(2) #29 California (8-14)

1680.52 (18)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
14 UCLA Loss 6-13 -12.08 73 3.97% Counts (Why) Jan 25th Santa Barbara Invite 2020
4 California-San Diego Loss 7-13 -0.02 34 3.97% Counts Jan 25th Santa Barbara Invite 2020
5 Washington Loss 10-12 9.38 52 3.97% Counts Jan 25th Santa Barbara Invite 2020
52 Victoria Win 11-10 -6.1 40 3.97% Counts Jan 25th Santa Barbara Invite 2020
35 Utah Win 12-7 17.14 7 3.97% Counts (Why) Jan 26th Santa Barbara Invite 2020
27 California-Davis Loss 7-11 -16.15 22 3.86% Counts Jan 26th Santa Barbara Invite 2020
14 UCLA Loss 8-9 8.42 73 4.41% Counts Feb 15th Presidents Day Invite 2020
4 California-San Diego Loss 7-11 4.29 34 4.53% Counts Feb 15th Presidents Day Invite 2020
19 Colorado Win 11-9 24.66 22 4.66% Counts Feb 15th Presidents Day Invite 2020
44 Whitman Win 9-8 -1.33 12 4.41% Counts Feb 15th Presidents Day Invite 2020
9 Stanford Loss 7-12 -4.68 105 4.66% Counts Feb 16th Presidents Day Invite 2020
10 California-Santa Barbara Loss 7-13 -9.58 62 4.66% Counts Feb 16th Presidents Day Invite 2020
19 Colorado Win 11-10 18.59 22 4.66% Counts Feb 16th Presidents Day Invite 2020
14 UCLA Loss 8-11 -2.83 73 4.66% Counts Feb 17th Presidents Day Invite 2020
16 Western Washington Loss 8-11 -4.1 1 4.66% Counts Feb 17th Presidents Day Invite 2020
45 Chicago Win 9-8 -1.75 26 4.41% Counts Feb 17th Presidents Day Invite 2020
3 Tufts Loss 6-13 -0.69 286 5.47% Counts (Why) Mar 7th Stanford Invite 2020
17 British Columbia Loss 4-10 -16.34 4.77% Counts (Why) Mar 7th Stanford Invite 2020
60 Oregon Loss 8-9 -25.01 58 5.17% Counts Mar 7th Stanford Invite 2020
39 Cal Poly-SLO Win 11-8 14 32 5.47% Counts Mar 8th Stanford Invite 2020
58 California-Santa Cruz Win 9-6 5.34 91 4.86% Counts Mar 8th Stanford Invite 2020
23 Minnesota Loss 7-8 -1.12 4.86% Counts Mar 8th Stanford Invite 2020
**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.