College Women's USAU Rankings (NC)

2024-25 Season

Data updated through April 28 at 11:45pm EDT

FAQ
Division I // Division III
Rank    Change Team                                                 Record Rating Change Region Conference Div   SoS PDC %
2 Carleton College NC 1 23-2 2563.52 435 North Central Western North Central DI D-I 2186.01 377.52 0.17
24 Minnesota 16-7 1821.36 313 North Central Western North Central DI D-I 1737.33 84.05 0.05
35 5 Wisconsin 16-12 1633.39 390 North Central Lake Superior DI D-I 1503.58 129.83 0.09
46 11 Carleton College-Eclipse 14-4 1545.04 407 North Central North Central DIII D-III 1303.96 241.09 0.18
55 11 St Olaf 14-9 1441.89 182 North Central North Central DIII D-III 1329.72 112.23 0.08
88 8 Michigan Tech 12-7 1135.42 201 North Central North Central DIII D-III 1091.81 43.7 0.04
92 5 Iowa State 18-12 1112.02 217 North Central Western North Central DI D-I 1035.39 76.71 0.07
95 Marquette 8-4 1094.28 303 North Central Lake Superior DI D-I 1026.05 68.24 0.07
99 5 Wisconsin-Eau Claire 13-9 1086.22 258 North Central Lake Superior DI D-I 1028.46 57.82 0.06
102 18 Macalester 13-7 1055.12 378 North Central North Central DIII D-III 982.2 73.01 0.07
129 Winona State 14-11 845.74 253 North Central North Central DIII D-III 830.35 15.08 0.02
132 20 Iowa 12-10 826.85 138 North Central Western North Central DI D-I 768.42 58.48 0.08
147 1 Wisconsin-La Crosse 9-14 724.89 253 North Central Lake Superior DI D-I 682.53 42.43 0.06
194 7 St Olaf-B 4-13 471.63 196 North Central North Central DIII D-III 609.81 -138.2 -0.23
195 Minnesota-B 3-6 468.5 North Central ? 441.89 26.62 0.06
228 Carleton College-C 2-4 180.92 North Central ? 241.23 -60.17 -0.25
246 27 Wisconsin-B 0-10 27.96 387 North Central Lake Superior DI Dev 427.18 -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.