(1) #30 Utah State (10-10)

1584.74 (32)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
136 Arizona State Win 13-6 0.61 32 4.38% Counts (Why) Jan 28th Santa Barbara Invitational 2023
74 California-San Diego Win 13-6 15.06 27 4.38% Counts (Why) Jan 28th Santa Barbara Invitational 2023
9 California-Santa Cruz Loss 11-13 0.66 25 4.38% Counts Jan 28th Santa Barbara Invitational 2023
19 Washington Win 14-12 14.28 34 4.38% Counts Jan 28th Santa Barbara Invitational 2023
63 Utah Win 5-3 5.24 20 2.85% Counts (Why) Jan 29th Santa Barbara Invitational 2023
71 Northwestern Win 13-12 -6.33 99 4.38% Counts Jan 29th Santa Barbara Invitational 2023
32 Victoria Loss 11-12 -6.8 14 4.38% Counts Jan 29th Santa Barbara Invitational 2023
14 UCLA Loss 10-12 -3.26 17 5.21% Counts Feb 18th President’s Day Invite
24 California Loss 9-12 -16.71 33 5.21% Counts Feb 18th President’s Day Invite
47 Western Washington Win 11-8 11.13 38 5.21% Counts Feb 18th President’s Day Invite
76 Stanford Win 12-9 3.69 37 5.21% Counts Feb 19th President’s Day Invite
8 Cal Poly-SLO Loss 9-10 8.63 29 5.21% Counts Feb 19th President’s Day Invite
9 California-Santa Cruz Loss 8-12 -10.89 25 5.21% Counts Feb 19th President’s Day Invite
56 Colorado State Win 10-8 2.44 64 5.08% Counts Feb 20th President’s Day Invite
24 California Loss 10-13 -15.76 33 5.21% Counts Feb 20th President’s Day Invite
7 Oregon Loss 8-13 -11.16 30 5.85% Counts Mar 4th Stanford Invite Mens
32 Victoria Win 13-10 18.95 14 5.85% Counts Mar 4th Stanford Invite Mens
91 Santa Clara Win 13-4 14.53 26 5.85% Counts (Why) Mar 4th Stanford Invite Mens
16 British Columbia Loss 11-12 0.06 28 5.85% Counts Mar 5th Stanford Invite Mens
74 California-San Diego Loss 10-11 -24.66 27 5.85% Counts Mar 5th Stanford Invite Mens
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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.