Let's take a stroll back to our undergraduate pysch years, shall we? Remember Clark Hull, he the behaviorist/learning theorist who tried to come up with a mathematical equation that would explain the Stimulus -> Response pathway in humans? He started with just a couple of variables, such as O that stood for something like "all variables within the organism." When that didn't yield reliable or valid results, he kept adding more and more variables to the equation, such as "habit strength" and "drive", and when these didn't work he added subvariables to all, complete with supercripts and subscripts.

God I loved that man.

Well, I of course tried to do a similar thing as you and Hull, but trying to predict baseball games instead of basketball. I can't remember all the variables now, but they included such obvious things such as home winning percentage, road winning percentage, ratio of errors committed to errors by opposing team, ERA, etc. Common, obvious variables. When this yielded results that were less than perfect, I of course added some other variables. How about game temperature? Then, to get better results still more variables were added... with the thought being that if only I could add enough variables, and if only I could weight them appropriately, I'd be able to predict who'd win the division easily.

Of course, even with an infinite number of variables this doesn't work, because one forgets that sporting events, like the humans between the Stimulus -> Response link, are infinitely unpredictable.

To put it more clearly, as much as I'd like Texas to go to the final four, *my* model (i.e. "simple hunch") says that they'll lose in the reginal semis to Iowa, "rebound differential" be damned.

As Skinner would say, put that in your black box and smoke it... ;-)

There's also this, not to rub salt in a wound or anything:

http://tinyurl.com/zsdp4