(5) #108 Southern California (8-13)

1072.45 (23)

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# Opponent Result Effect % of Ranking Status Date Event
32 Brigham Young** Loss 4-13 0 0% Ignored Jan 26th Santa Barbara Invite 2019
24 Washington** Loss 5-13 0 0% Ignored Jan 26th Santa Barbara Invite 2019
48 California-Santa Cruz Loss 6-13 -9.42 5.85% Jan 26th Santa Barbara Invite 2019
90 Colorado State Loss 7-11 -19.46 5.7% Jan 27th Santa Barbara Invite 2019
84 Victoria Loss 5-10 -21.99 5.2% Jan 27th Santa Barbara Invite 2019
14 Colorado** Loss 2-13 0 0% Ignored Feb 16th Presidents Day Invite 2019
12 Minnesota** Loss 0-12 0 0% Ignored Feb 16th Presidents Day Invite 2019
29 Northwestern** Loss 2-7 0 0% Ignored Feb 17th Presidents Day Invite 2019
23 California Loss 4-7 19.53 5.29% Feb 17th Presidents Day Invite 2019
86 San Diego State Loss 2-7 -22.87 5.05% Feb 17th Presidents Day Invite 2019
136 Occidental Win 8-5 22.05 6.46% Mar 2nd 2019 Claremont Ultimate Classic
158 Claremont Win 13-4 30.03 7.81% Mar 2nd 2019 Claremont Ultimate Classic
158 Claremont Win 11-9 0.31 7.81% Mar 2nd 2019 Claremont Ultimate Classic
237 North Texas** Win 10-2 0 0% Ignored Mar 23rd Womens College Centex 2019
173 Baylor Win 12-5 24.45 8.91% Mar 23rd Womens College Centex 2019
278 Texas-B** Win 13-0 0 0% Ignored Mar 23rd Womens College Centex 2019
80 St Olaf Win 10-9 33.13 9.29% Mar 23rd Womens College Centex 2019
99 MIT Loss 7-9 -20.9 8.52% Mar 24th Womens College Centex 2019
132 Boston University Win 8-7 2.89 8.25% Mar 24th Womens College Centex 2019
59 Duke Loss 7-10 -1.56 8.79% Mar 24th Womens College Centex 2019
102 LSU Loss 4-7 -34.16 7.07% Mar 24th Womens College Centex 2019
**Blowout Eligible

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.