(6) #158 UCLA-B (13-6)

1289.07 (317)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
332 California-San Diego-B Win 10-7 -11.85 420 3.7% Counts Jan 20th Pres Day Quals
144 Santa Clara Win 12-9 15.98 343 3.91% Counts Jan 20th Pres Day Quals
230 California-Davis Win 13-9 5.9 167 3.91% Counts Jan 21st Pres Day Quals
211 San Diego State Win 11-6 12.98 309 3.7% Counts (Why) Jan 21st Pres Day Quals
192 Loyola Marymount Loss 7-10 -20.09 46 3.7% Counts Jan 21st Pres Day Quals
334 California-Santa Barbara-B** Win 13-5 0 179 0% Ignored (Why) Mar 9th Silicon Valley Rally 2024
354 California-Santa Cruz-B** Win 13-2 0 252 0% Ignored (Why) Mar 9th Silicon Valley Rally 2024
344 Chico State Win 12-7 -14.22 222 5.86% Counts (Why) Mar 9th Silicon Valley Rally 2024
221 California-B Win 13-5 22.4 222 5.86% Counts (Why) Mar 10th Silicon Valley Rally 2024
334 California-Santa Barbara-B Win 13-9 -18 179 5.86% Counts Mar 10th Silicon Valley Rally 2024
124 San Jose State Loss 6-13 -30.92 266 5.86% Counts (Why) Mar 10th Silicon Valley Rally 2024
- Arizona -B** Win 15-5 0 0% Ignored (Why) Apr 14th Southwest Dev Mens Conferences 2024
121 Cal Poly-SLO-B Loss 6-15 -41.4 367 7.82% Counts (Why) Apr 14th Southwest Dev Mens Conferences 2024
221 California-B Win 15-6 30.54 222 7.82% Counts (Why) Apr 14th Southwest Dev Mens Conferences 2024
298 Southern California-B Win 14-4 2.64 293 7.82% Counts (Why) Apr 14th Southwest Dev Mens Conferences 2024
125 California-Irvine Loss 7-10 -25.98 283 8.31% Counts Apr 27th Southwest D I College Mens Regionals 2024
33 California-Santa Cruz Loss 9-12 26.25 270 8.78% Counts Apr 27th Southwest D I College Mens Regionals 2024
133 Arizona State Win 11-8 43.47 183 8.78% Counts Apr 28th Southwest D I College Mens Regionals 2024
113 Southern California Loss 8-9 3.62 269 8.31% Counts Apr 28th Southwest D I College Mens Regionals 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.