(8) #202 California-B (12-7)

826.52 (9)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
133 Loyola Marymount Loss 4-11 -13.16 17 3.99% Counts (Why) Jan 20th Pres Day Quals
329 California-San Diego-C Win 13-7 -4.27 50 4.35% Counts (Why) Jan 20th Pres Day Quals
329 California-San Diego-C** Win 12-5 0 50 0% Ignored (Why) Jan 21st Pres Day Quals
330 California-San Diego-B Win 11-6 -4.65 161 4.12% Counts (Why) Jan 21st Pres Day Quals
349 Cal Poly-Humboldt Win 11-6 -12.24 14 4.62% Counts (Why) Feb 3rd Stanford Open 2024
151 Cal Poly-SLO-B Loss 9-11 -2.15 45 4.88% Counts Feb 3rd Stanford Open 2024
297 Occidental Win 10-5 5.14 15 4.34% Counts (Why) Feb 3rd Stanford Open 2024
349 Cal Poly-Humboldt** Win 13-4 0 14 0% Ignored (Why) Mar 9th Silicon Valley Rally 2024
194 California-Davis Win 9-8 9.63 13 6.17% Counts Mar 9th Silicon Valley Rally 2024
129 San Jose State Loss 8-10 2.53 73 6.35% Counts Mar 9th Silicon Valley Rally 2024
164 UCLA-B Loss 5-13 -31.73 34 6.52% Counts (Why) Mar 10th Silicon Valley Rally 2024
292 California-Santa Barbara-B Win 11-7 2.92 103 6.35% Counts Mar 10th Silicon Valley Rally 2024
129 San Jose State Win 9-6 44.18 73 5.79% Counts Mar 10th Silicon Valley Rally 2024
211 Arizona Loss 8-9 -14.15 127 7.33% Counts Mar 30th 2024 Sinvite
110 Arizona State Loss 4-13 -19.66 94 7.75% Counts (Why) Mar 30th 2024 Sinvite
234 Claremont Win 9-6 21.84 9 6.89% Counts Mar 30th 2024 Sinvite
292 California-Santa Barbara-B Win 9-6 -0.39 103 6.89% Counts Mar 30th 2024 Sinvite
211 Arizona Win 7-4 27.74 127 5.9% Counts (Why) Mar 31st 2024 Sinvite
129 San Jose State Loss 8-12 -11.87 73 7.75% Counts Mar 31st 2024 Sinvite
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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.