Place Team Points #1 Votes Movement 1 Texas 168 1 - 2 Michigan 154 1 - 3 Florida State 148 1 +1 4 Ohio State 142 1 +1 5 Washington 140 0 +1 6 Penn State 134 0 +2 7 Georgia 133 3 -4 8 Oregon 123 0 +3 9 USC 117 0 -2 10 Oklahoma 113 0 - 11 Utah 99 0 +4 12 Alabama 96 0 +5 13 North Carolina 83 0 - 14 Notre Dame 75 0 -5 15 Duke 66 0 -1 16 Washington State 62 0 +6 17 Miami (FL) 61 0 +3 18 LSU 60 0 -2 19 Ole Miss 42 0 -7 20 Kansas 32 0 NR 21 Tennessee 28 0 +2 22 Texas A&M 28 0 NR 23 Oregon State 27 0 -4 24 Missouri 23 0 - 25 Louisville 20 0 NR
Others receiving votes: Fresno State (15), Kentucky (14), Liberty (11), Wisconsin (11), Rutgers (9), Maryland (8), Florida (7), Kansas State (7), Iowa (5), Ohio (5), Syracuse (5), BYU (2), James Madison (2)
Link to check out the spreadsheet of responses here
I think pollsters tend to show some cognitive dissonance about poll inertia.
For example, UGA continues to receive plenty of first place votes (and heaps of them in the AP poll) based on preseason expectations (seeing as they’ve done nothing on the field). On the other hand, CU enters the poll, defying preseason expectations (and logic), and then completely falls out because they embarrassed themselves in front of 10 million viewers. Why the double standard?
That’s one reason why I like computer polls. Teams know exactly what they need to do to move up. The BCS computers did nothing wrong!!!
I never had a problem with BCS, I was too young to know anything else lol. fwiw, in my totally subject to bias gut rankings approach UGA has been steadily dropping over the past 2 or 3 weeks, and I don’t know if CU made it higher than 22nd or 23rd. But I do partially think that a cfb poll should be reflective of relative performance as momentum and vibes are very real things in this sport imo. And computers can have this bias as well. A formula that say, favors explosive pass plays, would think we were the greatest last year and a mid 40s team this year. But as someone who has watched both teams I still contend our ceiling is about as high this year, we just won’t spend as much time there. As is oft the case in data analysis, half the trick of understanding the data is being able to interpret why the data is the way it is. Potentially bad example because of my biases but hopefully that point comes across.