There is a brief explaination of his rankings at the bottom of the link but not enough to really tell what he's doing. But it's kind of interesting to look at. Claims to have a pretty good success rate at predicting games.
My preseason rankings come from a regression model that uses The Power Rank’s team rankings from the past 4 years, turnovers and returning starters. For more details, see the bottom of this post.
The college football preseason model is simple. It doesn’t break down a team into offense and defense or distinguish a starting quarterback from a linebacker. These are areas for potential improvements.
However, the model performs very well in predicting the winners of games. The model assigns each team a rating, and the difference in the rating of two teams gives a predicted margin of victory on a neutral field. The home team gets an extra 3 points.
For example, Ohio States opens the season at Virginia Tech. The Buckeyes have a 19.7 rating, which gives a predicted margin of victory against an average team. The Hokies have a 11.0 rating.
On a neutral field, Ohio State would beat Virginia Tech by 8.7 points. The home advantage for Virginia Tech brings this advantage down to 5.7 points. The model still predicts an Ohio State victory.
In 2014, this model predicted the winners in 70.4% of games. With a large sample of 678 games, the model performed very close to the 70.5% win rate from the 2005 through 2013 seasons.
Link1. Alabama, 20.91.
2. Ohio State, 19.70.
3. Oregon, 19.65.
4. Baylor, 18.02.
5. TCU, 17.22.
14. Wisconsin, 12.72.
16. Michigan State, 11.97.
32. Nebraska, 7.94.
34. Michigan, 7.60.
38. Penn State, 5.79.
42. Brigham Young, 5.08.
43. Miami (FL), 4.84.
47. Minnesota, 3.38.
49. Iowa, 3.19.
55. Northwestern, 0.99.
70. Rutgers, -1.80.
71. Purdue, -1.99.
75. Illinois, -2.65.
109. Southern Miss, -12.92.
Last edited by a moderator: