Saturday, June 29, 2013

Phillies vs Dodgers - Top 200 Most Likely Final Scores


Note: Out of one million games simulated.

RankPHILANCount RankPHILANCount
11257816 101311817
22351597 102112784
30140798 103510722
43436095 104105718
52135952 10579707
63232823 10697701
71331649 107112693
80230175 108111668
93125278 109113644
100324892 110411625
111024598 111212618
122424485 11298603
131423463 113106558
144322525 114012551
154520932 115610520
164220851 116114504
172020407 117312496
180418529 118110472
194117523 119107435
202516927 120511415
211516446 121115404
223016308 122122404
233514817 123123370
245213601 124910368
250513536 125113364
265313198 126121364
275412780 127412354
285112088 128710354
294011871 129013322
301611136 130213319
315610883 131116297
322610719 132108293
33369783 133611292
34069120 134313286
35628791 135124276
36638454 136810271
37508403 137120260
38617931 138512255
39467763 139125234
40647347 140132229
41177247 141131225
42276923 142109224
43656646 143117218
44076236 144133214
45375767 145114205
46725579 146413205
47605566 147214192
48715240 148711187
49735181 149014174
50675025 150118174
51474801 151134174
52184723 152811169
53284468 153130155
54744368 154126153
55083958 155513149
56573718 156314147
57703678 157612132
58753629 1581011127
59823624 159135124
60383442 160712123
61813287 161119121
62833225 162141115
63763214 163143112
64193032 164115109
65292790 165911107
66482744 166142107
67842695 167215106
68092588 168414106
69802352 169015102
70392281 170127102
71922201 171144102
72782151 172111093
73582144 17361386
74852065 17431585
75912001 17514584
76931967 17651482
772101836 17713681
781101781 17814080
79861747 17981279
80491662 18041572
81681658 18112872
82941582 18211669
830101558 18313769
843101469 18412966
85901452 18561464
86871427 18614664
871021265 18715364
88951222 18871363
89591158 18915158
901011129 19015256
911111118 19191252
921031080 19231651
93961006 19351551
94211992 19421748
95410981 19501647
9689960 196111245
97104919 19713845
98011918 19821644
9969904 199121044
100100837 20015042

Phillies vs Dodgers Simulation Results - June 29th


Wednesday, June 26, 2013

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.

PitcherPlayerInnOutsBaseHOU RunsDET RunsPlayFG WESIM WEFG WPASIM WPA
NANA-00005078.2300
M.ScherzerR.GrossmanT10000BB46.575.930.0350.023
M.ScherzerJ.ElmoreT10100KS49.878.13-0.033-0.022
M.ScherzerR.GrossmanT11100CS53.880.48-0.04-0.0235
M.ScherzerC.PenaT12000F-854.881.26-0.01-0.0078
D.KeuchelO.InfanteB10000KS52.679.72-0.022-0.0154
D.KeuchelT.HunterB11000F-95178.29-0.016-0.0143
D.KeuchelM.CabreraB12000F-95076.97-0.01-0.0132
M.ScherzerC.CarterT20000F-352.478.11-0.024-0.0114
M.ScherzerJ.MartinezT21000F-95478.93-0.016-0.0082
M.ScherzerC.CorporanT21000HR43.168.210.1090.1072
M.ScherzerJ.ParedesT22010KS44.168.68-0.01-0.0047
D.KeuchelP.FielderB200104-341.666.49-0.025-0.0219
D.KeuchelV.MartinezB210106-339.864.81-0.018-0.0168
D.KeuchelJ.PeraltaB22010BB41.266.040.0140.0123
D.KeuchelM.TuiasosopoB221101B44.169.10.0290.0306
D.KeuchelB.PenaB225111B54.579.420.1040.1032
D.KeuchelA.GarciaB22314HR80.395.610.2580.1619
D.KeuchelO.InfanteB220146-379.795.43-0.006-0.0018
M.ScherzerM.DominguezT30014KS81.995.99-0.022-0.0056
M.ScherzerM.GonzalezT310144-383.396.31-0.014-0.0032
M.ScherzerR.GrossmanT32014KC84.296.51-0.009-0.002
D.KeuchelT.HunterB30014F-98396.16-0.012-0.0035
D.KeuchelM.CabreraB310141B84.396.360.0130.002
D.KeuchelP.FielderB311144-6-381.795.48-0.026-0.0088
M.ScherzerJ.ElmoreT400142B7693.780.0570.017
M.ScherzerJ.ElmoreT40214Balk73.693.280.0240.005
M.ScherzerC.PenaT404241B68.889.810.0480.0347
M.ScherzerC.CarterT40124BB61.886.080.070.0373
M.ScherzerJ.MartinezT40354HR37.559.190.2430.2689
M.ScherzerC.CorporanT40054F-739.860.54-0.023-0.0135
M.ScherzerJ.ParedesT410542B35.657.170.0420.0337
M.ScherzerJ.ParedesT41254CS41.561.46-0.059-0.0429
M.ScherzerM.DominguezT42054F-542.662.2-0.011-0.0074
D.KeuchelV.MartinezB40054KS39.659.57-0.03-0.0263
D.KeuchelJ.PeraltaB41054BB42.962.060.0330.0249
D.KeuchelM.TuiasosopoB411541B47.866.370.0490.0431
D.KeuchelB.PenaB413545-4-33655.82-0.118-0.1055
M.ScherzerM.GonzalezT500543-138.457.18-0.024-0.0136
M.ScherzerR.GrossmanT51054F-940.158.28-0.017-0.011
M.ScherzerJ.ElmoreT52054KS41.358.94-0.012-0.0066
D.KeuchelA.GarciaB50054KS37.955.28-0.034-0.0366
D.KeuchelO.InfanteB51054F-835.552.14-0.024-0.0314
D.KeuchelT.HunterB52054F-833.850-0.017-0.0214
M.ScherzerC.PenaT60054KC36.351.51-0.025-0.0151
M.ScherzerC.CarterT61054KS38.152.72-0.018-0.0121
M.ScherzerJ.MartinezT620546-339.453.43-0.013-0.0071
D.KeuchelM.CabreraB60054F-835.452.57-0.04-0.0086
D.KeuchelP.FielderB610544-332.556.18-0.0290.0361
D.KeuchelV.MartinezB62054E-634.753.270.022-0.0291
D.KeuchelJ.PeraltaB621541B38.252.010.035-0.0126
D.KeuchelM.TuiasosopoB623551B56.475.10.1820.2309
T.BlackleyB.PenaB62355F-45069.53-0.064-0.0557
M.ScherzerC.CorporanT70055BB44.165.450.0590.0408
M.ScherzerJ.ParedesT70155FC49.769.18-0.056-0.0373
M.ScherzerM.DominguezT71155F-854.671.75-0.049-0.0257
M.ScherzerJ.ParedesT72155CS58.873.51-0.042-0.0176
T.BlackleyA.GarciaB70055F-554.972.2-0.039-0.0131
E.GonzalezO.InfanteB710551B5974.950.0410.0275
E.GonzalezO.InfanteB71155Pick Off52.169.34-0.069-0.0561
E.GonzalezT.HunterB72055BB54.270.480.0210.0114
E.GonzalezM.CabreraB72155KS5065.44-0.042-0.0504
D.SmylyM.GonzalezT80055KS54.767.57-0.047-0.0213
D.SmylyR.GrossmanT810551B49.865.640.0490.0193
D.SmylyJ.ElmoreT811551B43.159.670.0670.0597
D.SmylyC.PenaT81355KC51.765.57-0.086-0.059
A.AlburquerqueR.GrossmanT82355WP48.864.30.0290.0127
A.AlburquerqueC.CarterT82655KS60.773.25-0.119-0.0895
W.WrightP.FielderB80055KS56.168.95-0.046-0.043
W.WrightV.MartinezB81055F-852.665.31-0.035-0.0364
H.AmbrizJ.PeraltaB82055KS5063.43-0.026-0.0188
A.AlburquerqueJ.MartinezT90055BB41.861.480.0820.0195
P.CokeC.CorporanT901652B11.613.430.3020.4805
P.CokeJ.ParedesT90265SAC 5-311.913.29-0.0030.0014
P.CokeM.DominguezT91475SAC F-98.89.160.0310.0413
P.CokeM.GonzalezT920751-39.211.67-0.004-0.0251
J.VerasD.KellyB900753-14.55.75-0.047-0.0592
J.VerasB.PenaB91075BB10.713.430.0620.0768
J.VerasA.DirksB91175F-64.55.73-0.062-0.077
J.VerasB.PenaB92175DI4.86.020.0030.0029
J.VerasO.InfanteB92275BB9.111.870.0430.0585
J.VerasT.HunterB92375HBP17.527.730.0840.1586
J.VerasM.CabreraB92775F-800-0.175-0.2773

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.

Wednesday, June 19, 2013

MLB World Series Odds - June 19th


Team12/15/12 Odds12/29/12 Odds5/22/20136/20/2013
Tigers7776
Cardinals1414116.5
Braves161697.5
Athletics40302510
Rangers10147.2511
Reds101213.511
Red Sox30301412
Nationals889.516
Orioles50502516
Giants141413.518
Yankees101213.520
Diamdonbacks40503520
Pirates50504020
Blue Jays10103522
Rays20162525
Dodgers772733
Indians60803033
Angels773333
Royals30404033
Phillies16146640
Rockies1001006640
Padres10010015040
White Sox4040175150
Twins100100200150
Mariners6080175200
Mets60100200250
Brewers3030125500
Cubs6050300500
Astros10010025002500
Marlins10010025005000

Thursday, June 13, 2013

Bullpen Win Probability Added Leaders by Team


TeamWPA
Rangers5.62
Pirates5.53
Yankees3.75
Rockies3.71
Athletics3.58
Twins3.50
Blue Jays3.16
Diamondbacks3.06
White Sox2.92
Braves2.51
Padres1.89
Red Sox1.87
Brewers1.41
Angels1.33
Nationals0.63
Cardinals0.39
Marlins0.27
Royals0.20
Giants0.17
Reds0.09
Tigers0.00
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


Wednesday, June 05, 2013

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.