(3) #108 California-San Diego-B (9-10)

950.19 (141)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
24 Carleton College-Eclipse Loss 5-9 15.77 141 5.85% Counts Feb 4th Stanford Open
178 Chico State Win 9-1 0.06 146 5.64% Counts (Why) Feb 4th Stanford Open
- Humboldt State Win 7-5 -17.16 144 5.42% Counts Feb 4th Stanford Open
79 Nevada-Reno Loss 4-5 4.95 142 4.69% Counts Feb 4th Stanford Open
50 California-Santa Cruz Loss 5-8 2.02 142 5.64% Counts Feb 5th Stanford Open
53 Cal Poly-SLO Loss 2-8 -9.53 142 5.31% Counts (Why) Feb 5th Stanford Open
79 Nevada-Reno Win 4-3 15.14 142 4.14% Counts (Why) Feb 5th Stanford Open
50 California-Santa Cruz Loss 4-10 -8.07 142 6.69% Counts (Why) Feb 18th Santa Clara Rage Tournament
178 Chico State Win 9-3 0.07 146 6.33% Counts (Why) Feb 18th Santa Clara Rage Tournament
197 California-B** Win 9-2 0 153 0% Ignored (Why) Feb 18th Santa Clara Rage Tournament
196 California-Davis-B** Win 11-4 0 178 0% Ignored (Why) Feb 18th Santa Clara Rage Tournament
90 Claremont Loss 4-6 -14.8 140 5.55% Counts Feb 19th Santa Clara Rage Tournament
150 Arizona State Win 8-3 16.25 143 5.96% Counts (Why) Feb 19th Santa Clara Rage Tournament
101 Occidental Win 8-3 39.62 132 5.96% Counts (Why) Feb 19th Santa Clara Rage Tournament
79 Nevada-Reno Loss 4-7 -16.74 142 5.82% Counts Feb 19th Santa Clara Rage Tournament
90 Claremont Loss 4-6 -16.74 140 6.23% Counts Mar 5th Claremont Classic
84 California-Irvine Loss 5-7 -12.05 140 6.83% Counts Mar 5th Claremont Classic
162 UCLA-B Win 8-4 8.45 140 6.83% Counts (Why) Mar 5th Claremont Classic
101 Occidental Loss 6-7 -7.6 132 7.11% Counts Mar 5th Claremont Classic
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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.