When does modern baseball cross the divide into mainstream science?? How can we get more people to work and investigate probabilistic sabermetrical applications in the nuts and bolts of game theoretic related activities. I've provided excerpts below from two articles that may inspire (feel free to look at the link too).

http://www.science20.com/bruce_bukie...diction-121526

In baseball, there are only 25 possible situations: 3 out situations (0, 1, or 2 outs) times 8 base runner situations (no one on base, man on first etc. through bases loaded) plus the 3 outs state. By arranging the computation efficiently one can compute the run distributions for 2 lineups and use basic probability ideas to compute the probability of each team winning a game in about one second on a typical laptop computer. One can also use this technique to compute how well a team should do during a season, who should win the MVP and Cy Young awards, whether a trade should produce more or less wins for a team, who should win a post-season series and more.
http://www.hardballtimes.com/10-less...all-simulator/

On the other hand, the results shown above contradict the way in which many to most baseball fans and analysts think about the game, and about season results. A team that wins 104 games is*clearly*better than a team that wins 88. Even a team that wins 98 would be put in a different category than a team that wins 93, especially with the same quality of opponent. And yet, these are entirely plausible ranges of outcome for a team season. 104 and 88 are likely extreme cases for the team, but as we see above, it is not out of the question for a 96ish-win team to end up with 88 or 104 wins. As much as we want to ascribe final results entirely to skill, the fact of the matter is that there’s always an error range, even for entire seasons, a fact that should be in the back of our minds when we summarize past seasons or predict future ones.