(4) #47 Santa Clara (12-9)

1368.16 (38)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
125 San Diego State** Win 11-1 0 10 0% Ignored (Why) Feb 4th Stanford Open
59 California-Santa Cruz Win 11-5 33.12 23 6.15% Counts (Why) Feb 4th Stanford Open
102 Claremont Win 6-4 -5.69 7 4.86% Counts (Why) Feb 4th Stanford Open
95 Southern California Loss 6-7 -33.1 79 5.54% Counts Feb 4th Stanford Open
34 Portland Loss 7-8 -1.91 77 5.95% Counts Feb 5th Stanford Open
155 Stanford-B** Win 12-1 0 40 0% Ignored (Why) Feb 5th Stanford Open
26 Carleton College-Eclipse Loss 6-8 -7.11 35 5.75% Counts Feb 5th Stanford Open
34 Portland Loss 5-9 -26.5 77 5.75% Counts Feb 5th Stanford Open
102 Claremont Win 9-4 8.16 7 6.22% Counts (Why) Feb 18th Santa Clara Rage Tournament
116 Occidental Win 11-5 0.95 13 6.9% Counts (Why) Feb 18th Santa Clara Rage Tournament
178 Pacific Lutheran** Win 13-3 0 162 0% Ignored (Why) Feb 18th Santa Clara Rage Tournament
93 California-Irvine Win 10-7 -3.15 11 7.11% Counts Feb 18th Santa Clara Rage Tournament
155 Stanford-B** Win 11-2 0 40 0% Ignored (Why) Feb 18th Santa Clara Rage Tournament
157 UCLA-B** Win 12-0 0 28 0% Ignored (Why) Feb 19th Santa Clara Rage Tournament
59 California-Santa Cruz Win 7-6 2.02 23 6.22% Counts Feb 19th Santa Clara Rage Tournament
11 Oregon Loss 2-12 -0.63 60 8.58% Counts (Why) Mar 11th Stanford Invite Womens
6 Colorado** Loss 3-13 0 27 0% Ignored (Why) Mar 11th Stanford Invite Womens
19 California-San Diego Loss 5-9 -20.3 18 7.68% Counts Mar 11th Stanford Invite Womens
23 California-Davis Win 8-4 57.96 26 7.11% Counts (Why) Mar 12th Stanford Invite Womens
19 California-San Diego Loss 7-8 13.81 18 7.95% Counts Mar 12th Stanford Invite Womens
35 UCLA Loss 7-9 -17.08 85 8.21% Counts Mar 12th Stanford Invite Womens
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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.