(28) #145 UCLA-B (8-9)

766.28 (90)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
90 Southern California Loss 7-9 5.09 25 6.05% Counts Feb 1st Presidents’ Day Qualifier Women
183 Arizona Win 7-4 9.27 25 5.01% Counts (Why) Feb 1st Presidents’ Day Qualifier Women
171 California-Irvine Win 10-5 20.73 36 5.86% Counts (Why) Feb 1st Presidents’ Day Qualifier Women
125 Chico State Loss 7-9 -8.8 129 6.05% Counts Feb 2nd Presidents’ Day Qualifier Women
148 Arizona State Win 10-5 33.66 44 5.86% Counts (Why) Feb 2nd Presidents’ Day Qualifier Women
100 Cal State-Long Beach Loss 3-9 -17.37 51 5.45% Counts (Why) Feb 2nd Presidents’ Day Qualifier Women
96 Occidental Win 9-8 29.73 32 6.23% Counts Feb 2nd Presidents’ Day Qualifier Women
114 Pacific Lutheran Loss 6-11 -21.99 82 6.57% Counts Feb 8th Stanford Open 2020
163 Sonoma State Win 10-4 25.62 12 6.07% Counts (Why) Feb 8th Stanford Open 2020
67 Carleton College Loss 5-8 4.87 26 5.75% Counts Feb 8th Stanford Open 2020
167 Air Force Win 5-4 -4.55 188 4.78% Counts Feb 9th Stanford Open 2020
157 Humboldt State Loss 4-5 -13.65 83 4.78% Counts Feb 9th Stanford Open 2020
187 Cal Poly SLO-B Win 7-6 -13.4 374 5.75% Counts Feb 9th Stanford Open 2020
125 Chico State Win 6-5 14.95 129 5.29% Counts Feb 9th Stanford Open 2020
161 Claremont Loss 6-7 -21.81 114 6.75% Counts Feb 29th 2nd Annual Claremont Ultimate Classic
- San Diego State University-B Loss 6-8 -9.78 76 7% Counts Feb 29th 2nd Annual Claremont Ultimate Classic
124 California-San Diego-B Loss 4-9 -33.08 116 6.75% Counts (Why) Feb 29th 2nd Annual Claremont Ultimate Classic
**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.