(28) #255 Cal State-Long Beach (7-13)

922.79 (201)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
155 Washington-B Loss 8-13 -5.57 435 4.47% Counts Feb 3rd Stanford Open 2024
169 Puget Sound Loss 5-10 -10.13 9 3.98% Counts Feb 3rd Stanford Open 2024
354 California-Santa Cruz-B Win 13-3 7.9 252 4.47% Counts (Why) Feb 3rd Stanford Open 2024
350 Arizona State-B Win 9-4 11.08 209 5.55% Counts (Why) Mar 24th Southwest Showdown 2024
211 San Diego State Win 11-8 37.58 309 6.7% Counts Mar 24th Southwest Showdown 2024
235 Claremont Loss 6-11 -32.12 292 6.34% Counts Mar 24th Southwest Showdown 2024
339 Occidental Win 9-8 -16.21 192 6.34% Counts Mar 24th Southwest Showdown 2024
298 Southern California-B Win 10-7 13.47 293 6.72% Counts Mar 30th 2024 Sinvite
71 Grand Canyon** Loss 2-12 0 289 0% Ignored (Why) Mar 30th 2024 Sinvite
124 San Jose State Loss 3-7 -7.09 266 5.15% Counts (Why) Mar 30th 2024 Sinvite
332 California-San Diego-B Win 14-5 20.52 420 7.1% Counts (Why) Mar 30th 2024 Sinvite
219 Arizona Win 9-7 28.52 280 6.52% Counts Mar 31st 2024 Sinvite
133 Arizona State Loss 6-9 2.27 183 6.31% Counts Mar 31st 2024 Sinvite
71 Grand Canyon** Loss 3-13 0 289 0% Ignored (Why) Mar 31st 2024 Sinvite
113 Southern California Loss 5-13 -5.96 269 7.97% Counts (Why) Apr 13th SoCal D I Mens Conferences 2024
60 California-Santa Barbara** Loss 4-12 0 246 0% Ignored (Why) Apr 13th SoCal D I Mens Conferences 2024
211 San Diego State Loss 6-11 -31.76 309 7.54% Counts Apr 13th SoCal D I Mens Conferences 2024
24 UCLA** Loss 3-13 0 212 0% Ignored (Why) Apr 13th SoCal D I Mens Conferences 2024
192 Loyola Marymount Loss 6-8 -4.95 46 6.84% Counts Apr 14th SoCal D I Mens Conferences 2024
125 California-Irvine Loss 7-13 -7.66 283 7.97% Counts Apr 14th SoCal D I Mens Conferences 2024
**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.