College Women's USAU Rankings (NC)

2024-25 Season

Data updated through May 4 at 8:45pm EDT (some scores likely missing)

FAQ
Division I // Division III
Rank    Change Team                                                 Record Rating Change Region Conference Div   SoS PDC %
2 Carleton College NC 1 23-2 2568.81 5 North Central Western North Central DI D-I 2191.3 377.52 0.17
24 Minnesota 16-7 1823.12 2 North Central Western North Central DI D-I 1739.08 84.05 0.05
35 Wisconsin 16-12 1633.8 0 North Central Lake Superior DI D-I 1503.98 129.83 0.09
46 Carleton College-Eclipse 14-4 1538.86 6 North Central North Central DIII D-III 1297.78 241.09 0.19
55 St Olaf 14-9 1441.22 1 North Central North Central DIII D-III 1328.99 112.23 0.08
87 1 Michigan Tech 12-7 1133.92 2 North Central North Central DIII D-III 1090.22 43.7 0.04
92 Iowa State 18-12 1111.73 0 North Central Western North Central DI D-I 1035.03 76.71 0.07
95 Marquette 8-4 1094.02 0 North Central Lake Superior DI D-I 1025.78 68.24 0.07
98 1 Wisconsin-Eau Claire 13-9 1085.78 0 North Central Lake Superior DI D-I 1027.97 57.82 0.06
101 1 Macalester 13-7 1054.48 1 North Central North Central DIII D-III 981.47 73.01 0.07
128 1 Winona State 14-11 844.6 1 North Central North Central DIII D-III 829.5 15.08 0.02
130 2 Iowa 12-10 826.07 1 North Central Western North Central DI D-I 767.6 58.48 0.08
145 2 Wisconsin-La Crosse 9-14 724.19 1 North Central Lake Superior DI D-I 681.77 42.43 0.06
192 2 St Olaf-B 4-13 470.42 1 North Central North Central DIII D-III 608.6 -138.2 -0.23
195 Minnesota-B 3-6 467.82 1 North Central ? 441.21 26.62 0.06
227 1 Carleton College-C 2-4 180.25 1 North Central ? 240.42 -60.17 -0.25
245 1 Wisconsin-B 0-10 26.43 2 North Central Lake Superior DI Dev 426.11 -399.69 -0.94

FAQ

The results on this page ("USAU") are the results of an implementation of the USA Ultimate Top 20 algorithm, which is used to allocate post season bids to both colleg and club ultimate teams. The data was obtained by scraping USAU's score reporting website. Learn more about the algorithm here. TL;DR, here is the rating function. Every game a team plays gets a rating equal to the opponents rating +/- the score value. With all these data points, we iterate team ratings until convergence. There is also a rule for discounting blowout games (see next FAQ)
For reference, here is handy table with frequent game scrores and the resulting game value:
"...if a team is rated more than 600 points higher than its opponent, and wins with a score that is more than twice the losing score plus one, the game is ignored for ratings purposes. However, this is only done if the winning team has at least N other results that are not being ignored, where N=5."

Translation: if a team plays a game where even earning the max point win would hurt them, they can have the game ignored provided they win by enough and have suffficient unignored results.