(5) #23 California-Davis (10-12)

1560.72 (26)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
59 California-Santa Cruz Win 11-6 11.9 23 4.38% Counts (Why) Jan 28th Santa Barbara Invitational 2023
50 Wisconsin Win 12-8 11.42 119 4.63% Counts Jan 28th Santa Barbara Invitational 2023
19 California-San Diego Loss 7-11 -17.69 18 4.51% Counts Jan 28th Santa Barbara Invitational 2023
8 Stanford Loss 6-12 -3.76 54 4.51% Counts Jan 29th Santa Barbara Invitational 2023
30 California Win 9-8 2.85 91 4.38% Counts Jan 29th Santa Barbara Invitational 2023
13 Victoria Loss 5-9 -10.65 46 3.98% Counts Jan 29th Santa Barbara Invitational 2023
6 Colorado** Loss 4-10 0 27 0% Ignored (Why) Feb 18th President’s Day Invite
61 Cal Poly-SLO Win 12-4 13.71 44 5.29% Counts (Why) Feb 18th President’s Day Invite
30 California Win 12-5 29.99 91 5.29% Counts (Why) Feb 18th President’s Day Invite
81 Utah Win 11-4 4.4 56 5.06% Counts (Why) Feb 18th President’s Day Invite
95 Southern California** Win 9-3 0 79 0% Ignored (Why) Feb 19th President’s Day Invite
8 Stanford Loss 2-11 -5.34 54 5.06% Counts (Why) Feb 19th President’s Day Invite
18 Colorado State Win 11-8 27.25 208 5.51% Counts Feb 19th President’s Day Invite
12 California-Santa Barbara Loss 4-11 -14.73 27 5.06% Counts (Why) Feb 19th President’s Day Invite
37 Duke Win 11-8 14.74 0 5.51% Counts Feb 20th President’s Day Invite
19 California-San Diego Loss 6-7 -1.56 18 4.56% Counts Feb 20th President’s Day Invite
2 British Columbia** Loss 5-13 9.98 39 6.55% Counts (Why) Mar 11th Stanford Invite Womens
3 Tufts Loss 5-10 8.05 21 5.82% Counts Mar 11th Stanford Invite Womens
35 UCLA Loss 5-6 -12.03 85 4.99% Counts Mar 11th Stanford Invite Womens
47 Santa Clara Loss 4-8 -41.6 38 5.21% Counts Mar 12th Stanford Invite Womens
35 UCLA Loss 4-8 -36.75 85 5.21% Counts Mar 12th Stanford Invite Womens
19 California-San Diego Win 5-4 10.27 18 4.51% Counts Mar 12th Stanford Invite Womens
**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.