() #15 California-San Diego (10-11) SW 4

2162.55 (18)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
1 British Columbia** Loss 4-15 0 24 0% Ignored (Why) Jan 27th Santa Barbara Invite 2024
30 California Loss 11-12 -20.16 19 4.76% Counts Jan 27th Santa Barbara Invite 2024
27 Utah Loss 9-12 -29.31 10 4.76% Counts Jan 27th Santa Barbara Invite 2024
23 Cal Poly-SLO Loss 7-11 -32 6 4.64% Counts Jan 28th Santa Barbara Invite 2024
82 Northwestern** Win 15-5 0 189 0% Ignored (Why) Jan 28th Santa Barbara Invite 2024
24 California-Davis Win 12-9 9.24 14 5.66% Counts Feb 17th Presidents Day Invite 2024
5 Oregon Loss 9-14 -1.84 28 5.66% Counts Feb 17th Presidents Day Invite 2024
113 Denver** Win 15-2 0 18 0% Ignored (Why) Feb 17th Presidents Day Invite 2024
6 Stanford Loss 6-12 -10.69 23 5.51% Counts Feb 18th Presidents Day Invite 2024
13 Western Washington Win 13-12 10.46 40 5.66% Counts Feb 18th Presidents Day Invite 2024
9 California-Santa Barbara Win 10-9 23.07 18 5.66% Counts Feb 18th Presidents Day Invite 2024
27 Utah Win 12-4 20.66 10 5.44% Counts (Why) Feb 18th Presidents Day Invite 2024
5 Oregon Loss 4-15 -9.42 28 5.66% Counts (Why) Feb 19th Presidents Day Invite 2024
9 California-Santa Barbara Loss 8-12 -10.92 18 5.66% Counts Feb 19th Presidents Day Invite 2024
1 British Columbia Loss 6-11 11.83 24 6.01% Counts Mar 2nd Stanford Invite 2024
30 California Win 7-4 11.09 19 4.84% Counts (Why) Mar 2nd Stanford Invite 2024
32 UCLA Win 12-4 18.8 2 6.1% Counts (Why) Mar 2nd Stanford Invite 2024
46 Texas Win 10-2 6.73 2 5.55% Counts (Why) Mar 2nd Stanford Invite 2024
2 Vermont Loss 7-10 15.19 50 6.01% Counts Mar 3rd Stanford Invite 2024
14 California-Santa Cruz Loss 8-9 -6.87 21 6.01% Counts Mar 3rd Stanford Invite 2024
25 Pittsburgh Win 10-9 -5.07 9 6.36% Counts Mar 3rd Stanford Invite 2024
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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.