The Monday after the first semi-full weekend of DI games mark the return of the Rutter Rankings. Here are the current top 10:
Many more details, including the rest of the teams, explanation of the ratings, a cool graph, and much (OK, not that much) more can be found here:
http://math.bd.psu.edu/faculty/rutter/WomensRankings.html
FAQ
Q. [Insert team here, like Cornell] hasn't played a game yet. How can they be ranked?
A. The power of Bayesian statistics. Each team has a prior rating based on last year, so early season rankings are possible. As more games are played, the effect of the prior is reduced.
Q. Is home ice advantage included?
A. No. Although I have estimates of home ice advantage for both the entire division and for each team, they are not included in these rankings. It emulates the NCAA criteria in this respect.
Q. Is margin of victory (MOV) included?
A. No, just wins, losses, and ties. Again, the NCAA doesn't use margin of victory and since the games are low scoring, I haven't found a satisfactory way to include MOV.
Q. How do you include ties?
A. If you look at the web page, you will see how ties are included. In short, I estimate a region that includes a probability of a tie which is larger when the teams are closely ranked. No "tie is half a win, half a loss" simplification here.
Q. How does your method compare to RPI?
A. The two are very different. I think my system (and KRACH) are a much better reflection of the quality of teams as statistical models are used as opposed to arbitrary alegbra. But I am biased. Someday, I will do a really complete comparission of the methiods.
Code:
Team Rating
1 Minnesota 2.0722
2 Wisconsin 1.5283
3 Ohio State 1.4741
4 Cornell 1.4576
5 Boston Univ. 1.2104
6 Clarkson 1.1280
7 Mercyhurst 1.0514
8 Northeastern 0.9756
9 North Dakota 0.8331
10 Harvard 0.7293Many more details, including the rest of the teams, explanation of the ratings, a cool graph, and much (OK, not that much) more can be found here:
http://math.bd.psu.edu/faculty/rutter/WomensRankings.html
FAQ
Q. [Insert team here, like Cornell] hasn't played a game yet. How can they be ranked?
A. The power of Bayesian statistics. Each team has a prior rating based on last year, so early season rankings are possible. As more games are played, the effect of the prior is reduced.
Q. Is home ice advantage included?
A. No. Although I have estimates of home ice advantage for both the entire division and for each team, they are not included in these rankings. It emulates the NCAA criteria in this respect.
Q. Is margin of victory (MOV) included?
A. No, just wins, losses, and ties. Again, the NCAA doesn't use margin of victory and since the games are low scoring, I haven't found a satisfactory way to include MOV.
Q. How do you include ties?
A. If you look at the web page, you will see how ties are included. In short, I estimate a region that includes a probability of a tie which is larger when the teams are closely ranked. No "tie is half a win, half a loss" simplification here.
Q. How does your method compare to RPI?
A. The two are very different. I think my system (and KRACH) are a much better reflection of the quality of teams as statistical models are used as opposed to arbitrary alegbra. But I am biased. Someday, I will do a really complete comparission of the methiods.
