() #16 British Columbia (14-6) NW 3

1992.55 (14)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
42 Grand Canyon Win 15-4 12.61 4 3.88% Counts (Why) Jan 28th Santa Barbara Invitational 2023
53 Utah Win 11-6 6.63 22 3.67% Counts (Why) Jan 28th Santa Barbara Invitational 2023
73 California-Santa Barbara Win 14-2 4 11 3.88% Counts (Why) Jan 28th Santa Barbara Invitational 2023
7 Cal Poly-SLO Loss 10-11 2.33 14 3.88% Counts Jan 28th Santa Barbara Invitational 2023
54 Northwestern Win 11-5 8.25 16 3.56% Counts (Why) Jan 29th Santa Barbara Invitational 2023
15 UCLA Loss 7-8 -3.18 3 3.44% Counts Jan 29th Santa Barbara Invitational 2023
44 Victoria Loss 7-10 -26.09 112 3.67% Counts Jan 29th Santa Barbara Invitational 2023
17 Washington Win 11-9 9.95 38 3.88% Counts Jan 29th Santa Barbara Invitational 2023
57 Stanford Win 13-6 10.35 8 5.17% Counts (Why) Mar 4th Stanford Invite Mens
47 Colorado State Win 13-9 4 10 5.17% Counts Mar 4th Stanford Invite Mens
17 Washington Loss 10-13 -18.04 38 5.17% Counts Mar 4th Stanford Invite Mens
32 Oregon State Loss 10-11 -17.01 5 5.17% Counts Mar 5th Stanford Invite Mens
29 Utah State Win 12-11 -1.6 21 5.17% Counts Mar 5th Stanford Invite Mens
23 Wisconsin Win 11-10 1.47 11 5.17% Counts Mar 5th Stanford Invite Mens
86 Dartmouth Win 15-5 3.1 64 6.52% Counts (Why) Apr 1st Northwest Challenge Mens
81 Whitman Win 15-7 4.99 98 6.52% Counts (Why) Apr 1st Northwest Challenge Mens
29 Utah State Loss 8-11 -36.26 21 6.52% Counts Apr 1st Northwest Challenge Mens
32 Oregon State Win 15-9 22.92 5 6.52% Counts Apr 2nd Northwest Challenge Mens
46 Western Washington Win 12-9 2.88 26 6.52% Counts Apr 2nd Northwest Challenge Mens
53 Utah Win 13-8 8.62 22 6.52% Counts Apr 2nd Northwest Challenge 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.