Masahiro Tanaka At The All-Star Break

Here is a list of the Vegas odds of all 18 of Masahiro Tanaka's starts up to the All-Star break.

Date	Away	Home	Away Pitcher	Home Pitcher	Fave	ML Fave	ML Dog	Tanaka Win%
4/4/2014	NYA	TOR	Masahiro Tanaka	Derek McGowan	NYA	-126	121	55.26
4/9/2014	BAL	NYA	Miguel Gonzalez	Masahiro Tanaka	NYA	-175	168	63.17
4/16/2014	CHN	NYA	Jason Hammel	Masahiro Tanaka	NYA	-200	185	65.81
4/22/2014	NYA	BOS	Masahiro Tanaka	Jon Lester	BOS	-116	106	47.39
4/27/2014	LAA	NYA	Garrett Richards	Masahiro Tanaka	NYA	-159	154	61.01
5/3/2014	TB	NYA	Jake Odorizzi	Masahiro Tanaka	NYA	-195	187	65.64
5/9/2014	NYA	MIL	Masahiro Tanaka	Yovani Gallardo	NYA	-129	124	55.85
5/14/2014	NYA	NYN	Masahiro Tanaka	Raul Montero	NYA	-165	159	61.83
5/20/2014	NYA	CHN	Masahiro Tanaka	Jason Hammel	NYA	-158	153	60.86
5/25/2014	NYA	CHA	Masahiro Tanaka	Andre Rienzo	NYA	-164	158	61.69
5/31/2014	MIN	NYA	Kevin Correia	Masahiro Tanaka	NYA	-248	238	70.85
6/5/2014	OAK	NYA	Drew Pomeranz	Masahiro Tanaka	NYA	-137	127	56.90
6/11/2014	NYA	SEA	Masahiro Tanaka	Chris Young	NYA	-185	175	64.29
6/17/2014	TOR	NYA	Marcus Stroman	Masahiro Tanaka	NYA	-167	160	62.05
6/22/2014	BAL	NYA	Chris Tillman	Masahiro Tanaka	NYA	-202	192	66.33
6/28/2014	BOS	NYA	Jon Lester	Masahiro Tanaka	NYA	-150	145	59.60
7/3/2014	NYA	MIN	Masahiro Tanaka	Phil Hughes	NYA	-150	145	59.60
7/8/2014	NYA	CLE	Masahiro Tanaka	Trevor Bauer	NYA	-147	142	59.10

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Top 10 Biggest Road Favorites

In today's Dodgers vs Rockies game the Dodgers are a 65.6% favorite to win on the road. This is the largest road favorite of the year so far this season. Of course it is a game that a red-hot Clayton Kershaw is pitching in and the Rockies Jair Jurrjens isn't exactly the leagues best pitcher. This got me to thinking what the top ten list would look like for largest road favorites this year.  Kershaw and Strasburg appear twice on this list.

Here is the list

Date	Away	Home	Away SP	Home SP	Vegas Fave	ML Fave	ML Dog	Vegas Win Exp
7/4/2014	LAN	COL	Clayton Kershaw	Jair Jurrjens	LAN	-198	184	65.6%
5/2/2014	SEA	HOU	Felix Hernandez	Brad Peacock	SEA	-185	175	64.3%
6/11/2014	NYA	SEA	Masahiro Tanaka	Chris Young	NYA	-185	175	64.3%
4/15/2014	WAS	MIA	Stephen Strasburg	Toby Koehler	WAS	-178	171	63.6%
5/23/2014	LAN	PHI	Clayton Kershaw	Roberto Hernandez	LAN	-172	165	62.8%
4/25/2014	OAK	HOU	Jesse Chavez	Brad Peacock	OAK	-171	161	62.4%
3/31/2014	WAS	NYN	Stephen Strasburg	Dillon Gee	WAS	-172	158	62.3%
5/3/2014	STL	CHN	Michael Wacha	Jake Arrieta	STL	-167	160	62.0%
4/22/2014	STL	NYN	Adam Wainwright	Dillon Gee	STL	-168	158	62.0%
4/30/2014	DET	CHA	Max Scherzer	Hector Noesi	DET	-168	158	62.0%

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Park Factor Surprises

Nobody likes surprises, right? Unless it is your birthday and then maybe you do. But when it comes to park factors (runs) it is often difficult to nail down a teams park factor and randomness plays havoc with what smart people think the park factors should be. As you know, I keep track of the runs scored portion of a teams park factor along with a Vegas park factor that I reverse engineer from each teams over/under, where I replace the actual runs scored in each game with the Vegas over/under total. This gives me another aspect of the park factor. The aspect of the wisdom of the crowd of the people who are actually risking their hard earned money on knowing how many runs scored each game is likely to have. I love comparing things like over/unders, expected win totals and player projections to the people who risk their money on each game. What I have listed in the table below is each teams current 2014 park factor for runs scored along with their Vegas park factor. The table is sorted by the most similar park factors with the biggest surprises at the bottom of the table. Enjoy!

Team	Actual PF	Vegas PF	2014 Delta
Nationals	0.990	0.988	0.0026
Reds	1.039	1.032	0.0064
Padres	0.882	0.873	0.0093
Blue Jays	1.088	1.074	0.0136
Marlins	1.032	1.018	0.0140
Angels	0.963	0.977	0.0146
White Sox	1.010	0.990	0.0206
Diamdonbacks	1.086	1.064	0.0214
Giants	0.893	0.915	0.0221
Indians	1.022	0.987	0.0351
Braves	0.983	0.944	0.0391
Tigers	1.073	1.031	0.0429
Royals	0.952	1.004	0.0519
Rangers	1.035	1.102	0.0671
Athletics	0.997	0.927	0.0699
Red Sox	0.980	1.055	0.0753
Dodgers	0.990	0.911	0.0783
Mariners	0.853	0.936	0.0831
Astros	1.130	1.044	0.0855
Cubs	0.949	1.038	0.0888
Yankees	0.964	1.056	0.0920
Mets	0.812	0.913	0.1011
Twins	1.115	1.010	0.1056
Rays	1.032	0.923	0.1092
Pirates	1.072	0.958	0.1139
Brewers	0.874	1.006	0.1317
Rockies	1.437	1.283	0.1535
Phillies	0.822	0.976	0.1539
Orioles	0.820	1.033	0.2137
Cardinals	1.228	0.964	0.2642

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MLB Over/Unders And The Empirical Data

In my previous post I used my simulator to come up with a set of equations to convert an MLB Over/Under to an average runs scored per game number. Basically, a conversion tool to go from the median to mean for runs scored in a game. In this post I am going to show what the actual empirical data looks like based off of the 1266 games played so far. Obviously, the sample size here will be problematic. The next step will be to add data from previous seasons to the data that I have for the current 2014 season. I may or may not be able to do this but here is the 2014 data nonetheless. And keep in mind this data is not taking into account the odds or percentage chance of the game going over or under. It is assuming that all games have a 50/50 chance of going over or under, which is wrong but it should even out a little bit.

Over/Under	Count	Average RPG
5.5	1	11.00
6	9	7.33
6.5	93	6.59
7	245	7.59
7.5	305	8.09
8	204	8.45
8.5	208	8.53
9	126	8.77
9.5	44	8.98
10	18	11.50
10.5	18	11.33
11	1	13.00
11.5	2	12.00

As you can see the sample size problem makes this data pretty close to unusable. And that is part of what I am trying to show here. What I would expect to see in the "Average Runs Per Game" column of the table had the sample size been in the tens of thousands is a number about 0.45 higher than the over/under number. Our largest sample size is the over/under of 7.5 and the average runs scored per game is 0.59 higher than the over/under.