(31) #354 California-Santa Cruz-B (6-12)

491.33 (252)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
121 Cal Poly-SLO-B** Loss 0-13 0 367 0% Ignored (Why) Jan 20th Pres Day Quals
352 Cal Poly-SLO-C Loss 7-9 -13.59 239 4.76% Counts Jan 20th Pres Day Quals
125 California-Irvine** Loss 4-13 0 283 0% Ignored (Why) Jan 20th Pres Day Quals
298 Southern California-B Win 8-7 17.1 293 4.61% Counts Jan 20th Pres Day Quals
332 California-San Diego-B Loss 9-10 -1.38 420 5.19% Counts Jan 21st Pres Day Quals
396 California-San Diego-C Win 11-9 -6.84 58 5.19% Counts Jan 21st Pres Day Quals
255 Cal State-Long Beach Loss 3-13 -10.42 201 5.82% Counts (Why) Feb 3rd Stanford Open 2024
155 Washington-B Loss 6-11 15.27 435 5.51% Counts Feb 3rd Stanford Open 2024
169 Puget Sound** Loss 3-13 0 9 0% Ignored (Why) Feb 3rd Stanford Open 2024
334 California-Santa Barbara-B Loss 8-11 -23.23 179 7.77% Counts Mar 9th Silicon Valley Rally 2024
158 UCLA-B** Loss 2-13 0 317 0% Ignored (Why) Mar 9th Silicon Valley Rally 2024
344 Chico State Loss 7-11 -34.22 222 7.57% Counts Mar 9th Silicon Valley Rally 2024
338 Cal Poly-Humboldt Win 13-4 56.66 536 7.77% Counts (Why) Mar 10th Silicon Valley Rally 2024
230 California-Davis Loss 4-12 -6.12 167 7.46% Counts (Why) Mar 10th Silicon Valley Rally 2024
344 Chico State Win 10-9 14.65 222 7.77% Counts Mar 10th Silicon Valley Rally 2024
332 California-San Diego-B Loss 7-15 -57.92 420 10.38% Counts (Why) Apr 14th Southwest Dev Mens Conferences 2024
396 California-San Diego-C Win 15-5 26.14 58 10.38% Counts (Why) Apr 14th Southwest Dev Mens Conferences 2024
334 California-Santa Barbara-B Win 9-8 23.41 179 9.82% Counts Apr 14th Southwest Dev Mens Conferences 2024
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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.