For almost 11 years now I have allowed my passion to get in the way of sound statistical reasoning when it came to predicting the chances for my hometown Toronto Blue Jays to compete for the American League Wildcard.

Last season, I had the Jays finishing with 94 wins, and just ahead of the Yankees for second place in the East. The season before, one that saw the Jays win 68 games, I listened to Carlos Tosca and had the Jays with 90 wins. The year before that I had the Jays beating out the Twins for the wildcard spot. In 1994, I had the Jays winning their third straight World Series (whatever happened to that season anyway?).

At the conclusion of every season since 1993, it seems, I have had to deal with the wrath of my friends, family members, and advice-seekers (whom I managed to coerce into wagering some of their hard-earned money) for my failure in separating reason from passion as it pertains to the Toronto Blue Jays. As an aside, my predictions as to the finishes of other teams in which I do not have a rooting interest have seen successes in the 91% range.

So imagine my predicament this season, with Steve Phillips, as well as some other so called reputable magazines having the Jays finishing with 90+ wins and the wildcard spot. Am I ready to go out on a limb once again, and predict the Jays to return to the playoffs?

And what about the other happenings in MLB this season? Will the Whitesox repeat as World Series Champions? Who will win the Cy Young Award? The batting title? Will anyone hit 50 homeruns in this post-steroid era? Will Florida or Kansas City win a game? Can Gillick lead the Phillies back to the postseason? Will the Braves return to the playoffs…again?

I have set out to answer all of these questions this season, not by making predictions from the heart, but by using a computer-based simulation program known as Diamond Mind Baseball. DMB is a comprehensive simulation program that, at the most easy to understand level, uses statistics, player attributes, stadium rankings and managerial profiles to generate a hypothetical season based on the information that the user inputs into the program. For example, a user can test their general manager capabilities by drafting or trading for any roster of players that he wishes (Eric Hinske playing right field for the Yankees and Alex Rodriguez playing shortstop for the Jays) and then simulating the season to see the results.

At any rate, DMB has proven itself to be very accurate when it comes to expected results. In my, and others, experiences with the program it has been a rarity to come across statistical anomalies, for example, Eric Hinske having a season ending batting average above .279 with 30+ homeruns, or Manny Lee hitting 5 homeruns in a game.

With this in mind, I set up DMB baseball to simulate the upcoming 2006 season. Before going into the results of this simulation, I think it prudent to point out a few details as to exactly how I went about setting up the program.

First, while I did use 2006 rosters for every team, (using the team sites on www.mlb.com) the statistics used for every player is from their 2005 seasons. This means that DMB, and therefore my simulation, did not take into account potential player improvements (A.J. Burnett having a better 2006 season because of his contract and newfound desire). Also, since I used 2005 stats, rookies could not be used. This proved difficult when trying to assemble the lineups for Florida and Kansas City.

Second, in determining the starters for every position, I used the depth chart listed on mlb.com for each team. So, while it may seem unlikely that K.Millar will play right field for the Orioles, this is where he was placed in the simulation.

Third, to eliminate my influences, batting lineups were given a standardized order for every team. This was 1. 2B 2. Lf 3. RF. 4. 1B 5. DH 6. CF 7. 3B 8. C 9. SS So, in some instances we had players hitting in simulated positions that they would surely never actually hit in real life (Tejada and Rodriguez hitting 9th)

Fourth, all managerial profiles were set to neutral. Anyone familiar with the program knows that you can change managerial attributes to allow a team to run more, play the infield in, uses platoon situations etc. Since it is impossible to know exactly how Jay Gibbons manages in comparison to Tony Larussa, I decided to remove such influences on the simulation.

Fifth, all teams had a mandatory 5 man rotation. Each teams rotation was established using the depth chart on mlb.com.

Despite taking all of these steps to ensure statistical accuracy, it must be stated that while DMB is an extraordinarily detailed simulation program, it is not a guaranteed correct one. So before I go into describing the results, remember that I do not recommend using the results to influence any gambling tendencies….

Here we go……

2006 Final Standings

Al East
		 W	L
New York	102	60
Toronto		 93	69
Boston		 76	86
Baltimore	 75	87
Tampa Bay	 66	96


AL Central	 W	L
Cleveland	 90	72
Minnesota	 89	73
Chicago		 87	75
Detroit		 78	84
Kansas City	 64	97

AL West		 W	L
Los Angeles	 85	77
Texas	    	 85	77
Oakland		 82	80
Seattle		 69	92

NL East		 W	L
Philadelphia	99	63
New York	92	70
Atlanta		92	70
Washington	74	88
Florida		51	111

NL Central	 W	L
Chicago		86	76
Milwaukee	80	82
St. Louis	80	82
Houston		76	86
Pittsburg	73	89
Cincinnati	73	89

NL West		 W	L
San Francisco	90	72
Los Angeles	87	75
San Diego	84	78
Colorado	79	83
Arizona		72	90

Playoffs

Divisional Series: Yankees over Blue Jays, Angels over Indians, Phillies over Mets, Cubs over Giants

LCS: Yankees over Angels Cubs over Phillies

World Series: Yankees over Cubs

AL Cy Young: Wang (Yankees) 20-4 2.32
NL Cy Young: Smoltz (Braves) 22-10 2.44

AL Batting Champion: Mark Ellis (Oak) .359
NL Batting Champion: Moises Alou (SF) .346

AL HR Champion: Jeremy Hermidia (Fla) 51
NL HR Champion: Reggie Sanders (KC) 38

From a Blue Jays perspective there were many “eye-openers.” (Besides qualifying for the post season) B.J. Ryan proved himself to be an average closer, blowing eight saves. His final numbers were 9-5 with 27 saves and an a 3.00 Era. A.J Burnett seemed to follow his career numbers going 13-13 with a 3.37 Era. Halladay bounced back (15-3, 1.83) and Lilly continued his downward spiral (7-8, 5.09). The break out member of the pitching staff was Josh Towers (17-12, 2.71).

On the offensive side of the ledger, Troy Glaus provided his expected power numbers (36 HR), but the team as a whole struggled badly (.258 B.A.). Vernon Wells had a terrible year (.248 18 HR 71 RBI) and Lyle Overbay put up Eric Hinske-like numbers (.258 8 HR 44 RBI). Benji Molina added greatly to the offense, hitting .315 with 16 hrs.

So there you have it. A statistical approach to the upcoming 2006 season. I will be back in October to see how accurate DMB was in predicting the final results. One thing is for certain, if the program does prove to be correct, then all of us who participate in the DMB league may in fact have the capabilities of being real life general managers, not to mention making millions of dollars in Vegas.

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