Sunday, July 13, 2014

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.

DateAwayHomeAway PitcherHome PitcherFaveML FaveML DogTanaka Win%
4/4/2014NYATORMasahiro TanakaDerek McGowanNYA-12612155.26
4/9/2014BALNYAMiguel GonzalezMasahiro TanakaNYA-17516863.17
4/16/2014CHNNYAJason HammelMasahiro TanakaNYA-20018565.81
4/22/2014NYABOSMasahiro TanakaJon LesterBOS-11610647.39
4/27/2014LAANYAGarrett RichardsMasahiro TanakaNYA-15915461.01
5/3/2014TBNYAJake OdorizziMasahiro TanakaNYA-19518765.64
5/9/2014NYAMILMasahiro TanakaYovani GallardoNYA-12912455.85
5/14/2014NYANYNMasahiro TanakaRaul MonteroNYA-16515961.83
5/20/2014NYACHNMasahiro TanakaJason HammelNYA-15815360.86
5/25/2014NYACHAMasahiro TanakaAndre RienzoNYA-16415861.69
5/31/2014MINNYAKevin CorreiaMasahiro TanakaNYA-24823870.85
6/5/2014OAKNYADrew PomeranzMasahiro TanakaNYA-13712756.90
6/11/2014NYASEAMasahiro TanakaChris YoungNYA-18517564.29
6/17/2014TORNYAMarcus StromanMasahiro TanakaNYA-16716062.05
6/22/2014BALNYAChris TillmanMasahiro TanakaNYA-20219266.33
6/28/2014BOSNYAJon LesterMasahiro TanakaNYA-15014559.60
7/3/2014NYAMINMasahiro TanakaPhil HughesNYA-15014559.60
7/8/2014NYACLEMasahiro TanakaTrevor BauerNYA-14714259.10
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Friday, July 04, 2014

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

DateAwayHomeAway SPHome SPVegas FaveML FaveML DogVegas Win Exp
7/4/2014LANCOLClayton KershawJair JurrjensLAN-19818465.6%
5/2/2014SEAHOUFelix HernandezBrad PeacockSEA-18517564.3%
6/11/2014NYASEAMasahiro TanakaChris YoungNYA-18517564.3%
4/15/2014WASMIAStephen StrasburgToby KoehlerWAS-17817163.6%
5/23/2014LANPHIClayton KershawRoberto HernandezLAN-17216562.8%
4/25/2014OAKHOUJesse ChavezBrad PeacockOAK-17116162.4%
3/31/2014WASNYNStephen StrasburgDillon GeeWAS-17215862.3%
5/3/2014STLCHNMichael WachaJake ArrietaSTL-16716062.0%
4/22/2014STLNYNAdam WainwrightDillon GeeSTL-16815862.0%
4/30/2014DETCHAMax ScherzerHector NoesiDET-16815862.0%
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Thursday, July 03, 2014

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!

TeamActual PFVegas PF2014 Delta
Nationals0.9900.9880.0026
Reds1.0391.0320.0064
Padres0.8820.8730.0093
Blue Jays1.0881.0740.0136
Marlins1.0321.0180.0140
Angels0.9630.9770.0146
White Sox1.0100.9900.0206
Diamdonbacks1.0861.0640.0214
Giants0.8930.9150.0221
Indians1.0220.9870.0351
Braves0.9830.9440.0391
Tigers1.0731.0310.0429
Royals0.9521.0040.0519
Rangers1.0351.1020.0671
Athletics0.9970.9270.0699
Red Sox0.9801.0550.0753
Dodgers0.9900.9110.0783
Mariners0.8530.9360.0831
Astros1.1301.0440.0855
Cubs0.9491.0380.0888
Yankees0.9641.0560.0920
Mets0.8120.9130.1011
Twins1.1151.0100.1056
Rays1.0320.9230.1092
Pirates1.0720.9580.1139
Brewers0.8741.0060.1317
Rockies1.4371.2830.1535
Phillies0.8220.9760.1539
Orioles0.8201.0330.2137
Cardinals1.2280.9640.2642
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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/UnderCountAverage RPG
5.5111.00
697.33
6.5936.59
72457.59
7.53058.09
82048.45
8.52088.53
91268.77
9.5448.98
101811.50
10.51811.33
11113.00
11.5212.00

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.