(1) #18 California-San Diego (12-10) SW 3

1697.49 (44)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
24 California-Davis Win 11-7 17.43 33 4.59% Counts Jan 28th Santa Barbara Invitational 2023
43 Wisconsin Win 9-8 -9.24 20 4.46% Counts Jan 28th Santa Barbara Invitational 2023
7 Carleton College Loss 3-15 -7.19 87 4.71% Counts (Why) Jan 29th Santa Barbara Invitational 2023
71 Utah Win 10-7 -9.88 53 4.46% Counts Jan 29th Santa Barbara Invitational 2023
12 California-Santa Barbara Loss 5-10 -14.8 49 4.19% Counts Jan 29th Santa Barbara Invitational 2023
85 Southern California** Win 11-4 0 28 0% Ignored (Why) Feb 18th President’s Day Invite
25 Duke Win 11-7 19.51 121 5.45% Counts Feb 18th President’s Day Invite
12 California-Santa Barbara Loss 4-11 -19.77 49 5.14% Counts (Why) Feb 18th President’s Day Invite
49 Texas Win 11-7 5.71 40 5.45% Counts Feb 18th President’s Day Invite
3 Colorado** Loss 3-14 0 151 0% Ignored (Why) Feb 19th President’s Day Invite
30 California Win 10-6 17.92 34 5.14% Counts (Why) Feb 19th President’s Day Invite
29 UCLA Win 11-8 12.17 80 5.6% Counts Feb 19th President’s Day Invite
11 Oregon Loss 9-13 -8.68 8 5.6% Counts Feb 19th President’s Day Invite
23 Carleton College-Eclipse Win 10-8 9.82 53 5.45% Counts Feb 20th President’s Day Invite
24 California-Davis Win 7-6 1.01 33 4.63% Counts Feb 20th President’s Day Invite
41 Santa Clara Win 9-5 14.45 39 5.72% Counts (Why) Mar 11th Stanford Invite Womens
3 Colorado** Loss 5-12 0 151 0% Ignored (Why) Mar 11th Stanford Invite Womens
11 Oregon Loss 8-13 -15.98 8 6.66% Counts Mar 11th Stanford Invite Womens
4 Tufts Loss 5-10 1.82 35 5.92% Counts Mar 11th Stanford Invite Womens
41 Santa Clara Win 8-7 -10.44 39 5.92% Counts Mar 12th Stanford Invite Womens
24 California-Davis Loss 4-5 -11.02 33 4.59% Counts Mar 12th Stanford Invite Womens
12 California-Santa Barbara Loss 8-9 7.42 49 6.3% Counts Mar 12th Stanford Invite Womens
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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.