(5) #43 California-San Diego (6-14)

1562.26 (47)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
17 Brigham Young Loss 14-15 9.18 39 4.65% Counts Jan 27th Santa Barbara Invite 2024
83 Northwestern Win 15-9 14.09 140 4.65% Counts Jan 27th Santa Barbara Invite 2024
22 Washington Loss 7-15 -16.75 44 4.65% Counts (Why) Jan 27th Santa Barbara Invite 2024
23 UCLA Loss 12-13 5.91 49 4.65% Counts Jan 27th Santa Barbara Invite 2024
39 Victoria Loss 9-12 -15.71 38 4.65% Counts Jan 28th Santa Barbara Invite 2024
47 Oklahoma Christian Loss 11-12 -8.15 30 4.65% Counts Jan 28th Santa Barbara Invite 2024
53 Colorado State Win 14-12 6.31 118 4.65% Counts Jan 28th Santa Barbara Invite 2024
3 Colorado** Loss 5-15 0 35 0% Ignored (Why) Feb 17th Presidents Day Invite 2024
65 Stanford Win 10-7 12.83 80 5.23% Counts Feb 17th Presidents Day Invite 2024
30 Utah Loss 10-12 -7.23 31 5.53% Counts Feb 17th Presidents Day Invite 2024
24 British Columbia Loss 9-10 6.63 42 5.53% Counts Feb 18th Presidents Day Invite 2024
134 California-Irvine Win 14-7 7.6 43 5.53% Counts (Why) Feb 18th Presidents Day Invite 2024
54 California-Santa Barbara Win 12-8 20.41 55 5.53% Counts Feb 18th Presidents Day Invite 2024
24 British Columbia Loss 8-9 6.26 42 5.23% Counts Feb 19th Presidents Day Invite 2024
23 UCLA Loss 4-15 -20.72 49 5.53% Counts (Why) Feb 19th Presidents Day Invite 2024
40 Illinois Loss 10-11 -7.12 18 6.21% Counts Mar 2nd Stanford Invite 2024
33 Wisconsin Loss 11-12 -2.77 14 6.21% Counts Mar 2nd Stanford Invite 2024
44 Tulane Loss 6-7 -7.88 21 5.14% Counts Mar 2nd Stanford Invite 2024
79 Grand Canyon Win 11-4 22.87 111 5.7% Counts (Why) Mar 3rd Stanford Invite 2024
63 Western Washington Loss 8-10 -25.91 46 6.04% Counts Mar 3rd Stanford Invite 2024
**Blowout Eligible. Learn more about how this works here.

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.