Padres	100	100	150
White Sox	40	40	175
Mariners	60	80	175
Mets	60	100	200
Twins	100	100	200
Cubs	60	50	300
Marlins	100	100	2500
Astros	100	100	2500

Note: All odds are "to 1", ie - 60 is 60-1 odds.

Dodgers vs Brewers - Simulation Results May 22nd

Dodgers vs Brewers - May 21st Simulation Results

Dodgers vs Brewers Simulation Results - May 20th

Dodgers vs Braves - May 19th Simulation Results

Vegas Top Favorites

Date	Away	Home	Away SP	Home SP	Vegas Fave	ML Fave	ML Dog	Vegas Win Exp	Result
5/15/2013	HOU	DET	Dan Keuchel	Max Scherzer	DET	-360	325	77.40%	HOU 7-5
5/14/2013	HOU	DET	Lucas Harrell	Doug Fister	DET	-285	265	73.33%	DET 6-2
5/13/2013	HOU	DET	Bud Norris	Anibal Sanchez	DET	-275	257	72.68%	DET 7-2
4/30/2013	HOU	NYA	Phillip Humber	Hiroki Kuroda	NYA	-273	256	72.57%	NYA 7-4
4/27/2013	HOU	BOS	Brad Peacock	Felix Doubront	BOS	-265	250	72.03%	BOS 8-4
5/5/2013	DET	HOU	Justin Verlander	Phillip Humber	DET	-260	245	71.63%	DET 9-0
4/1/2013	MIA	WAS	Ricky Nolasco	Stephen Strasburg	WAS	-258	244	71.51%	WAS 2-0
4/3/2013	MIA	WAS	Kevin Slowey	Gio Gonzalez	WAS	-258	244	71.51%	WAS 3-0
4/25/2013	HOU	BOS	Phillip Humber	Clay Buchlz	BOS	-250	240	71.01%	BOS 7-2
4/30/2013	MIN	DET	Vance Worley	Justin Verlander	DET	-250	240	71.01%	DET 6-1

Dodgers vs Braves - May 17th Simulation Results

Marlins vs Dodgers - May 10th Simulation Results

Diamondbacks vs Dodgers - May 8th Simulation Results

Diamondbacks vs Dodgers - May 7th Simulation Results

Environmental Friendly Dodgers Busy Conserving Power

Normally conserving power is a good thing.  We all want cleaner air to breath, right?  Well, the 2013 version of the Los Angeles Dodgers - the same Dodgers that are highly paid if I remember my numbers correctly are doing their part in conserving power.  Their bats have been recycled and sent to wind farms in the Far East.  Their homerun trots have been outsourced to Cleveland and most notably their winning percentage is at a 52 week low.  So what's going on?  Heck if I (or Mark McGwire for that matter) know - but here are the numbers and they will make you feel green.

First let's look at the pathetic

Name	HR
Carl Crawford	5
Adrian Gonzalez	3
Andre Ethier	3
A.J. Ellis	2
Juan Uribe	2
Mark Ellis	2
Clayton Kershaw	1
Hanley Ramirez	1
Jerry Hairston	1
Justin Sellers	1
Matt Kemp	1

Yes, that is right. Matt Kemp only has one HR. Luckily, Carl Crawford has come out of his shell this season to lead the team with five homeruns. Perhaps Crawford shouldn't be hitting in front of the pitcher for the time being, no?

Well, how is the rest of the league doing? 22 homeruns is probably middle of the pack or something, don't you think? Hmmm, not quite.

More Leaderboards

Rank	Team	HR	Team	OBP	Team	SLG	Team	wOBA
1	Indians	44	Rockies	0.352	Indians	0.476	Rockies	0.356
2	Braves	44	Tigers	0.352	Rockies	0.471	Indians	0.352
3	Rockies	43	Red Sox	0.347	Red Sox	0.451	Red Sox	0.347
4	Blue Jays	41	Athletics	0.343	Yankees	0.433	Tigers	0.34
5	Yankees	40	Indians	0.34	Braves	0.429	Athletics	0.332
6	Orioles	39	Dodgers	0.333	Tigers	0.428	Yankees	0.33
7	Cubs	39	Reds	0.33	Brewers	0.427	Braves	0.328
8	Rays	37	Rangers	0.33	Orioles	0.425	Rangers	0.328
9	Red Sox	36	Yankees	0.328	Rangers	0.42	Brewers	0.324
10	Brewers	36	Braves	0.323	Athletics	0.413	Orioles	0.323
11	Rangers	36	Royals	0.323	Cubs	0.412	Rays	0.318
12	White Sox	36	Brewers	0.322	Diamondbacks	0.409	Diamondbacks	0.313
13	Diamondbacks	35	Orioles	0.322	Rays	0.407	Cardinals	0.312
14	Mariners	35	Cardinals	0.321	Angels	0.403	Angels	0.312
15	Pirates	34	Giants	0.32	Blue Jays	0.399	Royals	0.311
16	Athletics	33	Twins	0.32	Pirates	0.397	Giants	0.31
17	Mets	33	Rays	0.317	Mets	0.397	Reds	0.31
18	Angels	32	Angels	0.316	Astros	0.393	Pirates	0.308
19	Tigers	31	Diamondbacks	0.315	Giants	0.39	Dodgers	0.308
20	Nationals	30	Padres	0.314	Royals	0.39	Mets	0.308
21	Astros	30	Mets	0.309	Cardinals	0.39	Cubs	0.308
22	Reds	28	Mariners	0.308	Mariners	0.387	Mariners	0.306
23	Phillies	28	Pirates	0.307	Nationals	0.381	Blue Jays	0.306
24	Cardinals	27	Astros	0.305	White Sox	0.379	Astros	0.306
25	Giants	25	Cubs	0.301	Reds	0.378	Padres	0.305
26	Padres	25	Blue Jays	0.301	Padres	0.378	Twins	0.305
27	Dodgers	22	Phillies	0.298	Phillies	0.377	Nationals	0.294
28	Twins	21	Nationals	0.295	Twins	0.366	Phillies	0.294
29	Marlins	19	Marlins	0.29	Dodgers	0.363	White Sox	0.289
30	Royals	16	White Sox	0.283	Marlins	0.325	Marlins	0.273

Yeah, that's pretty ugly. 27th out of 30, 14th out of 15 (NL) in homeruns. Thank God for teams like the Marlins. In slugging percentage the Dodgers drop to 29th, once again with the Marlins cushioning the fall. Is there a silver lining you ask? Well, of course there is. Look at the Dodgers on base percentage. They are 6th! Good news, the offense isn't totally pathetic - just the part where we get runners past first base is. Dodgers management needs to put the breaks on this power conservation policy. Puig in a few more bats, oops did I incorrectly spell 'plug'? Silly me. Gas up the batting cages and turn this ship around.

Diamondbacks vs Dodgers - May 6th Simulation Results

Dodgers vs Giants - May 5th Simulation Results

Dodgers vs Giants - May 4th Simulation Results

Dodgers vs Giants - May 3rd Simulation Results

Brewers vs Dodgers - April 28th Simulation Results

Brewers vs Dodgers - April 27th Simulation Results

Brewers vs Dodgers - April 25th Simulation Results

Dodgers vs Mets - April 25th Simulation Results

Dodgers vs Mets - April 24th Simulation Results

Dodgers vs Orioles - April 21st Simulation Results

Dodgers vs Orioles - April 20th Simulation Results

Dodgers vs Orioles - April 19th Simulation Results

Top 20 Most Likely Final Scores
1. BAL 3-2
2. BAL 4-3
3. BAL 5-4
4. BAL 2-1
5. LAN 4-3
6. LAN 3-2
7. BAL 6-5
8. BAL 4-2
9. LAN 4-2
10. LAN 5-4
11. BAL 3-1
12. LAN 2-1
13. BAL 5-3
14. LAN 5-3
15. LAN 3-1
16. BAL 5-2
17. BAL 4-1
18. LAN 5-2
19. LAN 4-1
20.LAN 6-5

Padres vs Dodgers - April 17th Simulation Results

Top 20 Most Likely Final Scores
1. LAN 2-1
2. LAN 3-2
3. LAN 1-0
4. LAN 2-0
5. LAN 3-0
6. LAN 3-1
7. SD 2-1
8. LAN 4-3
9. LAN 4-0
10. LAN 4-1
11. SD 3-2
12. LAN 4-2
13. SD 1-0
14. LAN 5-0
15. LAN 5-1
16. SD 4-3
17. SD 3-1
18. LAN 5-2
19. LAN 6-0
20. SD 2-0

Padres vs Dodgers - April 16th Simulation Results

Top 25 Most Likely Final Scores
1. LAN 3-2
2. LAN 2-1
3. LAN 4-3
4. SD 3-2
5. SD 2-1
6. LAN 3-1
7. LAN 1-0
8. LAN 4-2
9. SD 4-3
10. LAN 5-4
11. LAN 4-1
12. SD 3-1
13. LAN 2-0
14. SD 4-2
15. LAN 3-0
16. LAN 5-2
17. SD 1-0
18. LAN 5-3
19. LAN 5-1
20.LAN 4-0
21. SD 2-0
22. SD 5-4
23. SD 5-3
24. SD 4-1
25. LAN 6-5

Padres vs Dodgers - April 15th Simulation Results

Top 20 Most Likely Final Scores
1. LAN 3-2
2. LAN 2-1
3. LAN 4-3
4. LAN 3-1
5. SD 2-1
6. SD 3-2
7. LAN 1-0
8. LAN 4-2
9. LAN 4-1
10. LAN 2-0
11. LAN 3-0
12. SD 4-3
13. LAN 4-3
14. LAN 4-0
15. LAN 5-1
16. LAN 5-2
17. SD 3-1
18. SD 1-0
19. SD 4-2
20. LAN 5-3

Dodgers vs Diamondbacks - April 12th Simulation Results

Top 20 Most likely Final Scores:
1. ARI 3-2
2. ARI 4-3
3. ARI 5-4
4. ARI 2-1
5. LAN 3-2
6. LAN 4-3
7. ARI 6-5
8. LAN 4-2
9. ARI 4-2
10. LAN 5-4
11. LAN 3-1
12. LAN 2-1
13. LAN 5-3
14. LAN 5-2
15. LAN 4-1
16. ARI 3-1
17. ARI 5-3
18. ARI 7-6
19. ARI 4-1
20. LAN 5-1

Note: There will be no Saturday/Sunday Simulations.


Dodgers vs Padres - April 11th Simulation Results

Simulator:
Dodgers Win Expectancy: 55.35%
Dodgers RS: 3.26
Padres RS: 2.60

Vegas
Dodgers Win Expectancy: 60.86%
Total Runs: ~ 6.8

Top 20 Most Likely Final Scores
1. SD 2-1
2. SD 3-2
3. LAN 2-1
4. SD 1-0
5. LAN 3-2
6. LAN 3-1
7. SD 4-3
8. LAN 1-0
9. LAN 2-0
10. LAN 3-0
11. SD 3-1
12. LAN 4-1
13. SD 2-0
14. LAN 4-2
15. LAN 4-3
16. LAN 4-0
17. SD 4-2
18. LAN 5-1
19. SD 5-4
20. LAN 5-2
.
.
100. LAN 9-7

Dodgers vs Padres - April 10th Simulation Results

Once again, another screenshot of today's simulation box score along with the top 20 most likely scores.

Simulator:
Dodgers Win Expectancy: 53.91%
Dodgers RS: 3.43
Padres RS: 2.87

Vegas
Dodgers Win Expectancy: 53.27%
Total Runs: ~ 6.8

Top 20 Most Likely Final Scores
1. SD 2-1
2. SD 3-2
3. LAN 2-1
4. LAN 3-2
5. SD 1-0
6. SD 4-3
7. LAN 3-1
8. LAN 1-0
9. SD 3-1
10. LAN 2-0
11. LAN 4-2
12. LAN 4-3
13. LAN 4-1
14. SD 2-0
15. LAN 3-0
16. SD 4-2
17. SD 5-4
18. LAN 4-0
19. LAN 5-2
20. LAN 5-1
.
.
100. LAN 10-6


(Click image for larger size)

 



Dodgers vs Padres - April 9th Simulation Results

Here are the simulation results for the April 9th game between the Dodgers and the Padres

Dodgers vs Padres
Josh Beckett vs Clayton Richards

Record: 4-2 (0.667)
RS: 17
RA: 10
Pythag Win%: 0.725
Pythag Pace: 117.5 - 44.5
Pythag Record: 4.35 - 1.65
Record As Favorites: 4-2
Record As Underdogs: 0-0

Simulation Results Notes:
Dodgers win expectancy is 49.52%
Dodgers on average score 3.315 runs
Padres on average score 3.104 runs
Total Runs scored on average: 6.419

Starting Pitching Lines:
Josh Beckett - 6.4ip, 6.4so, 2.1bb, 5.4hits, 0.58hrs, 100pitches, 3.395fip
Clayton Richard - 6.4ip, 4.7so, 1.8bb, 6.3hits, 0.58hrs, 98pitches, 3.718fip

Some numbers from the Vegas odds:
Dodgers win expectancy: 57.89%
Over/Under: 6.5 -112o +102u

Top 20 Most Likely Final Scores:
1. Padres 2-1
2. Padres 3-2
3. Dodgers 2-1
4. Padres 4-3
5. Dodgers 3-2
6. Padres 1-0
7. Dodgers 3-1
8. Padres 3-1
9. Dodgers 4-3
10. Dodgers 4-2
11. Dodgers 1-0
12. Padres 4-2
13. Padres 2-0
14. Padres 5-4
15. Dodgers 2-0
16. Dodgers 4-1
17. Padres 4-1
18. Dodgers 3-0
19. Padres 3-0
20. Dodgers 5-2
.
.
99. Dodgers 11-4

Dodgers Box Score (Click Image To Enlarge)

Pirates vs Dodgers - April 6th Simulation Results

Here are the simulation results for the April 6th game between the Pirates and the Dodgers.

Pirates vs Dodgers
A.J. Burnett vs Clayton Kershaw

Record: 2-2 (0.500)
RS: 10
RA: 8
Pythag Win%: 0.601
Pythag Pace: 97.3 - 64.7
Pythag Record: 2.40 - 1.60
Record As Favorites: 2-2
Record As Underdogs: 0-0

Simulation Results Notes:
Dodgers win expectancy is 64.84%
Pirates on average score 2.500 runs
Dodgers on average score 3.542 runs
Total Runs scored on average: 6.042

Starting Pitching Lines:
A.J. Burnett - 6.08ip, 6.3so, 2.0bb, 6.0hits, 0.63hrs, 98pitches, 3.454fip
Clayton Kershaw - 6.7ip, 9.1so, 1.8bb, 5.1hits, 0.46hrs, 105pitches, 2.201fip

Some numbers from the Vegas odds:
Dodgers win expectancy: 69.23%
Over/Under: 6.0 -105o -105u
Notes: Dodgers are the largest Vegas favorite of the day.

Top 20 Most Likely Final Scores:
1. Dodgers 2-1
2. Dodgers 3-2
3. Dodgers 1-0
4. Pirates 2-1
5. Dodgers 4-3
6. Dodgers 3-1
7. Dodgers 2-0
8. Dodgers 3-0
9. Pirates 3-2
10. Dodgers 4-1
11. Dodgers 4-2
12. Pirates 1-0
13. Dodgers 4-0
14. Pirates 3-1
15. Pirates 4-3
16. Dodgers 5-4
17. Pirates 2-0
18. Dodgers 5-1
19. Dodgers 5-2
20. Pirates 4-2
.
.
100. Dodgers 12-0

Pirates vs Dodgers - April 5th Simulation Results

Here are the simulation results for the April 5th game between the Pirates and the Dodgers.

Pirates vs Dodgers
Jonathan Sanchez vs Zack Greinke

Record: 1-2 (0.333)
RS: 7
RA: 8
Pythag Win%: 0.439
Pythag Pace: 71.2 - 90.8
Pythag Record: 1.32 - 1.68
Record As Favorites: 1-2
Record As Underdogs: 0-0

Simulation Results Notes:
Dodgers win expectancy is 67.68%
Pirates on average score 2.728 runs
Dodgers on average score 4.166 runs
Total Runs scored on average: 6.894

Starting Pitching Lines:
Jonathan Sanchez - 5.2ip, 5.2so, 3.3bb, 5.3hits, 0.59hrs, 93pitches, 4.593fip
Zack Greinke - 6.8ip, 8.9so, 1.5bb, 5.4hits, 0.60hrs, 104pitches, 2.381fip

Some numbers from the Vegas odds:
Dodgers win expectancy: 65.81%
Over/Under: 7.0 +100o -110u
Notes: Dodgers are the largest Vegas favorite of the day.

Top 20 Most Likely Final Scores:
1. Dodgers 3-2
2. Dodgers 2-1
3. Dodgers 4-3
4. Dodgers 3-1
5. Dodgers 1-0
6. Pirates 2-1
7. Pirates 3-2
8. Dodgers 4-2
9. Dodgers 4-1
10. Dodgers 2-0
11. Dodgers 3-0
12. Dodgers 5-4
13. Dodgers 4-0
14. Pirates 4-3
15. Dodgers 5-1
16. Dodgers 5-2
17. Pirates 3-1
18. Pirates 1-0
19. Dodgers 5-3
20. Dodgers 5-0
.
.
99. Dodgers 12-3

Giants vs Dodgers - Simulation Results April 3

Here are the simulation results for the April 2nd game between the Giants and the Dodgers.

Giants vs Dodgers
Tim Lincecum vs Josh Beckett

Record: 1-1 (0.500)
RS: 4
RA: 3
Pythag Record: 1.28 - 0.72 (0.640)
Record As Favorites: 1-1
Record As Underdogs: 0-0

Simulation Results Notes:
Dodgers win expectancy is 49.44%
Giants on average score 3.572 runs
Dodgers on average score 3.371 runs
Total Runs scored on average: 6.943

Starting Pitching Lines:
Tim Lincecum - 6.0ip, 6.9so, 2.5bb, 5.5hits, 0.55hrs, 99pitches, 3.351fip
Josh Beckett - 6.2ip, 6.2so, 2.0bb, 6.1hits, 0.59hrs, 99pitches, 3.390fip

Some numbers from the Vegas odds:
Dodgers win expectancy: 54.65%
Over/Under: 6.5 -110o +100u

Top 20 Most Likely Final Scores:
1. Dodgers 3-2
2. Dodgers 2-1
3. Dodgers 4-3
4. Giants 3-2
5. Giants 2-1
6. Giants 3-1
7. Giants 4-3
8. Giants 4-2
9. Dodgers 1-0
10. Dodgers 3-1
11. Dodgers 5-4
12. Dodgers 4-2
13. Giants 2-0
14. Giants 4-1
15. Giants 1-0
16. Dodgers 2-0
17. Dodgers 4-1
18. Giants 5-4
19. Giants 5-3
20. Giants 3-0
.
.
100. Dodgers 9-7

Giants vs Dodgers - Simulation Results April 2

Here are the simulation results for the April 2nd game between the Giants and the Dodgers.

Giants vs Dodgers
Madison Bumgarner vs Hyun-Jin Ryu

Record: 1-0
RS: 4
RA: 0
Pythag Record: Infinity
Record As Favorites: 1-0
Record As Underdogs: 0-0

Simulation Results Notes:
Dodgers win expectancy is 48.89%
Giants on average score 3.395 runs
Dodgers on average score 3.127 runs
Total Runs scored on average: 6.522

Starting Pitching Lines:
Madison Bumgarner - 6.4ip, 6.8so, 1.6bb, 6.2hits, 0.56hrs, 101pitches, 2.982fip
Hyun-Jin Ryu - 6.2ip, 6.6so, 2.3bb, 5.8hits, 0.50hrs, 100pitches, 3.233fip

Giants players most likely to hit a HR: Buster Posey, Hunter Pence, Pablo Sandoval
Dodgers players most likely to hit a HR: Matt Kemp, Adrian Gonzalez, Andre Ethier

Giants hitters most strikeouts: Brandon Belt (1.4 times), Gregor Blanco (1.2), Hunter Pence (1.1)
Dodgers hitters most strikeouts: Carl Crawford (1.2 times), Matt Kemp (1.2), Andre Ethier (1.2)

Giants hitter most walks: Buster Posey (0.51 times)
Dodgers hitter most walks: AJ Ellis (0.48 times)

Some numbers from the Vegas odds:
Dodgers win expectancy: 50.37%
Over/Under: 6.5 -120o +110u (or about 6.7 runs)

Top 20 Most Likely Final Scores:
1. Dodgers 2-1
2. Dodgers 3-2
3. Giants 2-1
4. Giants 3-2
5. Dodgers 4-3
6. Dodgers 1-0
7. Giants 3-1
8. Giants 4-3
9. Dodgers 3-1
10. Giants 1-0
11. Giants 4-2
12. Giants 2-0
13. Dodgers 4-2
14. Dodgers 2-0
15. Dodgers 5-4
16. Giants 4-1
17. Giants 3-0
18. Dodgers 4-1
19. Dodgers 3-0
20. Giants 5-2
.
.
100. Dodgers 10-0

Giants vs Dodgers, Opening Day Simulation Results

Here are the simulation results for the opening day game between the Giants and the Dodgers.

Giants vs Dodgers
Matt Cain vs Clayton Kershaw

Simulation Results Notes:
Dodgers win expectancy is 56.17%
Giants on average score 2.87 runs
Dodgers on average score 3.22 runs
Total Runs scored on average: 6.09

Starting Pitching Lines:
Matt Cain - 6.4ip, 6.2so, 1.7bb, 6.3hits, 0.5hrs, 100pitches, 3.101fip
Clayton Kershaw - 6.6ip, 8.2so, 1.9bb, 5.5hits, 0.4hrs, 104pitches, 2.383fip

Giants player most likely to hit a HR: Buster Posey
Dodgers player most likely to hit a HR: Matt Kemp

Giants hitter most strikeouts: Brandon Belt (1.6 times)
Dodgers hitter most strikeouts: Matt Kemp (1.2 times)

Giants hitter most walks: Buster Posey (0.433 times)
Dodgers hitter most walks: AJ Ellis (0.447 times)

Some numbers from the Vegas odds:
Dodgers win expectancy: 59.76%
Over/Under: 6 -115o +105u

Top 20 Most Likely Final Scores:
1. Dodgers 2-1
2. Dodgers 3-2
3. Dodgers 1-0
4. Giants 2-1
5. Giants 3-2
6. Dodgers 4-3
7. Dodgers 3-1
8. Dodgers 2-0
9. Giants 1-0
10. Giants 3-1
11. Dodgers 3-0
12. Giants 4-3
13. Dodgers 4-2
14. Giants 2-0
15. Dodgers 4-1
16. Giants 4-2
17. Dodgers 5-4
18. Dodgers 4-0
19. Giants 4-1
20. Giants 3-0
.
.
100. Dodgers 9-8

MLB Simulation Results - March 31st

Away	Home	Favorite	Vegas ML	Vegas Win Exp	Vegas Total Runs	Sim Win Exp	Sim Total Runs
Texas	Houston	Texas	-160 / +150	60.78%	8.5 o100, u-120	59.66%	7.75

Most Likely Final Scores

1 HOU 3-2 6:1		51 TEX 7-6 37:1
2 TEX 3-2 7:1		52 TEX 8-4 37:1
3 HOU 2-1 7:1		53 TEX 9-2 40:1
4 HOU 4-3 7:1		54 TEX 9-3 43:1
5 TEX 2-1 8:1		55 HOU 5-0 44:1
6 TEX 4-3 8:1		56 HOU 7-4 44:1
7 TEX 4-2 9:1		57 HOU 7-3 47:1
8 TEX 3-1 9:1		58 TEX 9-1 47:1
9 HOU 5-4 11:1		59 TEX 8-5 47:1
10 TEX 4-1 11:1		60 HOU 7-5 47:1
11 TEX 5-3 12:1		61 HOU 7-2 49:1
12 TEX 5-2 12:1		62 TEX 8-0 50:1
13 TEX 5-4 12:1		63 TEX 9-4 56:1
14 HOU 3-1 12:1		64 TEX 8-6 59:1
15 HOU 4-2 13:1		65 HOU 7-1 59:1
16 TEX 2-0 14:1		66 HOU 6-0 63:1
17 TEX 5-1 14:1		67 TEX 10-2 63:1
18 HOU 1-0 15:1		68 TEX 10-3 66:1
19 TEX 1-0 15:1		69 HOU 8-7 66:1
20 TEX 3-0 16:1		70 TEX 10-1 68:1
21 TEX 6-3 16:1		71 TEX 9-0 68:1
22 TEX 6-2 16:1		72 TEX 9-5 74:1
23 HOU 5-3 16:1		73 HOU 8-2 76:1
24 HOU 4-1 17:1		74 TEX 8-7 76:1
25 TEX 6-4 18:1		75 HOU 8-3 77:1
26 TEX 6-1 18:1		76 HOU 8-4 79:1
27 HOU 6-5 18:1		77 HOU 8-5 82:1
28 TEX 4-0 18:1		78 TEX 10-4 85:1
29 HOU 5-2 19:1		79 TEX 11-2 89:1
30 HOU 2-0 19:1		80 TEX 9-6 94:1
31 TEX 6-5 20:1		81 HOU 8-6 94:1
32 TEX 5-0 22:1		82 HOU 8-1 97:1
33 TEX 7-2 22:1		83 HOU 7-0 98:1
34 HOU 3-0 22:1		84 TEX 10-0 99:1
35 TEX 7-3 23:1		85 TEX 10-5 100:1
36 TEX 7-1 24:1		86 TEX 11-1 104:1
37 TEX 7-4 25:1		87 TEX 11-3 106:1
38 HOU 5-1 26:1		88 TEX 11-4 120:1
39 HOU 6-4 26:1		89 HOU 9-3 120:1
40 TEX 6-0 26:1		90 HOU 9-2 124:1
41 HOU 6-3 27:1		91 TEX 9-7 128:1
42 TEX 8-2 30:1		92 HOU 9-4 132:1
43 HOU 6-2 31:1		93 TEX 11-0 135:1
44 TEX 7-5 31:1		94 TEX 12-2 135:1
45 HOU 4-0 31:1		95 TEX 10-6 144:1
46 TEX 8-3 33:1		96 TEX 12-3 145:1
47 TEX 8-1 33:1		97 HOU 9-8 145:1
48 HOU 7-6 34:1		98 HOU 8-0 152:1
49 TEX 7-0 35:1		99 HOU 9-1 156:1
50 HOU 6-1 37:1		100 TEX 12-1 159:1

Dodgers Best Lineup

With Hanley Ramirez starting the season on the DL, the Dodgers will have to make some changes to their starting lineup and with these changes comes the question of what their most productive lineup is.  I used my simulator to figure out just that.  I used 2013 Bill James projections as input and for this exercise I had the simulator come up with the most productive lineup against RHP.  I assumed that the Dodgers would start Nick Punto at 3B against RHP and I am not about to project Puig as a starter yet.  Here is the best lineup that it found.

1. Carl Crawford  (L)
2. Matt Kemp
3. Adrian Gonzalez  (L)
4. Andre Ethier  (L)
5. Luiz Cruz
6. Mark Ellis
7. Nick Punto  (S)
8. A.J. Ellis
9. Pitchers Spot

I was surprised to see that batting the pitcher 8th was no longer the best option.  I suppose having a weak hitter like Nick Punto in the lineup put a damper on that strategy.

Exportable 2013 MLB Schedule

Something that is difficult to find on the internet is an excel download file of the 2013 MLB Schedule.  I have taken the time to type up the complete 2013 MLB Schedule in to CSV format 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,Away,Home

1,2013,3,31,TEX,HOU
2,2013,4,1,FLA,WAS
3,2013,4,1,SD,NYN
4,2013,4,1,CHN,PIT
5,2013,4,1,COL,MIL
6,2013,4,1,SF,LAN
7,2013,4,1,PHI,ATL
8,2013,4,1,STL,ARI
9,2013,4,1,DET,MIN
10,2013,4,1,BOS,NYA
.
.
.
2428,2013,9,29,OAK,SEA
2429,2013,9,29,COL,LAN
2430,2013,9,29,WAS,ARI

2013 MLB Win Totals - Part I

Instead of just posting the current Vegas MLB odds to win the World Series, I am going to take things a step further and interpolate those W.S. odds out to 2013 win totals. There is no great scientific method that I am going to be using except for keeping consistent with the Vegas odds in the order I assign team wins.  I think I have a pretty good idea at what Vegas typically sets their highest and lowest over/under win totals at, so everything in between I can do an "eye ball" interpolation on to set the win total over/under for each team. Later in my Part II of this series, I will use a more scientific method for matching up the Vegas odds to win totals. But for now... here we go!

Team	Odds To Win W.S.	% Chance to Win	2013 Wins Over/Under
Angels	8-1	8.39	93
Dodgers	8-1	8.39	93
Blue Jays	8-1	8.39	93
Nationals	8-1	8.39	93
Tigers	9-1	7.55	92
Braves	12-1	5.81	90
Yankees	12.5-1	5.59	89
Reds	13-1	5.40	88
Rangers	13.5-1	5.21	88
Phillies	17-1	4.20	86
Giants	17-1	4.20	86
Red Sox	20-1	3.60	85
Cardinals	20-1	3.60	85
Rays	22-1	3.28	84
Athletics	30-1	2.44	82
Orioles	40-1	1.84	81
Diamondbacks	45-1	1.64	80
White Sox	50-1	1.48	79
Royals	50-1	1.48	79
Brewers	50-1	1.48	79
Pirates	72-1	1.03	77
Mets	75-1	0.99	75
Padres	75-1	0.99	75
Indians	77.5-1	0.96	75
Cubs	100-1	0.75	71
Rockies	100-1	0.75	71
Mariners	110-1	0.68	70
Twins	125-1	0.60	67
Marlins	150-1	0.50	63
Astros	200-1	0.38	61

Notes: 
Percent chance of winning W.S. has the Vegas juice removed.
American League has a 51.5% (Vegas) chance of winning the W.S.
National League has a 48.5% (Vegas) chance of winning the W.S.
Odds were taken from 5Dimes on 2/12/13.
In calculating the pecent chance to win the W.S., I removed the Vegas "juice".
The 2013 Over/Unders from the Atlantis came out the day after this post.

For the most part, I did a straight interpolation from (Vegas) World Series odds to an over/under on team wins.  There a couple of things that could be wrong with this.  The first comes from the possibility that the Vegas World Series odds have some bias to them.  Perhaps Vegas moved the odds up or down based on how popular a team is.  This of course has nothing to do with my methodology.  On the otherhand, something that does have to do with my methodology is that I am not controlling for the teams playing in divisions.  Divisions that may be stronger or weaker than others.  For example, the Royals might win a World Series out of every 50 times playing out of the AL Central, but what if they played out of the AL East?  Chances are that 50 number would climb.  A next step to this exercise would be to control for how hard or easy it is to win a division.  I'm not sure on the best way to do this yet, so if you have any ideas feel free to leave a comment or send me a tweet (@DodgerSims).

2013 World Series Odds

As of 1/30/2013...

Team	12/15/12 Odds	12/29/12 Odds	1/30/13
Angels	7-1	7-1	7-1
Tigers	7-1	7-1	7-1
Dodgers	7-1	7-1	7-1
Nationals	8-1	8-1	8-1
Blue Jays	10-1	10-1	10-1
Yankees	10-1	12-1	14-1
Reds	10-1	12-1	12-1
Rangers	10-1	14-1	18-1
Cardinals	14-1	14-1	14-1
Giants	14-1	14-1	14-1
Phillies	16-1	14-1	14-1
Braves	16-1	16-1	14-1
Rays	20-1	16-1	16-1
Brewers	30-1	30-1	60-1
Red Sox	30-1	30-1	30-1
Athletics	40-1	30-1	30-1
Royals	30-1	40-1	40-1
White Sox	40-1	40-1	40-1
Diamdonbacks	40-1	50-1	50-1
Orioles	50-1	50-1	50-1
Pirates	50-1	50-1	50-1
Cubs	60-1	50-1	50-1
Mariners	100-1	80-1	80-1
Indians	100-1	80-1	80-1
Mets	60-1	100-1	100-1
Padres	100-1	100-1	100-1
Marlins	100-1	100-1	100-1
Twins	100-1	100-1	100-1
Astros	100-1	100-1	100-1
Rockies	100-1	100-1	100-1

Source: Sportsbook.com

The Better Offense - Rockies or Dodgers

So there is this Buster Olney guy who gets paid money to write about baseball.  Recently, he put up an article where he ranked the best offenses in baseball.  More than a few Dodgers fans were up in arms when Buster ranked the Rockies offense a few spots ahead of the Dodgers offense.  I was asked by a couple followers on twitter to run the numbers through my simulator where I can take into effect things like the enormously friendly park that the Rockies hit in.

This is a pretty easy exercise for me to run in my simulator.  What I did is get park neutral offensive stats for both the Rockies and Dodgers lineups.  I took the 8 starting hitters for each teams, as listed at MLBdepthCharts website and gave each team what the simulator thought was its most efficient lineup.  I then gave each team the exact same starting pitcher, bullpen, bench and made all park factors league average.  I then ran five million individual game simulations, pitting the two teams together.  I let each team be the away/home team and repeated the above process for both a right handed and left handed starting pitcher.

So the only difference is the two hitting lineups, everything else is held constant.  The metric to determine the better offense can now be measured in how many of the five million games each team won.  Here are the results...