() #31 Minnesota (8-14)

1713.47 (33)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
8 Brigham Young Loss 7-13 -7.45 13 3.59% Counts Jan 31st Florida Warm Up 2025
47 McGill Win 13-4 17.78 43 3.59% Counts (Why) Jan 31st Florida Warm Up 2025
62 Tulane Win 13-9 6.23 4 3.59% Counts Jan 31st Florida Warm Up 2025
13 Texas Loss 11-13 1.06 15 3.59% Counts Feb 1st Florida Warm Up 2025
28 Pittsburgh Loss 9-13 -13.66 11 3.59% Counts Feb 1st Florida Warm Up 2025
38 Utah State Win 13-12 1.64 44 3.59% Counts Feb 1st Florida Warm Up 2025
44 Emory Win 13-11 4.6 42 3.59% Counts Feb 2nd Florida Warm Up 2025
40 Wisconsin Loss 9-11 -12.45 19 3.59% Counts Feb 2nd Florida Warm Up 2025
21 Georgia Tech Win 13-12 12.6 27 4.52% Counts Mar 1st Smoky Mountain Invite 2025
1 Massachusetts Loss 4-13 -2.57 108 4.52% Counts (Why) Mar 1st Smoky Mountain Invite 2025
13 Texas Loss 5-13 -16.21 15 4.52% Counts (Why) Mar 1st Smoky Mountain Invite 2025
3 North Carolina Loss 10-15 1.85 5 4.52% Counts Mar 1st Smoky Mountain Invite 2025
16 Brown Loss 11-15 -8.28 74 4.52% Counts Mar 2nd Smoky Mountain Invite 2025
21 Georgia Tech Loss 13-15 -3.45 27 4.52% Counts Mar 2nd Smoky Mountain Invite 2025
65 Tennessee Win 15-6 16.09 83 4.52% Counts (Why) Mar 2nd Smoky Mountain Invite 2025
36 Michigan Win 13-5 32.55 13 5.69% Counts (Why) Mar 29th Easterns 2025
3 North Carolina Loss 8-13 -0.21 5 5.69% Counts Mar 29th Easterns 2025
20 Vermont Loss 10-13 -11.08 36 5.69% Counts Mar 29th Easterns 2025
17 Tufts Loss 8-10 -4.64 80 5.54% Counts Mar 29th Easterns 2025
21 Georgia Tech Loss 11-15 -14.48 27 5.69% Counts Mar 30th Easterns 2025
25 Penn State Loss 11-15 -16.13 56 5.69% Counts Mar 30th Easterns 2025
20 Vermont Win 15-14 16.27 36 5.69% Counts Mar 30th Easterns 2025
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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.