The New York Times computer model additionally collapses runaway scores, takes note of home-field advantage, and counts recent games more heavily than earlier games. The rankings compiled by Jeff Sagarin incorporate similar adjustments. These computer-generated rankings inevitably show biases, reflecting human judgment about which criteria are the best predictors of future success. Once the method is selected, human emotion no longer comes into play from one week to the next in determining who's up or down in the rankings. Despite their seeming objectivity, however, many people can't quite bring themselves to trust the computer-generated rankings.
Fans find apparently counterintuitive results hard to swallow.
Besides, the computer polls don't even agree among themselves. In the end, when it comes down to determining the merit of various methods for ranking teams, the value of any particular system resides in the mind of the beholder. There is no unique, optimal way to devise an objective ranking scheme. Moreover, there's always the temptation to fiddle with the mathematics to obtain a result more in line with human thinking, in effect enshrining preconceived notions of how things should turn out. Every scheme has particular strengths and weaknesses.