Tuesday, December 31, 2013

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...

Monday, December 30, 2013

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.

Sunday, December 29, 2013

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.

Friday, December 27, 2013

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.

Monday, December 23, 2013

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.

Wednesday, December 18, 2013

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 RHPGGNLGGALvs LHPGGNLGGAL
1G.ParraD.Pedroia1G.ParraD.Pedroia
2Y.MolinaA.Gordon2Y.MolinaS.Victorino
3P.GoldschmidtS.Victorino3P.GoldschmidtE.Hosmer
4C.GonzalezE.Hosmer4C.GonzalezA.Jones
5N.ArenadoA.Jones5N.ArenadoA.Gordon
6C.GomezS.Perez6A.SimmonsS.Perez
7A.SimmonsM.Machado7B.PhillipsM.Machado
8B.PhillipsJ.Hardy8C.GomezJ.Hardy
9PitcherPitcher9PitcherPitcher
(GGNL - Gold Glove NL, GGAL - Gold Glove AL)

And here are the results

This table has all the players set to their defensive values.
DescriptionAwayHomeWinnerAway RSHome RSWin %Total Runs
vs RHPGGNLGGALGGAL3.433.3250.426.75
vs RHPGGALGGNLGGNL3.143.6157.726.75
vs LHPGGNLGGALGGAL3.483.3650.376.84
vs LHPGGALGGNLGGNL3.163.6758.136.83

... and this table has all the players set to league average defensive values.
DescriptionAwayHomeWinnerAway RSHome RSWin %Total Runs
vs RHPGGNLGGALGGAL3.773.8052.197.57
vs RHPGGALGGNLGGNL3.603.9756.337.57
vs LHPGGNLGGALGGAL3.823.8452.107.66
vs LHPGGALGGNLGGNL3.634.0456.697.67

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.

NLAL
CY.Molina (15)S.Perez (13)
1BP.Goldschmidt (4)E.Hosmer (3)
2BB.Phillips (8)D.Pedroia (10)
3BN.Arenado (12)M.Machado (15)
SSA.Simmons (15)J.Hardy (10)
LFC.Gonzalez (10)A.Gordon (7)
CFC.Gomez (13)A.Jones (-3)
RFG.Parra (12)S.Victorino (15)

Tuesday, December 17, 2013

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.

BABIPAway RunsWin %
0.0001.388116.29%
0.1001.951924.59%
0.2002.911137.45%
0.3004.395954.38%
0.4006.547872.20%
0.5009.517886.66%
0.60013.457895.28%
0.70018.529598.8766%
0.80024.606599.8373%
0.90031.356899.9871%
1.00038.876999.9997%

Friday, November 22, 2013

2013 Vegas Park Factors


There are more than a few ways of calculating park factors.  The simplest system of all and the one that tells the best story of which park played as a hitter or pitchers park based on the empirical data (actual results) is the one where you simply divide runs per game at home scored by both teams by runs per game on the road scored by both teams.  ESPN does a great job of providing this data for previous seasons.

One problem with these year to year park factors (for runs scored) is that there is a ton of noise (variance) from year to year.  It is just very difficult to pin down what the true park factor should be for each park.  Some people like to take the previous two or three seasons and weight the more recent seasons heavier to come up with a number.  This is actually a safe way of doing it and one I usually prefer.

When it comes to betting on baseball run totals (over/unders) one needs a really good idea on what a stadiums' true park factor is.  From this base park factor number you can adjust up or down based off of weather or wind conditions if you like, but you need a good park factor number for each stadium first.  The Vegas sportsbooks obviously have their own numbers and if they don't you can easily reverse engineer the numbers that they used over the course of the season for each park.  All you need to do is take all of their run total numbers and adjust for juice to come up with an over/under number for each game.  Let's say you calculate that number as 7.25 runs scored.  You do this for all games and use this 7.25 (calculated number) as a substitute for the actual number of runs that were scored in that game and calculate each teams' park factor based off of this calculated number instead of the actual total number of runs scored.  In doing so, you can get a glimpse into what Vegas used as park factors for each team and then compare their park factors with the actual empirical number.  I calculate these Vegas park factors as the season progresses as kind of a sanity check against the park factors that I use in my day to day baseball game simulations.

Below is a look at each teams' Vegas park factor and Actual 2013 park factor and the difference between the two sorted by parks that Vegas had the run environment too low on.  Just because Vegas was off on a park factor may or may not mean they were dumb on selecting their park factor for that team as like I said above there is quite a bit of noise involved here.  But it would've obviously made for some good betting opportunities.

TeamVegas PFActual 2013 PF2013 Delta
Tigers1.0221.1390.1167
Cubs1.0831.1920.1091
Phillies1.0231.1070.0838
Blue Jays1.0441.1180.0743
Mariners0.9180.9910.0733
Marlins0.9591.0300.0715
Royals1.0161.0820.0661
Astros1.0091.0740.0652
Brewers1.0461.1100.0642
Yankees1.0281.0870.0590
Twins0.9751.0200.0446
Nationals0.9691.0130.0444
Rockies1.2391.2730.0343
Orioles1.0381.0570.0187
Angels0.9640.9680.0036
Braves0.9550.9560.0008
White Sox1.0030.998-0.0050
Rays0.9410.931-0.0103
Giants0.8890.869-0.0204
Dodgers0.8960.868-0.0279
Athletics0.9190.889-0.0295
Reds1.0320.989-0.0425
Indians0.9770.933-0.0442
Padres0.8770.831-0.0461
Pirates0.9610.907-0.0535
Mets0.9410.867-0.0736
Cardinals0.9790.892-0.0868
Red Sox1.0830.960-0.1227
Diamondbacks1.0980.974-0.1237
Rangers1.1210.985-0.1357

As a further exercise I computed the RMSE for the Vegas 2013 park factors against the actual park factors for the 2013, 2012 and 2011 seasons for the fun of it.

The RMSE (sum of the squares of the 32 park factor errors)... were.....
2013 = 0.1429
2012 = 0.4396
2011 = 0.2423
(these numbers are pre-square root)

You would expect to see the 2013 number be the lowest as that is what Vegas was predicting against.  The 2012 park factors had a lot of noise as there were a few crazy outliers bringing the error total up.  The 2011 park factors did pretty well, but about where you would expect it.


Wednesday, November 20, 2013

How Important Is Roster Flexibility


Let me make a simplified hypothetical situation to make this as easy as possible.  Let's say you have the choice between being the GM of one of these two teams.  Everything about these two teams is equal, except you know that Team A has a 6 WAR player and a 0 WAR player and Team B has a pair of 3 WAR players.  This is all we know about these two teams, assume everything else is equal (contracts, payroll etc...).  Which of these two teams would you rather have and why?

Team A:  6+0
Team B: 3+3

Would you value the flexibility that Team A has given that they have a 0 WAR player that should be pretty easy to replace via free agency or trade?  Assume each team was allowed to increase their payroll a little bit by the same amount.  Which team would be able to improve quicker?

So what would it be.

Team A because of roster flexibility and the ease to improve.
Team B because of ???
Niether because there is no difference.

Tuesday, October 29, 2013

Cardinals vs Red Sox - World Series Game 6 Simulation Results



                                 Top 100 Most Likely Final Scores
RankCardinalsRed SoxOccurrencesRankCardinalsRed SoxOccurrences
1234699951065999
2124294652755618
3343860453575295
4323163254825002
5212938655834855
6132604456704782
7452558857764773
8432550958814687
9312455359284549
10012450560384438
11242428461844241
12422346462184058
13411955363074032
14021897164483684
15141884465853471
16201712466783422
17521699267923249
18251695368583121
19531689869803114
20541683470932932
21351669871292878
22101652572912862
23031538073392745
24301510674862736
25511480675082612
26561466876682582
27151327777192539
28401310678942513
29621192879492273
30041160880872170
31631160081952157
32361157482901906
332610701831021880
346110696841031799
356410389852101736
364610098861011706
3750996087591703
3865950888091692
3916921289961669
40058388903101632
41728029911101556
4273763992891538
43677575931041538
4460729994691491
45717074954101390
46276994961051237
4737678597971228
4874661998791145
49476430991001117
501762441005101078

World Series Remaining Game Odds


With a maximum of two games remaining in the 2013 World Series this is the last installment of the reverse engineered game odds.  The only unknown left is the odds for Game #7.  The Game #6 odds and the final series winner odds are both out and from those two knowns we can reverse engineer what the Game #7 odds are (or should be).

     Individual Game Odds
Game # Red SoxCardinals
Game 1100%0%
Game 20%100%
Game 30%100%
Game 4100%0%
Game 5100%0%
Game 653.16%46.84%
Game 755.75%44.25%
Series79.27%20.73%

And using the nifty spreadsheet calculator that one of my readers made for me, we can also see the chances that each team wins the series in X number of games.  There are only three possible outcomes left obviously and they are the Red Sox winning in six or seven games or the Cardinals winning in seven games.  Here is another table showing those odds.

ResultChance %Odds
79.27%
Red Sox in 40.0%NA
Red Sox in 50.0%NA
Red Sox in 653.16%0.88
Red Sox in 726.11%2.83
20.73%
Cardinals in 40.0%NA
Cardinals in 50.0%NA
Cardinals in 60.0%NA
Cardinals in 720.73%3.82

Monday, October 28, 2013

Red Sox vs Cardinals - World Series Game 5 Simulation Results




                                       Top 100 Most Likely Final Scores
RankRed SoxCardinalsOccurrencesRankRed SoxCardinalsOccurrences
11246055173565
22345165271558
33434435360552
42132145418502
53231805557495
60128735675485
71328205738453
84324225876424
93123625908418
102423596029396
111422556170377
120222396283375
134522336382374
141021426419367
154221176548351
160319866681348
172017936739343
184117716858316
192517486984308
200416727009304
211516597185277
223515587278276
235415337349256
243015127492254
255214817580249
2653147176110231
275112537786228
2826124978210225
295612167968225
300511888093216
314011678191211
3216115082010204
3336113283310198
346310038487198
35629588594193
36469188659165
37179128795164
386485788410162
39068548990159
406584490103149
41508289169144
422781492211143
436179293101141
443770494111137
45476369596135
46076069689128
477459797102127
48675959897123
497257899011118
5028571100311113

Sunday, October 27, 2013

Red Sox vs Cardinals - World Series Game 4 Simulation Results




                                       Top 100 Most Likely Final Scores
RankRed SoxCardinalsOccurrencesRankRed SoxCardinalsOccurrences
12341345171665
21236035276629
33433915357598
43228665460569
52126745507561
64325355628542
71323735783530
84523285882528
92422805938514
103121636018493
114221536184465
121419646285447
130119036348444
145417746481441
154117016570433
162516856619420
170216726708376
183516526878376
195316436958370
205216197039366
211015937129360
222015627286351
230315447393340
241515347480315
255614967592308
265113727694301
273013427768300
280412507891292
296312097949290
302611758087272
3136115081210251
3262111882110240
336411088309233
341610858495221
3565108385410214
3640105986310207
3705104487102206
384610238859205
396190989103200
40678179089196
41508079196196
42178039269182
432779393104179
443777994101172
457476195211158
460675996311157
47737599790157
48477139897149
497269899010148
5075667100105141

Friday, October 25, 2013

Red Sox vs Cardinals - World Series Game Three Simulation Results




                                       Top 100 Most Likely Final Scores
RankRed SoxCardinalsOccurrencesRankRed SoxCardinalsOccurrences
1234294851755988
2123938652475746
3343457953825340
4323443754835065
5213166955704965
6432755856764930
7312597257574921
8132525558814567
9422495459844314
10452328560284309
11242319961074220
12012184862383973
13412050463183933
14141989464853770
15101895365923396
16201840766483313
17521829567933118
18531803068803091
19021786969783048
20541779170862962
21251675071912951
22351621372582838
23301613173942740
24031591174082668
25511474075292637
26151416876392536
27561320777192466
28401271778872355
29041251679682297
30631247580952069
31621218981902041
32641105882492002
332610874831021900
343610684841031839
356110537851011815
36651010886961761
37509662872101657
38469613881041615
3916958989091598
4005930390591591
41728209913101532
42737940921101475
4374745893691314
44717135941051314
45607028951001238
4667686296971230
4727678897891202
48376475984101178
49176343991121149
500663121001131092

World Series Individual Game Odds Reverse Engineered


Here are the odds of each remaining World Series game based on the knowledge of the Vegas odds that each team wins the World Series and the Vegas odds for games 1,2 and 3.  The odds for games 1,2 and 3 give us a good approximation for the odds in games 5,6 and 7 and we can move the game four odds around in such a way that the chances for each team winning the series match the Vegas odds.

Here is a look at the table with the individual game odds.

Game #Red SoxCardinals
Game 1100.0%0.0%
Game 20.0%100.0%
Game 350.6%49.4%
Game 449.3%50.7%
Game 545.9%54.1%
Game 652.0%48.0%
Game 758.6%41.4%
Series52.4%47.6%

And here is a table with the chances of the series ending with one team winning in X number of games.

ResultChance %Odds
52.4%
Red Sox in 40.0%NA
Red Sox in 511.5%7.73
Red Sox in 619.0%4.28
Red Sox in 722.0%3.55
47.6%
Cardinals in 40.0%NA
Cardinals in 513.5%6.38
Cardinals in 618.5%4.40
Cardinals in 715.5%5.43

Notes
- HFA of 4% is assumed
- Data was computed with spreadsheet provided by one of my readers.

Thursday, October 24, 2013

World Series Individual Game Odds


Vegas has come out and given the Red Sox a 68.75% chance of winning the World Series.  So I am going to try to reverse engineer the odds of the remaining games as if we didn't know who the pitchers were, just where the games were being played (no advanced handicapping).  What we know is that the Red Sox were a 53.9% favorite in Game #1 and are a 53.4% favorite in Game #2.  Knowing the odds in Game #1 gives me a hint at the odds in Game #5 (assuming 4 man rotations and HFA of 4%), so I am going to subtract 8% from the Game #1 odds and give the Red Sox a 45.9% chance of winning Game #5.  I can do a similar thing with the Game #6 odds, as I can copy the Game #2 odds as that game will be played in the same park.  Now I just need to massage the numbers in games 3,4,5 and 7 in an attempt to make the Red Sox chances of winning come as close to 68.75% as possible.  The game 7 odds will be an 8% difference of the game 3 odds.

Using the nifty spreadsheet that my reader gave me in one of the previous similar exercises I did in the NLCS we come up with the following table of individual game odds needed to have the Red Sox be 68.75% favorites to win the World Series following the Game #1 results.  To take this to the next level you would break down the odds of games three and four by looking at the probably starting pitchers.  Where one of the two games would move up in odds, the other would need to move down.

Game #Red SoxCardinals
Game 1100.0%0.0%
Game 253.4%46.6%
Game 349.7%51.2%
Game 449.7%51.2%
Game 545.9%51.2%
Game 653.4%46.6%
Game 757.7%43.2%
Series68.7%31.3%

And below is the table that shows the percent chances and odds of each possible result in the World Series.

ResultChance %Odds
68.7%
Red Sox in 413.2%6.58
Red Sox in 517.5%4.70
Red Sox in 620.1%3.98
Red Sox in 717.9%4.59
31.3%
Cardinals in 40.0%NA
Cardinals in 56.4%14.58
Cardinals in 611.8%7.47
Cardinals in 713.1%6.62


Wednesday, October 23, 2013

Cardinals vs Red Sox - World Series Game #2 Simulation




                                     Top 100 Most Likely Final Scores
RankCardinalsRed SoxOccurrencesRankCardinalsRed SoxOccurrences
12345545106629
21241575275570
33438035357565
43230295483558
52128105576522
61326295681490
74525965782483
84324915870470
93123925938466
102423806018463
110123266184442
124222786228431
131418646307403
140218556448379
154118056578351
162517366685351
175417146793334
185316906858331
193516846980322
205216577092322
212015417139299
220315237291298
231015167386297
245614977429286
255114477568274
263014177608263
271513617719259
280411867895256
294011537949252
306211528087248
313611498194248
3263113682102213
3326112283103203
3464102984210202
3561102385101198
364610198690193
376598287310179
38169268809177
395092589104175
40728589059171
410585491110170
42738059269169
43677829389155
447175094410150
45277089596150
467468596105146
473768397100135
48476719897126
491765199211123
5060639100510120

Thursday, October 17, 2013

Dodgers vs Cardinals - NLCS Game 6 Simulation Results




                                        Top 100 Most Likely Final Scores
RankDodgersCardinalsOccurrencesRankDodgersCardinalsOccurrences
1125564451273958
2234739952173864
3214648753833677
4014089954673640
5323939255373525
6103700856753487
7313555857473011
8203424258912871
9343140159072784
10302974860842772
11412659761762716
12422619462922708
13432489363902676
14132472564282414
15022446765572296
16402278266182281
17241865467932213
18511843868852054
19521779769381993
204517141701011771
21031686071081766
22141656072481679
235016291731001617
24531530574941580
25541325875191513
26611239476781504
272511494771021499
28041137778291447
29621135279861425
30601102880581321
31351082981391283
321510001821031239
3363968983871181
3456820184951105
3571781785091085
3664761686681013
37057334871111009
387071348849925
3972711789110916
4026674590104906
4165639491112875
4236635092210870
4316619993110847
447359849496808
4546548695113770
468150639659711
4774468297310698
4806451198010648
4982450799105603
50804448100410568

The Odds Couple of Games


Vegas now lists the Dodgers chances of winning the NLCS as 23.2% and gives them a 55.7% chance of winning Game #6 in which Clayton Kershaw pitches. From those two pieces of information we can reverse engineer the Dodgers odds in Game #7. Below is a table showing this information. The math is getting a lot easier with only two possible games remaining.

GameLAD PitcherSTL PitcherLAD Win%
         6C.KershawM.Wacha55.70%
         7H.RyuA.Wainwright41.70%
NLCS Winner23.20%

Wednesday, October 16, 2013

Oddly Enough for The Dodgers


Not hope remains for the Dodgers in the NLCS. Vegas odds give the Dodgers a 13.6% chance of winning the NLCS, a feat that would involve winning the last three games of the series, with the last two games being played in St.Louis. The Dodgers are a generous 61.8% Vegas favorite in Game #5 and knowing that and the 13.6% number we can reverse engineer the probable odds of Game #6 and Game #7. Also we can easily calculate the odds that the series ends in a 5, 6 or 7 game victory for the Cardinals or a seven game victory for the Dodgers.

Reverse Engineered Game Odds
GameLAD StarterLAD Win%
5Z.Greinke61.8
6C.Kershaw56.0
7H.Ryu39.2

Series Result Odds
Result% Chance
Cardinals in 538.2%
Cardinals in 627.2%
Cardinals in 721.0%
Dodgers in 713.6%

Cardinals vs Dodgers - NLCS Game 5 Simulation Results



                                      Top 100 Most Likely Final Scores
RankCardinalsDodgersOccurrencesRankCardinalsDodgersOccurrences
1234771751605803
2124649152745517
3343673453285216
4213163454185031
5323155655754969
6012897456574517
7132848157384446
8242443358764316
9312407359824254
10432407360704063
11452330061833917
12022309462813825
13142265863083790
14422129264483514
15032081865843485
16101950166293222
17201787867193154
18251787168782988
19411734769852869
20151634870922760
21041634471392751
22351617972582712
23541520073802675
24531511974932551
25301488375912425
26521462576092381
27561296477862377
28511242478492100
29051204179942087
30261153380682072
314011337812101981
32161129982871977
333610786831101916
3462987184951814
3563976085901720
36469252863101691
37649013871021659
38618731881031611
3906864289591537
40658585901011487
4150804391961419
42277781920101393
4317755393691278
44376874941041272
45736445954101264
4672631996891244
47676308972111167
48475914981111150
4971587699971067
500758111003111039

Tuesday, October 15, 2013

A Case For Ricky Nolasco Game Four NLCS


In case you've been on the moon or busy watching soccer there has been some discussion in Dodgers-Land about whether or not to skip Ricky Nolasco in Game #4 of the NLCS or bring back Zack Greinke to pitch on short rest. I decided to take a back of the envelope look at which decision is the correct way to go. My exercise won't go into the nitty-gritty details which would add a little more precision to the numbers but it will provide a good framework and maybe some guidance into what the best decision is.

In general there is a 0.5 RA/9ip penalty for pitching on three days rest and there is probably a little penalty for pitching on too many days rest but let's leave that number unknown for now. What I did is made a table with the RA/9 expectancies for each of the Dodgers four starting pitchers. Then output tables showing which starting pitching arrangement looks best. The RA/9ip estimates can be changed if you don't agree with them, this is just the framework and with the framework you can tell how bad of a pitcher Nolasco must be to make skipping him and starting Greinke (and even perhaps Kershaw and Ryu) on short rest.

Input Table
PitcherRA/9IP
Kershaw2.25
Greinke3
Ryu3.5
Nolasco4

Now let's come up with some rotation arrangements. Let's first start off with the one where Nolasco pitches Game #4 and Greinke, Kershaw and Ryu all pitch on regular rest.

GameRestLAD StarterRA/9
4NormalNolasco4
5NormalGreinke3
6NormalKershaw2.25
7NormalRyu3.5
Total12.75

Now let's see what happens if Nolasco is skipped and Greinke (G4, G7), Kershaw (G5) and Ryu (G6) all pitch on short rest. Notice the 0.5 RA/9ip penalty applied.

GameRestLAD StarterRA/9
4ShortGreinke3.5
5ShortKershaw2.75
6ShortRyu4
7ShortGreinke3.5
Total13.75

This particular arrangement does not fair too well as you are applying four 0.5 RA/9ip penalties, adding up to 2.0 RA/9ip over four games. Nolasco's projection MUST be very very bad for this option to win out. How bad? I will visit that later.

Now onto the arrangement where Zack Greinke pitches Game #4 on short rest then Nolasco pitches Game #5 and Kershaw and Ryu pitch the last two games on full rest. I call this the "Rearranging The Deck Chairs" option.

GameRestLAD StarterRA/9
4ShortGreinke3.5
5NormalNolasco4
6NormalKershaw2.25
7NormalRyu3.5
Total13.25

This option always loses out to the first option as you are just swapping Game #4 and Game #5 starters and adding a penalty to Greinke's start. This option is stupid.

So it comes down to the first two options and the first option will win out unless you think that Nolasco is a terrible pitcher. Just how terrible in terms of RA/9ip? And you can feel free to combine Nolasco's RA/9ip with that of Volquez if you think they will tag team their start. Only good thing about that is that you can get an early pinch hitter at-bat in the game. But let's get back to the question of how bad would Nolasco's RA/9ip projection have to be to make the second option a better one than the third. The break even point for Nolasco's RA/9ip projection is 5.0. If you think his RA/9ip projection is worse than 5.0 then the second option would be better and you would go with a three man rotation. Of course there are some other minor things to take into consideration, so you could add or subtract those in to the RA/9ip projections but this exercise gives you an idea for which rotation arrangement is best.

I think the Dodgers should start Nolasco and if he isn't terribly sharp or being hit hard to pinch hit for him in either his first or second at-bat and then to use Volquez until he bats and then to let the bullpen finish out the game. Hopefully, you won't go extra innings as you will be using a lot of your bullets early on in the game.

Monday, October 14, 2013

Cardinals vs Dodgers - Simulation Results NLCS Game #4




                                           Top 100 Most Likely Final Scores
RankCardinalsDodgersOccurrencesRankCardinalsDodgersOccurrences
1234759351475743
2124584352705119
3343754653755073
4323288954824861
5213217555574685
6012699356814659
7132624257834645
8312577758284494
9432489759764422
10452371560074222
11242327061184010
12422314862843910
13022016663383894
14142003864483309
15101962865803294
16411942166853151
17201899567923124
18031656468783084
19301654669913048
20521620870932906
21251618571582835
22351618072082710
23531617073292671
24541605574862535
25511454375192514
26151402076392456
27561341377942441
28401338878682209
29041268179902171
30621132880872043
316310590811022034
32261057082492001
33361053783951925
346110135841011837
35509768851031737
36649653862101716
3716942887091704
38059345883101559
39469132891101506
4065888290591484
41727527911041482
4260723592961427
4371713393891369
44737039941001353
4527674995691222
46676682964101202
4706651297971172
48376264981121167
49176150991111128
507461371001051119

Updates NLCS Individual Game Odds


Here is an updated list of the individual game odds for the remaining games of the NLCS. As a reminder, the way the game odds are calculated is take the Vegas odds of the Cardinals winning the NLCS, which the last time I looked was (-350, +290) 76.19% chance of winning. We can give the Dodgers a 0% chance of winning for the games they already lost and we can input a win probability of 47.4% for Game #3 as those odds have already been published. Now to figure out the odds of the remaining games (Games 4-7) we need to adjust them until the string of games gives us a 23.81% chance of the Dodgers winning the series. To do this, I used the nifty excel spreadsheet that one of my readers gave me in the comments of the previous post on this topic. While these won't be the likely odds for Games four thru seven they are good "ballpark" estimates. And if you don't agree with any of these game odds, that is fine... but you will need to adjust the odds of some of the other games to even out any changes you made one way or the other.

GameAway StarterHome StarterDodgers Odds
1Zack GreinkeJoe Kelly0.0%
2Clayton KershawMichael Wacha0.0%
3Adam WainwrightHyun-Jin Ryu47.4%
4Shelby MillerRicky Nolasco59.0%
5Joe KellyZack Greinke65.0%
6Clayton KershawMichael Wacha57.0%
7Hyun-Jin RyuAdam Wainwright42.0%
Total23.8%

Saturday, October 12, 2013

Cardinals vs Dodgers - NLCS Game #3 Simulation Results




                                     Top 100 Most Likely Final Scores
RankCardinalsDodgersOccurrencesRankCardinalsDodgersOccurrences
11255885117409
22348375227402
32143495383386
40141085437375
53236165567366
63132975675365
71032645747356
83432235892321
92030415990320
101326546091319
114225916184304
123025216257286
130225116307285
144324646476261
154123796528253
164020256693251
172420156785248
180318306818227
194518236938220
2051179070101217
2114174671100192
2252168472102184
235315717394174
245014237408169
255414197548162
262512737686160
276112137778158
280412097829156
293511807919151
3015114980103137
3162114281111132
32609988287131
33639788358126
34569578495119
35648138539117
360579486104117
377178887112115
387276988110110
393673189113107
40167259049102
41266729168101
4270670920994
43656339310591
4473606949690
45466059531080
46815579621078
47065369712073
4874497989768
49824629910668
508045110012268

Friday, October 11, 2013

Dodgers vs Cardinals - NLCS Game #1 Simulation Results



Note: Vegas has the Dodgers as 55.6% favorites in Game #1 (ML -130, +120)

                                       Top 100 Most Likely Final Scores
RankDodgersCardinalsOccurrencesRankDodgersCardinalsOccurrences
12344025117569
21240595237566
33435315381555
43233765483551
52131925576536
64327495647510
73126955706476
84224855857459
94523845984432
101323526080395
110122636128386
122421096292379
134121076385378
141019416418363
155218376507358
163018216691351
172018196738342
185318166878324
195418016948323
201417457058310
215116807193293
220216297286292
230315277329291
244015167495276
253514577594268
262514457687246
275613227790231
286212347819229
291512297968221
306311978008220
3161118881102219
325011258249215
336410878339211
3404107684101203
3565104085103187
362693286210184
373692487104171
387386988310167
39728658959162
406083090112153
41468039109152
427179592110150
43167889396149
44057649489141
45677329597136
467472096100136
47276359798129
487558698105129
498258399211120
5070573100410116