(12) #21 Wisconsin (11-8)

1679.64 (94)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
16 Michigan Loss 9-11 -6.25 72 4.91% Counts Apr 2nd Easterns 2022
24 Ohio State Loss 11-12 -7.34 107 4.91% Counts Apr 2nd Easterns 2022
1 Brown Loss 8-13 1.76 23 4.91% Counts Apr 2nd Easterns 2022
30 North Carolina-Wilmington Loss 10-12 -16.68 33 4.91% Counts Apr 2nd Easterns 2022
69 Virginia Tech Win 15-9 8.57 33 4.91% Counts Apr 3rd Easterns 2022
189 Wisconsin-Eau Claire** Win 15-6 0 5 0% Ignored (Why) Apr 23rd Lake Superior D I College Mens CC 2022
172 Wisconsin-Whitewater Win 15-10 -21.7 7 5.84% Counts Apr 23rd Lake Superior D I College Mens CC 2022
133 Wisconsin-Milwaukee Win 15-8 -5.67 5 5.84% Counts (Why) Apr 23rd Lake Superior D I College Mens CC 2022
13 Minnesota Loss 6-13 -29.37 22 6.19% Counts (Why) Apr 30th North Central D I College Mens Regionals 2022
172 Wisconsin-Whitewater** Win 13-1 0 7 0% Ignored (Why) Apr 30th North Central D I College Mens Regionals 2022
133 Wisconsin-Milwaukee** Win 13-3 0 5 0% Ignored (Why) Apr 30th North Central D I College Mens Regionals 2022
97 Marquette Win 11-8 -9.48 5 6.19% Counts May 1st North Central D I College Mens Regionals 2022
22 Carleton College Win 13-8 32.29 17 6.19% Counts May 1st North Central D I College Mens Regionals 2022
95 Iowa State Win 10-9 -24.51 5 6.19% Counts May 1st North Central D I College Mens Regionals 2022
2 North Carolina Loss 12-15 16.84 31 7.8% Counts May 27th 2022 D I College Championships
10 California Loss 9-14 -25.7 20 7.8% Counts May 27th 2022 D I College Championships
24 Ohio State Win 14-10 32.27 107 7.8% Counts May 28th 2022 D I College Championships
9 Vermont Loss 13-15 0.61 97 7.8% Counts May 28th 2022 D I College Championships
14 North Carolina State Win 15-9 56.12 70 7.8% Counts May 29th 2022 D I College Championships
**Blowout Eligible. Learn more about how this works here.

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.