(12) #54 California-Santa Barbara (9-12)

1469.64 (55)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
17 Brigham Young Loss 10-13 3.39 39 4.18% Counts Jan 26th Santa Barbara Invite 2024
151 Cal Poly-SLO-B Win 15-6 7.17 45 4.18% Counts (Why) Jan 27th Santa Barbara Invite 2024
15 California Loss 6-15 -6.35 35 4.18% Counts (Why) Jan 27th Santa Barbara Invite 2024
30 Utah Loss 9-12 -6.03 31 4.18% Counts Jan 27th Santa Barbara Invite 2024
53 Colorado State Loss 7-15 -26.16 118 4.18% Counts (Why) Jan 27th Santa Barbara Invite 2024
67 Chicago Win 13-10 10.72 40 4.18% Counts Jan 28th Santa Barbara Invite 2024
83 Northwestern Win 14-11 7.82 140 4.18% Counts Jan 28th Santa Barbara Invite 2024
6 Oregon Loss 8-12 10.39 35 4.97% Counts Feb 17th Presidents Day Invite 2024
23 UCLA Loss 8-11 -1.4 49 4.97% Counts Feb 17th Presidents Day Invite 2024
39 Victoria Win 11-9 19.12 38 4.97% Counts Feb 17th Presidents Day Invite 2024
24 British Columbia Loss 7-9 2.47 42 4.57% Counts Feb 18th Presidents Day Invite 2024
134 California-Irvine Win 12-7 8.37 43 4.97% Counts (Why) Feb 18th Presidents Day Invite 2024
43 California-San Diego Loss 8-12 -18.25 47 4.97% Counts Feb 18th Presidents Day Invite 2024
35 California-Santa Cruz Win 11-10 15.32 50 4.97% Counts Feb 19th Presidents Day Invite 2024
65 Stanford Loss 7-9 -16.46 80 4.57% Counts Feb 19th Presidents Day Invite 2024
117 Vanderbilt Win 12-5 17.57 62 5.36% Counts (Why) Mar 2nd Stanford Invite 2024
160 Santa Clara Win 13-6 7.3 33 5.58% Counts (Why) Mar 2nd Stanford Invite 2024
35 California-Santa Cruz Loss 9-10 2.52 50 5.58% Counts Mar 2nd Stanford Invite 2024
65 Stanford Loss 5-10 -33.34 80 4.96% Counts Mar 2nd Stanford Invite 2024
63 Western Washington Loss 6-10 -29.36 46 5.12% Counts Mar 3rd Stanford Invite 2024
79 Grand Canyon Win 11-4 25.44 111 5.12% Counts (Why) 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.