(2) #113 Southern California (7-13)

1454.03 (269)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
33 California-Santa Cruz Loss 7-15 -5.31 270 3.49% Counts (Why) Jan 27th Santa Barbara Invite 2024
7 Oregon Loss 7-15 9.98 221 3.49% Counts (Why) Jan 27th Santa Barbara Invite 2024
67 Stanford Win 12-9 20.36 267 3.49% Counts Jan 27th Santa Barbara Invite 2024
40 Victoria Loss 7-13 -5.57 272 3.49% Counts Jan 27th Santa Barbara Invite 2024
61 Chicago Loss 10-13 -3.18 307 3.49% Counts Jan 28th Santa Barbara Invite 2024
106 Northwestern Loss 8-13 -16.9 147 3.49% Counts Jan 28th Santa Barbara Invite 2024
71 Grand Canyon Loss 9-11 -3.6 289 4.66% Counts Mar 2nd Stanford Invite 2024
7 Oregon** Loss 2-13 0 221 0% Ignored (Why) Mar 2nd Stanford Invite 2024
43 Tulane Loss 4-12 -10.09 297 4.47% Counts (Why) Mar 2nd Stanford Invite 2024
121 Cal Poly-SLO-B Win 12-10 9.06 367 4.66% Counts Mar 3rd Stanford Invite 2024
144 Santa Clara Win 12-9 11.12 343 4.66% Counts Mar 3rd Stanford Invite 2024
255 Cal State-Long Beach Win 13-5 4.85 201 6.59% Counts (Why) Apr 13th SoCal D I Mens Conferences 2024
60 California-Santa Barbara Loss 7-10 -8.51 246 6.23% Counts Apr 13th SoCal D I Mens Conferences 2024
211 San Diego State Win 12-6 14.08 309 6.41% Counts (Why) Apr 13th SoCal D I Mens Conferences 2024
24 UCLA Loss 7-13 0.64 212 6.59% Counts Apr 13th SoCal D I Mens Conferences 2024
125 California-Irvine Loss 8-10 -22.26 283 6.41% Counts Apr 14th SoCal D I Mens Conferences 2024
192 Loyola Marymount Win 13-7 18.3 46 6.59% Counts (Why) Apr 14th SoCal D I Mens Conferences 2024
121 Cal Poly-SLO-B Loss 9-10 -14.2 367 7.4% Counts Apr 27th Southwest D I College Mens Regionals 2024
24 UCLA Loss 9-15 4.09 212 7.4% Counts Apr 27th Southwest D I College Mens Regionals 2024
158 UCLA-B Win 9-8 -3.01 317 7% 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.