The best explanation for the inability of projection models to reflect reality in anything but the aggregate comes from an analogy to financial modeling, and the best explanation for the inability of financial modeling comes in Nassim Taleb's work, which I'd recommend even if you aren't interested in finance because of how universally applicable this kind of thinking is. I'm partial to "Fooled by Randomness" because the book blew my hair back when I first read it, but "The Black Swan" is quite good too. Basically, one of the core tenets of the arguments made is that, while a bell curve distribution may exist, the "tails," or the events that are statistically so improbable as to be virtually impossible are not only not impossible, but if they are treated as such, the models will not only fail but they will fail catastrophically, with cascading effects everywhere else. They're colloquially called the "fat tails" of the bell curve. We got an object lesson in the fat tails of the bell curve when mortgage backed securities, which were so "safe" as to be considered statistically virtually riskless, blew up and took down the market. Baseball reveals its fat tails again and again (remember the old saying "that's why they play the games"?), and as one poster previously mentioned, I would consider the Twins' 1991 season to be a resident of pretty darn fat part of a fat tail of a bell curve. TL;DR, the models may be able to accurately tell you what will happen in the aggregate, but there will be outliers each and every year that make you stand back, scratch your head, and ask "what the actual ****?"