(5) #28 Wisconsin (10-11)

1595.9 (66)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
20 Brown Loss 11-13 -6.24 6 3.94% Counts Feb 3rd Florida Warm Up 2023
124 Illinois Win 13-6 2.47 86 3.94% Counts (Why) Feb 3rd Florida Warm Up 2023
102 Central Florida Win 13-7 4.76 14 3.94% Counts (Why) Feb 4th Florida Warm Up 2023
10 Minnesota Loss 13-15 0.29 1 3.94% Counts Feb 4th Florida Warm Up 2023
109 Temple Win 13-9 -2.63 28 3.94% Counts Feb 4th Florida Warm Up 2023
4 Texas Win 11-10 19.63 34 3.94% Counts Feb 4th Florida Warm Up 2023
21 Michigan Win 13-10 16.32 2 3.94% Counts Feb 5th Florida Warm Up 2023
11 Pittsburgh Loss 8-13 -12.13 4 3.94% Counts Feb 5th Florida Warm Up 2023
74 California-San Diego Win 13-7 14.35 27 4.96% Counts (Why) Mar 4th Stanford Invite Mens
9 California-Santa Cruz Loss 10-11 5.59 25 4.96% Counts Mar 4th Stanford Invite Mens
119 Southern California Win 13-4 3.76 10 4.96% Counts (Why) Mar 4th Stanford Invite Mens
16 British Columbia Loss 10-11 -0.53 28 4.96% Counts Mar 5th Stanford Invite Mens
56 Colorado State Win 12-7 15.27 64 4.96% Counts (Why) Mar 5th Stanford Invite Mens
32 Victoria Loss 10-12 -14.24 14 4.96% Counts Mar 5th Stanford Invite Mens
3 Brigham Young Loss 10-13 7.38 42 5.57% Counts Mar 17th Centex 2023
6 Colorado Loss 6-11 -12.25 70 5.27% Counts Mar 18th Centex 2023
56 Colorado State Win 12-11 -6.08 64 5.57% Counts Mar 18th Centex 2023
13 Tufts Loss 9-12 -9.06 69 5.57% Counts Mar 18th Centex 2023
12 Carleton College Loss 9-15 -18.63 2 5.57% Counts Mar 19th Centex 2023
96 Texas A&M Win 15-6 12.05 75 5.57% Counts (Why) Mar 19th Centex 2023
33 Oklahoma Christian Loss 11-14 -20.57 162 5.57% Counts Mar 19th Centex 2023
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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.