World Series Game 5: Detroit vs St Louis

Game 5: Justin Verlander vs Jeff Weaver
Team	Average Runs	% Games won	Pyth.
Detroit	3.503	45.2%	.4863
St Louis	3.6	55.8%	.5137
Slight Advantage: Cardinals

World Series Game 4: Detroit vs St Louis

Game 4: Jeremy Bonderman vs Jeff Suppan
Team	Average Runs	% Games won	Pyth.
Detroit	3.468	46.5%	.5045
St Louis	3.437	53.5%	.4955
Slight Advantage: Tigers (Toss Up)

World Series Game 3: Detroit vs St Louis

Game 3: Nate Robertson vs Chris Carpenter
Team	Average Runs	% Games won	Pyth.
Detroit	3.01	37.8%	.3899
St Louis	3.77	62.6%	.6101
Advantage: Cardinals

World Series Game 2: St Louis vs Detroit

Game 2: Jeff Weaver vs Kenny Rogers
Team	Average Runs	% Games won	Pyth.
St Louis	5.188	62.6%	.6523
Detroit	3.788	37.4%	.3477
Big Advantage: Cardinals

World Series Game 1: St Louis vs Detroit

Game 1: Anthony Reyes vs Justin Verlander
Team	Average Runs	% Games won	Pyth.
St Louis	4.622	52.8%	.5568
Detroit	4.124	47.2%	.4432
Advantage: Cardinals

NLCS Game 7: St Louis vs New York

Game 7: Jeff Suppan vs Oliver Perez
Team	Average Runs	% Games won	Pyth.
St Louis	5.27	51.1%	.556
New York	4.71	48.9%	.444
Advantage: Cardinals

NLCS Game 6: Carpenter vs Maine

Game 6: Carpenter vs Maine
Team	Average Runs	% Games won	Pyth.
St Louis	4.40	49.2%	.529
New York	4.16	50.8%	.471
Slight Advantage: Cardinals

NLCS Game 5: New York vs St Louis

Game 5 Glavine vs Weaver
Team	Average Runs	% Games won	Pyth.
New York	5.27	53.3%	.582
St Louis	4.67	46.7%	.418
Advantage: Mets

NLCS Game 4: New York vs St Louis

Game 4 Perez vs Reyes
Team	Average Runs	% Games won	Pyth.
New York	4.5	40%	.475
St Louis	4.73	60%	.525
Slight Advantage: Cardinals

NLCS Game 2: St Louis vs New York

Game 2: Carpenter vs Maine
Team	Average Runs	% Games won	Pyth.
St Louis	4.36	43.3%	.474
New York	4.6	56.6%	.526
Slight Advantage: Mets

ALCS Game 3: Oakland vs Detroit

Game 3: Harden vs Rogers
Team	Average Runs	% Games won	Pyth.
Oakland	3.93	46.7	.483
Detroit	4.07	53.3%	.517
Slight Advantage: Detroit

ALCS Game 2: Detroit vs Oakland

Game 2: Verlander vs Loaiza
Team	Average Runs	% Games won	Pyth.
Detroit	4.28	44%	.455
Oakland	4.68	56%	.545
Slight Advantage: A's

NLCS Game 1: St Louis vs New York

Game 1: Weaver vs Glavine
Team	Average Runs	% Games won	Pyth.
St Louis	5.78	46%	.518
New York	5.58	54%	.482
Slight Advantage: Cardinals

ALCS Game 1: Detroit vs Oakland

Game 1: Robertson vs Zito
Team	Average Runs	% Games won	Pyth.
Detroit	4.58	52%	.536
Oakland	4.26	48%	.464
Slight Advantage: Tigers

Game 4: San Diego vs St Louis

Game 4: Williams vs Carpenter
Team	Average Runs	% Games won	Pyth.
San Diego	4.16	56%	.561
St Louis	3.68	44%	.439
Advantage: Padres

Game 4: New York vs Detroit

Game 4: Wright vs Bonderman
Team	Average Runs	% Games won	Pyth.
New York	7.20	64%	.707
Detroit	4.64	36%	.293
Big Advantage: Yankees

Game 3: San Diego vs St Louis

Game 3: Young vs Suppan
Team	Average Runs	% Games won	Pyth.
San Diego	5.40	72%	.726
St Louis	3.32	28%	.274
Big Advantage: Padres

Game 3: New York vs Detroit

Game 3: Johnson vs Rogers
Team	Average Runs	% Games won	Pyth.
New York	7.75	72.5%	.698
Detroit	5.10	27.5%	.302
Big Advantage: Yankees

Game 3: New York vs Los Angeles

Game 3: Trachsel vs Maddux
Team	Average Runs	% Games won	Pyth.
New York	5.16	60%	.601
Los Angeles	4.20	40%	.399
Advantage: Mets

Note: Sim ran again after Nomar injury was reported.

Game 3: Minnesota vs Oakland

Game 3: Radke vs Haren
Team	Average Runs	% Games won	Pyth.
Minnesota	4.80	52%	.526
Oakland	4.56	48%	.474
Slight Advantage: Twins

St Louis vs San Diego

Game 1: Carpenter vs Peavy
Team	Average Runs	% Games won	Pyth.
St Louis	4.88	64%	.643
San Diego	3.64	36%	.357
Advantage: Cardinals
Game 2: Weaver vs Wells
Team	Average Runs	% Games won	Pyth.
St Louis	5.24	52%	.548
San Diego	4.76	48%	.452
Advantage: Cardinals
Outcomes		Odds
Cardinals lead 2-0		35.2%
Series tied 1-1		48.7%
Padres lead 2-0		16.1%

Detroit vs New York

Game 1: Robertson vs Wang
Team	Average Runs	% Games won	Pyth.
Detroit	5.32	52%	.440
New York	6.0	48%	.560
Advantage: Yankees
Game 2: Verlander vs Mussina
Team	Average Runs	% Games won	Pyth.
Detroit	4.32	40%	.366
New York	5.68	60%	.634
Advantage: Yankees
Outcomes		Odds
Yankees lead 2-0		35.5%
Series tied 1-1		48.4%
Tigers lead 2-0		16.1%

Los Angeles vs New York

Game 1: Lowe vs Maine
Team	Average Runs	% Games won	Pyth.
Los Angeles	4.12	40%	.420
New York	4.84	60%	.580
Advantage: Mets
Game 2: Guo vs Glavine
Team	Average Runs	% Games won	Pyth.
Los Angeles	5.64	44%	.518
New York	5.44	56%	.482
Slight Advantage: Dodgers
Outcomes		Odds
Mets lead 2-0		28.0%
Series tied 1-1		50.2%
Dodgers lead 2-0		21.8%

Oakland vs Minnesota

Game 1: Zito vs Santana
Team	Average Runs	% Games won	Pyth.
Oakland	5.24	56%	.508
Minnesota	5.16	44%	.492
Slight Advantage: A's
Game 2: Loaiza vs Boonser
Team	Average Runs	% Games won	Pyth.
Oakland	4.8	44%	.526
Minnesota	4.56	56%	.474
Slight Advantage: A's
Outcomes		Odds
A's lead 2-0		30.5%
Series tied 1-1		46.2%
Twins lead 2-0		23.3%

2006 Playoff Sims

I will be using the baseball game simulator program that I wrote to simulate playoff game outcomes. I will run a sample size of 25 (now up to 1,000 games for the World Series) games for each game. I will run the first two games of each series, then I will simulate each remaining game the day of or the night before that game is to be played. How does the software work? A text file of hitting and pitching stats is used to determine the likeliehood of each at bat outcome. Hitting outcomes are determined on an at-bat by at-bat basis and not a pitch by pitch basis. The software uses a random number generator to determine if the batter gets on base. If his OBP is .400 and the random number is less than .400 then he gets on base. Once he is on base then this same random number is used to determine what kind of hit it is, or if it is a walk based on single, double, triple, HR and walk rates. A constant vs LHP vs RHP split is used to account for lefty pitcher vs lefty hitter type of situations. Good pitchers are given an advantage over bad pitchers in that they begin the game with a number that is subtracted off of the hitters OBP/batting average etc... This number is decreased for each batter the pitcher faces, thus simulating a pitcher getting tired the more pitches he throws. Artificial intelligence algorithms determine when to steal, sac bunt, pinch hit, bring in a new pitcher or throw a beach ball onto the field. Lineups and pitching matchups are selectable and there is an option for using a DH instead of having the pitcher bat, for American League simulated games. This is all I can think of as far as a description goes for now. If you have any questions, please send them to: xeifrank@yahoo.com vr, Xei