Sunday, January 30, 2011

Giants vs Dodgers, 2011 Pre-Season Simulations




The Giants might be the World Series Champions, but just how good are they? Are they good enough to be considered a strong NL West favorite? Are they better than the 2011 Dodgers on paper? Well, to shed some light on this I ran some head to head simulations between the 2011 Giants and Dodgers. Each pitching matchup was simulated 100K times with each team taking a shot at being both the away and home team. Below are the lineups, starting rotations used as well as the results (50).

Giants Starting Lineup Dodgers Starting Lineup
Giants vs LHP Giants vs RHP   Dodgers vs LHP Dodgers vs RHP
Order Pos Name Pos Name   Pos Name Pos Name
1 CF A.Torres CF A.Torres   SS R.Furcal SS R.Furcal
2 2B F.Sanchez 2B F.Sanchez   CF M.Kemp CF M.Kemp
3 1B A.Huff 1B A.Huff   RF A.Ethier RF A.Ethier
4 C B.Posey C B.Posey   3B C.Blake 1B J.Loney
5 RF C.Ross RF C.Ross   2B J.Uribe 3B C.Blake
6 LF P.Burrell LF P.Burrell   LF M.Thames 2B J.Uribe
7 SS M.Tejada SS M.Tejada   1B J.Loney LF J.Gibbons
8 3B P.Sandoval 3B P.Sandoval   C R.Barajas C R.Barajas


Giants Rotation Dodgers Rotation
#1 Tim Lincecum #1 Clayton Kershaw
#2 Matt Cain #2 Chad Billingsley
#3 Jonathan Sanchez #3 Hiroki Kuroda
#4 Madison Bumgarner #4 Ted Lilly
#5 Barry Zito #5 Jon Garland


Simulation Results
Away Starter Home Starter Favorite Win Prob
Tim Lincecum Clayton Kershaw Dodgers 53.97%
Clayton Kershaw Tim Lincecum Giants 57.43%
Tim Lincecum Chad Billingsley Dodgers 55.25%
Chad Billingsley Tim Lincecum Giants 56.85%
Tim Lincecum Hiroki Kuroda Dodgers 52.23%
Hiroki Kuroda Tim Lincecum Giants 58.72%
Tim Lincecum Ted Lilly Giants 55.42%
Ted Lilly Tim Lincecum Giants 65.50%
Tim Lincecum Jon Garland Giants 54.81%
Jon Garland Tim Lincecum Giants 65.43%
Matt Cain Clayton Kershaw Dodgers 57.01%
Clayton Kershaw Matt Cain Giants 54.36%
Matt Cain Chad Billingsley Dodgers 57.60%
Chad Billingsley Matt Cain Giants 53.51%
Matt Cain Hiroki Kuroda Dodgers 54.77%
Hiroki Kuroda Matt Cain Giants 55.41%
Matt Cain Ted Lilly Giants 52.71%
Ted Lilly Matt Cain Giants 62.31%
Matt Cain Jon Garland Giants 52.24%
Jon Garland Matt Cain Giants 62.55%
Jonathan Sanchez Clayton Kershaw Dodgers 57.95%
Clayton Kershaw Jonathan Sanchez Giants 53.27%
Jonathan Sanchez Chad Billingsley Dodgers 58.50%
Chad Billingsley Jonathan Sanchez Giants 54.19%
Jonathan Sanchez Hiroki Kuroda Dodgers 55.71%
Hiroki Kuroda Jonathan Sanchez Giants 55.34%
Jonathan Sanchez Ted Lilly Giants 51.84%
Ted Lilly Jonathan Sanchez Giants 62.07%
Jonathan Sanchez Jon Garland Giants 51.19%
Jon Garland Jonathan Sanchez Giants 62.25%
Madison Bumgarner Clayton Kershaw Dodgers 57.38%
Clayton Kershaw Madison Bumgarner Giants 53.95%
Madison Bumgarner Chad Billingsley Dodgers 58.24%
Chad Billingsley Madison Bumgarner Giants 52.94%
Madison Bumgarner Hiroki Kuroda Dodgers 55.42%
Hiroki Kuroda Madison Bumgarner Giants 54.80%
Madison Bumgarner Ted Lilly Giants 52.20%
Ted Lilly Madison Bumgarner Giants 62.11%
Madison Bumgarner Jon Garland Giants 52.16%
Jon Garland Madison Bumgarner Giants 62.05%
Barry Zito Clayton Kershaw Dodgers 59.68%
Clayton Kershaw Barry Zito Giants 51.33%
Barry Zito Chad Billingsley Dodgers 60.49%
Chad Billingsley Barry Zito Giants 50.35%
Barry Zito Hiroki Kuroda Dodgers 57.85%
Hiroki Kuroda Barry Zito Giants 52.69%
Barry Zito Ted Lilly Dodgers 50.75%
Ted Lilly Barry Zito Giants 59.48%
Barry Zito Jon Garland Dodgers 50.83%
Jon Garland Barry Zito Giants 59.79%


When you average the combined win probability of all fifty games, the Giants have an average win probability of 52.15%. Extrapolated out over a 162 game season the Giants would win 84.49 games and the Dodgers would win 77.51. Not a big difference but the simulator is showing that the Giants are a better team than the Dodgers, nothing that a little luck and favorable injuries couldn't overcome.

NBA Odds

Wednesday, January 26, 2011

Relegated To Baseball


Last March I proposed (for fun) my answer to the rumors of baseball realignment, a relegation system that placed all 30 MLB teams into three tiers. For a complete breakdown of my plan you can go here. What I'd like to show you are what the 2010 playoffs would've looked like under my plan and what the new 2011 divisions/tiers would look like.

2010 Playoffs
(T3#1 vs T3#2) vs T1#1
T1#4 vs T1#5
T2#1 vs T1#3
T2#2 vs T1#2

(Mets vs Padres) Winner vs Phillies
Giants vs Twins
Braves vs Yankees
Reds vs Rays

2011 Alignment
Tier 1 Tier 2 Tier 3
Phillies Rangers (y)Cubs
Rays Blue Jays (y)Mariners
Yankees (x)Padres Diamondbacks
Giants Brewers Indians
Twins White Sox Royals
Cardinals (y)Marlins Orioles
Rockies (y)Angels Nationals
Red Sox Athletics Pirates
Tigers (x)Mets  
(x)Braves Astros  
(x)Reds    
Dodgers    

x - denotes moved up a tier.
y - denotes moved down a tier.






MLB Odds

Thursday, January 20, 2011

Comparing The Talents Of National League Pitchers


Last week I looked at similarity scores between NL West starting pitchers using the stats of K/9, BB/9, GB/FB and Age. This week I am expanding my sample to include all projected National League starting pitchers. And this time I am going to narrow the stats down to the ones I feel are most reflective of a pitchers talent (K/9, BB/9, GB/FB). I tried to weight each of the three categories somewhat evenly, by using the number of standard deviations away from the mean (in each category) for each pitchers three inputs.

The way I am calculating the similarity scores is by using an X-Y-Z plot and calculating how close each pitcher is to each other in terms of distance using the three stats listed above. There is a simple formula to calculate this that we all learned in trigonometry. Dist = SQRT((X1-X2)^2 + (Y1-Y2)^2 + (Z1-Z2)^2)

The ultimate goal of each pitcher would be to score well in each of the three categories placing him in the top quadrant. A pitcher with a high K/9, low BB/9 and high GB/FB are positioned to have the most success. What really stands out before looking at any results are the numbers of Roy Halladay. His K/9 of 7.57 is above average, coming in at a modest 0.27 standard deviations above the mean. His BB/9 of 1.24 comes in at 2.65 (the best) standard deviations (to the good side) and lastly his GB/FB comes in at 1.89 standard deviations (above the mean). It is no wonder why Roy Halladay is the leagues best pitcher.

Roy Halladay (units SD)
K/9 BB/9 GB/FB
+0.27 +2.65 +1.89


Since this blog is primarily a Dodgers blog, let's take a look at the most similar NL pitchers for each of the Dodgers five starters.

Los Angeles Dodgers
Pitcher Most Similar K/9 BB/9 GB/FB Sim Score
C.Kershaw Y.Gallardo 9.65 vs 9.67 3.62 vs 3.43 1.06 vs 1.18 0.361
C.Billingsley R.Dempster 8.39 vs 8.14 3.34 vs 3.27 1.36 vs 1.41 0.223
H.Kuroda C.Carpenter 6.83 vs 6.39 2.17 vs 2.23 1.59 vs 1.81 0.539
T.Lilly S.Marcum 7.63 vs 7.65 2.23 vs 2.18 0.74 vs 0.94 0.404
J.Garland L.Hernandez 4.84 vs 4.91 2.83 vs 2.89 1.33 vs 1.15 0.371


A fair conclusion to come to from this, is to say that the Dodgers starting rotation of Kershaw, Billingsley, Kuroda, Lilly and Garland is equal to in terms of 2011 projected talent as a rotation of Gallardo, Dempster, Carpenter, Marcum and Hernandez. This leads to the very interesting question of, which of those two rotations would you rather want for the 2011 season, all other things (salary, age, etc...) being equal?

Here is a quick look at the rest of the National League starters with each pitchers closest comparable NL pitcher and their similarity score (lowest is closest).

NL Starting Pitchers - Similarity Scores
Pitcher Most Similar K/9 BB/9 GB/FB Sim Score
T.Lincecum Y.Gallardo 9.95 vs 9.67 3.01 vs 3.43 1.37 vs 1.18 0.733
B.Zito K.Correia 6.46 vs 6.60 3.72 vs 3.56 0.89 vs 1.14 0.555
M.Cain A.Harang 7.51 vs 7.38 2.66 vs 2.69 0.80 vs 0.93 0.277
J.Sanchez B.Norris 9.49 vs 8.95 4.33 vs 4.33 0.98 vs 1.04 0.392
M.Bumgarner J.Hammel 7.23 vs 7.25 2.43 vs 2.27 1.23 vs 1.33 0.302
U.Jimenez J.Chacin 8.59 vs 9.06 3.59 vs 3.83 1.64 vs 1.45 0.604
J.de la Rosa J.Chacin 8.64 vs 9.06 4.03 vs 3.83 1.31 vs 1.45 0.493
J.Hammel M.Bumgarner 7.25 vs 7.23 2.27 vs 2.43 1.33 vs 1.23 0.302
A.Cook T.Hudson 3.97 vs 5.63 2.78 vs 2.79 2.44 vs 2.60 1.191
J.Chacin J.de la Rosa 9.06 vs 8.64 3.83 vs 4.03 1.45 vs 1.31 0.493
I.Kennedy J.McDonald 7.97 vs 8.52 3.28 vs 3.48 0.84 vs 0.82 0.477
D.Hudson M.Cain 8.01 vs 7.51 2.69 vs 2.66 0.72 vs 0.80 0.383
J.Saunders R.Dickey 5.40 vs 5.42 2.85 vs 2.69 1.27 vs 1.39 0.330
B.Enright B.Arroyo 5.81 vs 5.38 2.25 vs 2.58 0.70 vs 1.01 0.830
Z.Duke R.Dickey 4.66 vs 5.42 2.46 vs 2.69 1.60 vs 1.39 0.747
M.Latos C.Hamels 9.18 vs 8.56 2.53 vs 2.32 1.03 vs 1.06 0.526
C.Richard J.Jurrjens 6.50 vs 6.54 3.25 vs 3.17 1.43 vs 1.26 0.357
T.Stauffer D.Moseley 6.05 vs 5.69 3.03 vs 3.06 1.31 vs 1.39 0.298
A.Harang J.Cueto 7.38 vs 7.16 2.69 vs 2.76 0.93 vs 1.02 0.255
D.Moseley T.Stauffer 5.69 vs 6.05 3.06 vs 3.03 1.39 vs 1.31 0.298
R.Halladay H.Kuroda 7.57 vs 6.83 1.24 vs 2.17 2.18 vs 1.59 1.786
C.Lee R.Nolasco 7.59 vs 8.82 1.30 vs 2.03 0.91 vs 0.95 1.254
R.Oswalt J.Hammel 7.54 vs 7.25 2.34 vs 2.27 1.48 vs 1.33 0.373
C.Hamels Z.Greinke 8.56 vs 8.55 2.32 vs 2.23 1.06 vs 1.01 0.162
J.Blanton M.Bumgarner 6.55 vs 7.23 2.50 vs 2.43 1.20 vs 1.23 0.484
T.Hudson J.Westbrook 5.63 vs 5.83 2.79 vs 2.97 2.60 vs 2.65 0.308
T.Hanson J.Vazquez 8.33 vs 7.68 2.67 vs 2.80 0.99 vs 0.96 0.490
D.Lowe J.Westbrook 6.26 vs 5.83 2.70 vs 2.97 3.08 vs 2.65 0.983
J.Jurrjens C.Richard 6.54 vs 6.50 3.17 vs 3.25 1.26 vs 1.43 0.357
M.Minor D.Hudson 8.74 vs 8.01 3.00 vs 2.69 0.73 vs 0.72 0.671
J.Johnson C.Hamels 8.96 vs 8.56 2.32 vs 2.32 1.45 vs 1.06 0.409
R.Nolasco C.Hamels 8.82 vs 8.56 2.03 vs 2.32 0.95 vs 1.06 0.361
J.Vazquez A.Harang 7.68 vs 7.38 2.80 vs 2.69 0.96 vs 0.93 0.267
A.Sanchez C.Billingsley 7.57 vs 8.39 3.51 vs 3.34 1.15 vs 1.36 0.606
C.Volstad D.Moseley 5.76 vs 5.69 3.34 vs 3.06 1.51 vs 1.39 0.467
J.Santana A.Harang 6.99 vs 7.38 2.46 vs 2.69 0.87 vs 0.93 0.441
M.Pelfrey D.Moseley 5.44 vs 5.69 2.99 vs 3.06 1.63 vs 1.39 0.517
J.Niese B.Myers 7.49 vs 7.44 3.06 vs 2.77 1.48 vs 1.46 0.416
R.Dickey J.Saunders 5.42 vs 5.40 2.69 vs 2.85 1.39 vs 1.27 0.330
C.Young C.Narveson 7.63 vs 7.45 3.70 vs 3.70 0.54 vs 0.87 0.667
L.Hernandez J.Garland 4.91 vs 4.84 2.89 vs 2.83 1.15 vs 1.33 0.371
J.Lannan P.Maholm 4.66 vs 4.95 3.21 vs 3.01 1.83 vs 1.84 0.349
J.Marquis J.Lannan 4.89 vs 4.66 3.43 vs 3.21 1.58 vs 1.83 0.608
J.Zimmerman W.Rodriguez 8.30 vs 8.40 2.86 vs 2.94 1.33 vs 1.26 0.143
T.Gorzelanny H.Bailey 8.00 vs 7.54 3.88 vs 3.93 1.06 vs 1.20 0.429
C.Carpenter H.Kuroda 6.39 vs 6.83 2.23 vs 2.17 1.81 vs 1.59 0.539
A.Wainwright J.Johnson 8.41 vs 8.96 2.24 vs 2.32 1.50 vs 1.45 0.409
K.Lohse B.Arroyo 5.50 vs 5.38 2.71 vs 2.58 1.12 vs 1.01 0.298
J.Westbrook T.Hudson 5.83 vs 5.63 2.97 vs 2.79 2.65 vs 2.60 0.308
J.Garcia J.Westbrook 7.30 vs 5.83 3.39 vs 2.97 2.29 vs 2.65 1.379
E.Volquez B.Norris 9.43 vs 8.95 4.68 vs 4.33 1.34 vs 1.04 0.845
B.Arroyo K.Lohse 5.38 vs 5.50 2.58 vs 2.71 1.01 vs 1.12 0.298
J.Cueto A.Harang 7.16 vs 7.38 2.76 vs 2.69 1.02 vs 0.93 0.255
T.Wood I.Kennedy 7.61 vs 7.97 3.14 vs 3.28 0.63 vs 0.84 0.522
H.Bailey T.Gorzelanny 7.54 vs 8.00 3.93 vs 3.88 1.20 vs 1.06 0.429
Z.Greinke C.Hamels 8.55 vs 8.56 2.23 vs 2.32 1.01 vs 1.06 0.162
Y.Gallardo C.Kershaw 9.67 vs 9.65 3.43 vs 3.62 1.18 vs 1.06 0.361
S.Marcum T.Lilly 7.65 vs 7.63 2.18 vs 2.23 0.94 vs 0.74 0.404
R.Wolf R.Ohlendorf 6.45 vs 6.48 3.32 vs 3.24 0.95 vs 0.92 0.130
C.Narveson T.Gorzelanny 7.45 vs 8.00 3.70 vs 3.88 0.87 vs 1.06 0.594
R.Dempster C.Billingsley 8.14 vs 8.39 3.27 vs 3.34 1.41 vs 1.36 0.223
R.Wells T.Stauffer 6.57 vs 6.05 2.76 vs 3.03 1.43 vs 1.31 0.577
M.Garza N.Figueroa 7.38 vs 7.04 2.94 vs 3.00 1.16 vs 1.05 0.332
C.Zambrano J.de la Rosa 7.92 vs 8.64 4.23 vs 4.03 1.51 vs 1.31 0.698
C.Silva H.Kuroda 5.63 vs 6.83 1.94 vs 2.17 1.51 vs 1.59 0.895
P.Maholm J.Lannan 4.95 vs 4.66 3.01 vs 3.21 1.84 vs 1.83 0.349
R.Ohlendorf R.Wolf 6.48 vs 6.45 3.24 vs 3.32 0.92 vs 0.95 0.130
C.Morton C.Richard 6.51 vs 6.50 3.64 vs 3.25 1.55 vs 1.43 0.604
J.McDonald I.Kennedy 8.52 vs 7.97 3.48 vs 3.28 0.82 vs 0.84 0.477
K.Correia R.Wolf 6.60 vs 6.45 3.56 vs 3.32 1.14 vs 0.95 0.520
W.Rodriguez J.Zimmerman 8.40 vs 8.30 2.94 vs 2.86 1.26 vs 1.33 0.193
B.Myers J.Niese 7.44 vs 7.49 2.77 vs 3.06 1.46 vs 1.48 0.416
J.Happ T.Gorzelanny 7.87 vs 8.00 4.15 vs 3.88 0.87 vs 1.06 0.546
B.Norris J.Sanchez 8.95 vs 9.49 4.33 vs 4.33 1.04 vs 0.98 0.392
N.Figueroa M.Garza 7.04 vs 7.38 3.00 vs 2.94 1.05 vs 1.16 0.332


There are some interesting results here. The two pitchers with the closest similarity scores are Randy Wolf (MIL) and Ross Ohlendorf (PIT) with a score of 0.130. Zack Greinke and Cole Hamels have a score of 0.162, the lowest score of what I would call upper tier pitchers. There are a few pitchers like Roy Halladay, Cliff Lee and Jaime Garcia who don't have any worthwhile comparables, usually from being outliers in more than one category. If you are interested in the complete spreadsheet of all the comparables, please write me an email (xeifrank-yahoo-com) or leave a comment.

Summary Statistics
Statistic Result
Average K/9 7.14
Stdev K/9 1.45
Average BB/9 2.98
Stdev BB/9 0.70
Average GB/FB 1.32
Stdev GB/FB 0.50


MLB Picks

Friday, January 14, 2011

Same As It Ever Was


Time for another fun little exercise. This time I take a look at similarity scores of NL West starting pitchers. Let's take a look at the scheduled starters from the 5-man rotations of the NL West. The statistics of choice will be K/9, BB/9, GB/FB and age. I will be using the square of the sums to come up with a similarity score for each of the 25 pitchers. Naturally, you don't want your best match to be someone like D.Moseley, T.Stauffer or A.Cook. Let's take a look at the closest overall matches and the closest match for each individual pitcher.

Top 10 Closest Similarity Scores
Rank Pitchers K/9 BB/9 GB/FB Age Score
1 D.Moseley / T.Stauffer 5.69 / 6.05 3.06 / 3.03 1.39 / 1.31 29 / 28 0.405
2 J.Saunders / D.Moseley 5.4 / 5.69 2.85 / 3.06 1.27 / 1.39 29 / 29 0.501
3 J.Saunders / T.Stauffer 5.4 / 6.05 2.85 / 3.25 1.27 / 1.43 29 / 27 0.571
4 C.Richard / T.Stauffer 6.5 / 6.05 3.25 / 3.03 1.43 / 1.31 27 / 28 0.614
5 M.Cain / D.Hudson 7.51 / 8.01 2.66 / 2.69 0.8 / 0.72 26 / 24 0.649
6 J.Garland / J.Saunders 4.84 / 5.40 2.83 / 2.85 1.33 / 1.27 31 / 29 0.651
7 C.Richard / D.Moseley 6.50 / 5.69 3.25 /3.06 1.43 / 1.39 27 / 29 0.797
8 J.Garland / D.Moseley 4.84 / 5.69 2.83 / 3.06 1.33 / 1.39 31 / 29 0.848
9 C.Billingsley / U.Jimenez 8.39 / 8.59 3.34 / 3.59 1.36 / 1.64 26 / 27 0.889
10 M.Cain / I.Kennedy 7.51 / 7.97 2.66 / 3.28 0.80 / 0.84 26 / 26 1.075


Now let's take a look at each pitchers closest match.

Similarities
Pitcher Closest match K/9 BB/9 GB/FB Age Score
C.Kershaw J.Chacin 9.65 / 9.06 3.62 / 3.83 1.06 / 1.45 23 / 23 1.132
C.Billingsley U.Jimenez 8.39 / 8.59 3.34 / 3.59 1.36 / 1.64 26 / 27 0.889
H.Kuroda J.Garland 6.83 / 4.84 2.17 / 2.83 1.59 / 1.33 36 / 31 2.211
T.Lilly A.Harang 7.63 / 7.38 2.23 / 2.69 0.74 / 0.93 35 / 32 1.221
J.Garland J.Saunders 4.84 / 5.40 2.83 / 2.85 1.33 / 1.27 31 / 29 0.651
T.Lincecum C.Billingsley 9.95 / 8.39 3.01 / 3.34 1.37 / 1.36 26 / 26 1.094
M.Cain D.Hudson 7.51 / 8.01 2.66 / 2.69 0.80 / 0.72 26 / 24 0.649
J.Sanchez J.de la Rosa 9.49 / 8.64 4.33 / 4.03 0.98 / 1.31 28 / 29 1.151
B.Zito C.Richard 6.46 / 6.50 3.72 / 3.25 0.89 / 1.43 32 / 27 2.088
M.Bumgarner M.Latos 7.23 / 9.18 2.43 / 2.53 1.23 / 1.03 21 / 23 1.406
U.Jimenez C.Billingsley 8.59 / 8.39 3.59 / 3.34 1.64 / 1.36 27 / 26 0.889
J.de la Rosa J.Sanchez 8.64 / 9.49 4.03 / 4.33 1.31 / 0.98 29 / 28 1.151
J.Hammel T.Stauffer 7.25 / 6.05 2.27 / 3.03 1.33 / 1.31 28 / 28 1.461
A.Cook Z.Duke 3.97 / 4.66 2.78 / 2.46 2.44 / 1.60 32 / 27 2.648
J.Chacin C.Kershaw 9.06 / 9.65 3.83 / 3.62 1.45 / 1.06 23 / 23 1.132
I.Kennedy M.Cain 7.97 / 7.51 3.28 / 2.66 0.84 / 0.80 26 / 26 1.075
D.Hudson M.Cain 8.01 / 7.51 2.69 / 2.66 0.72 / 0.80 24 / 26 0.649
J.Saunders D.Moseley 5.40 / 5.69 2.85 / 3.06 1.27 / 1.39 29 / 29 0.501
B.Enright M.Cain 5.81 / 7.51 2.25 / 2.66 0.70 / 0.80 25 / 26 1.291
Z.Duke J.Saunders 4.66 / 5.40 2.46 / 2.85 1.60 / 1.27 27 / 29 1.282
M.Latos D.Hudson 9.18 / 8.01 2.53 / 2.69 1.03 / 0.72 23 / 24 1.138
C.Richard T.Stauffer 6.50 / 6.05 3.25 / 3.03 1.43 / 1.31 27 / 28 0.614
T.Stauffer D.Moseley 6.05 / 5.69 3.03 / 3.06 1.31 / 1.39 28 / 29 0.405
A.Harang T.Lilly 7.38 / 7.63 2.69 / 2.23 0.93 / 0.74 32 / 35 1.221
D.Moseley T.Stauffer 5.69 / 6.05 3.06 / 3.03 1.39 / 1.31 29 / 28 0.405


Source: Fangraphs

Monday, January 10, 2011

Radio Silence


Not much activity here of late in terms of blog posts, but plenty of calibrating and testing going on behind the scenes for my baseball simulator. I have been back testing a few code changes both in and out of sample for the previous seasons. I am excited about the results I've been getting and I am really looking forward to the 2011 season getting started. I may have a few "just for fun" posts between now and the start of the season, when I begin posting the simulation results of actual games.

For now, I'd like to kick things off with a post documenting the win-loss records of all 30 teams from the August 1st until the end of the season, roughly the last 38 games for each team. I believe that over a 2 month period there are some interesting talent trends that begin to form. One of the most important calibrations for my simulator is determining the correct weighting of past performance in an attempt to best predict current true talent levels. You do not want to weight recent performance to heavy to the point that you are putting too much of an emphasis on good/bad luck. On the other hand, you do not want to give too much weight to past, or old performance to the point that you will miss out on a change in a players true talent level. A formula which strikes the best balance between the two is very challenging and requires a careful set of tests on an in and out of sample set of games. My measuring stick is always the ROI (return on investment) of bets against the Vegas money lines. While I am always looking for flaws in my methodology and ways to improve my results, I believe I have found a good balance between past and recent performance. Thus my interest in the records of each team over the last two months of the season. The table(s) below provide some interesting data, even though some teams have changed more than others this off season, it is interesting to be reminded of how each team finished off the last portion of the season.

2011 Standings (Aug 1st - Oct 3rd)
Team Div Wins Losses WPct
PHI NLE 40 17 .702
MIN ALC 36 22 .621
BAL ALE 33 24 .579
CIN NLC 33 24 .579
SF NLW 32 25 .561
TB ALE 32 27 .542
ATL NLE 32 27 .542
HOU NLC 32 27 .542
TOR ALE 31 27 .534
BOS ALE 30 28 .517
CHA ALC 30 28 .517
MIL NLC 29 28 .509
TEX ALW 29 29 .500
CHN NLC 29 29 .500
COL NLW 29 29 .500
SD NLW 30 30 .500
NYA ALE 29 30 .492
OAK ALW 29 30 .492
DET ALC 28 30 .483
STL NLC 28 30 .483
LAA ALW 27 29 .482
FLA NLE 27 30 .474
ARI NLW 27 31 .466
CLE ALC 26 32 .448
NYN NLE 26 32 .448
LAN NLW 26 32 .448
KC ALC 23 35 .397
WAS NLE 23 35 .397
SEA ALW 22 35 .386
PIT NLC 21 38 .356


Now how about a look at how the playoffs would have looked like using only the last two months of the season as final win-loss records. The National League ends up with the exact same four teams (PHI, CIN, SF, ATL) that actually ended up making the playoffs. In the American League, we end up with three of the four teams (MIN, TB, TEX) that actually made the playoffs. The one American League team, and the only one out of eight playoff teams in both leagues that did not make the playoffs was the Yankees, who were replaced with the Orioles who had tied the third best record of any team over the last two months of the season.

American League
Rays vs Twins
Rangers vs Orioles
 
National League
Giants vs Phillies
Braves vs Reds


So our one surprise team is the Orioles. What happened to their team? Did their players suddenly improve their true talent levels? Or did their luck just improve? It is a likely combination of the two. The important part is obviously being able to measure how much of it was luck and how much of it was an actual positive talent trend. The early 2011 simulations will likely show some favorable Oriole predictions. It will be interesting to see how a team like the Orioles does during the first month or two of the season in the tough AL East division.

Next up, I will begin to look at some head to head two team matchups as the rosters begin to shape up. Also I will have a 2011 "crazy prediction" post, where I make a crazy and "just for fun" prediction for each team, just as a break from the usual logic driven posts I make.