(2) #120 Grand Valley State (9-10)

1080.22 (22)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
68 Colorado College Win 11-10 19.86 37 4.9% Counts Apr 2nd Huck Finn 2022
232 Oklahoma Win 7-4 2.96 11 3.73% Counts (Why) Apr 2nd Huck Finn 2022
129 Boston University Loss 9-10 -7.98 31 4.9% Counts Apr 2nd Huck Finn 2022
133 Wisconsin-Milwaukee Win 8-5 16.75 5 4.05% Counts (Why) Apr 2nd Huck Finn 2022
57 Alabama Loss 7-12 -9.62 28 4.9% Counts Apr 3rd Huck Finn 2022
32 Washington University Loss 7-15 -4.64 20 4.9% Counts (Why) Apr 3rd Huck Finn 2022
64 Northwestern Loss 8-15 -14.23 23 4.9% Counts Apr 3rd Huck Finn 2022
16 Michigan Loss 6-13 6.98 72 5.19% Counts (Why) Apr 9th Michigan D I College Mens CC 2022
108 Michigan State Loss 10-11 -4.14 21 5.19% Counts Apr 9th Michigan D I College Mens CC 2022
194 Western Michigan Win 11-7 8.74 9 5.05% Counts Apr 9th Michigan D I College Mens CC 2022
278 Eastern Michigan Win 11-6 -4.46 3 4.91% Counts (Why) Apr 9th Michigan D I College Mens CC 2022
108 Michigan State Win 15-14 9.54 21 5.19% Counts Apr 10th Michigan D I College Mens CC 2022
194 Western Michigan Win 12-8 7.58 9 5.19% Counts Apr 10th Michigan D I College Mens CC 2022
49 Illinois Loss 11-15 -0.34 19 6.17% Counts Apr 30th Great Lakes D I College Mens Regionals 2022
64 Northwestern Win 15-14 27.2 23 6.17% Counts Apr 30th Great Lakes D I College Mens Regionals 2022
194 Western Michigan Win 12-8 9.11 9 6.17% Counts Apr 30th Great Lakes D I College Mens Regionals 2022
70 Chicago Loss 5-15 -23.19 19 6.17% Counts (Why) May 1st Great Lakes D I College Mens Regionals 2022
91 Kentucky Loss 3-14 -31.72 21 6.17% Counts (Why) May 1st Great Lakes D I College Mens Regionals 2022
52 Indiana Loss 8-13 -8.89 31 6.17% Counts May 1st Great Lakes D I College Mens Regionals 2022
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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.