(2) #138 California-Irvine (8-10)

1300.29 (20)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
120 Cal Poly-SLO-B Loss 8-12 -19.57 19 4.74% Counts Feb 1st Pres Day Quals men
174 California-Santa Cruz-B Win 13-8 17.85 17 4.74% Counts Feb 1st Pres Day Quals men
379 San Diego State-B** Win 13-2 0 16 0% Ignored (Why) Feb 1st Pres Day Quals men
175 California-Davis Win 13-8 17.83 20 4.74% Counts Feb 2nd Pres Day Quals men
85 Southern California Loss 9-10 3.68 20 4.74% Counts Feb 2nd Pres Day Quals men
126 San Jose State Win 10-7 22.76 19 5.03% Counts Feb 15th Vice Presidents Day Invite 2025
65 Grand Canyon Loss 7-9 2.29 21 4.88% Counts Feb 15th Vice Presidents Day Invite 2025
109 San Diego State Loss 8-10 -8.31 18 5.18% Counts Feb 15th Vice Presidents Day Invite 2025
217 Cal Poly-Pomona Win 13-2 16.63 19 5.32% Counts (Why) Feb 16th Vice Presidents Day Invite 2025
109 San Diego State Loss 7-9 -8.66 18 4.88% Counts Feb 16th Vice Presidents Day Invite 2025
85 Southern California Loss 4-12 -21.57 20 5.1% Counts (Why) Feb 16th Vice Presidents Day Invite 2025
6 Cal Poly-SLO Loss 6-13 31.04 31 8.44% Counts (Why) Apr 12th SoCal D I Mens Conferences 2025
333 Cal State-Long Beach** Win 13-2 0 17 0% Ignored (Why) Apr 12th SoCal D I Mens Conferences 2025
109 San Diego State Loss 8-12 -30.49 18 8.44% Counts Apr 12th SoCal D I Mens Conferences 2025
41 California-San Diego Loss 4-13 -11.8 20 8.44% Counts (Why) Apr 12th SoCal D I Mens Conferences 2025
217 Cal Poly-Pomona Win 15-12 -0.32 19 8.44% Counts Apr 13th SoCal D I Mens Conferences 2025
237 Loyola Marymount Win 15-9 11.29 18 8.44% Counts Apr 13th SoCal D I Mens Conferences 2025
109 San Diego State Loss 8-11 -23.52 18 8.44% Counts Apr 13th SoCal D I Mens Conferences 2025
**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.