[Bumped.  SpartanDan clearly getting a leg up for when everyone eventually concedes the BCS model is clearly superior to the NCAA Tournament and the NCAA goes out looking for non-margin-of-victory-acknowledging computer rankings to determine the basketball championship game match-up.

More seriously: This system seems to have some utility as basically the antithesis of the Pomeroy ratings.  Answers the question "Who have you beat?", as opposed to "How likely are you to beat future opponents?"  At least I hope so--otherwise we only have a 1/7 chance of beating Texas. -KJ]

For those of you who are hockey fans, you may have heard of KRACH (Ken's Ratings for American College Hockey). The basic idea behind the ratings is as follows:

1) If two teams play a long series, (Team A wins) / (Team B wins) should be equal to (Team A rating) / (Team B rating). Put another way, a team with a rating twice as high as their opponent should win 2/3 of the time.

2) Given the definition above of expected winning percentage in a given matchup, each team's expected winning percentage against their schedule to date should match their actual winning percentage. This is impossible with winless or unbeaten teams (their ratings would have to be 0 or infinite, respectively); as a fix, a fictional tie (half-win, half-loss) against a perfectly average team is added to each team's record. Non-D1 games are ignored.

A more detailed explanation can be seen here (the main differences are that I have used an average rating of 1 instead of 100, which merely scales everything by a factor of 100, and the only tie games are the fictional ones added to avoid zero or infinite ratings and guarantee some degree of connectedness between teams). Also HT: quakk at mgoblog, who has done the same thing for college football, and Ken Pomeroy who provides the list of games in an easy-to-parse format.

Like the RPI, and unlike Pomeroy's rankings, this system cares only about wins and losses (and who those wins and losses came against). The big advantage of this system relative to RPI is that a win can never hurt you. Playing against a terrible team can hurt your SOS badly enough to overcome the gain in record with the RPI, but here it adds slightly less than one win to your projected record - hence your rating must go up slightly (far less than if you beat a good team, however).

Top 10 + MSU are listed here (through games of Saturday); after the jump, a full table (top 100 + all BCS conference teams and MSU non-conference opponents)

1. Syracuse 110.44 (9-0, SOS = 5.812)
2. Villanova 98.299 (9-0, 5.173)
3. Kentucky 73.479 (10-0, 3.499)
4. New Mexico 58.276 (10-0, 2.775)
5. Texas 57.524 (8-0, 3.384)
6. Purdue 54.108 (9-0, 2.848)
7. Georgetown 53.720 (8-0, 3.160)
8. West Virginia 42.984 (7-0, 2.866)
9. Kansas 36.192 (9-0, 1.905)
10. Missouri St. 36.119 (9-0, 1.901)
37. Michigan St. 8.867 (8-2, 2.608)

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