(12) #86 San Diego State University (12-5)

1174.11 (18)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
120 Denver Loss 6-7 -22.01 107 5.61% Counts Jan 25th New Year Fest 2020
183 Arizona** Win 12-2 0 25 0% Ignored (Why) Jan 25th New Year Fest 2020
148 Arizona State Win 8-7 -20.24 44 6.03% Counts Jan 25th New Year Fest 2020
256 Arizona-B** Win 13-0 0 0% Ignored (Why) Jan 25th New Year Fest 2020
54 New Mexico Loss 5-9 -18.93 42 5.82% Counts Jan 26th New Year Fest 2020
130 Northern Arizona Win 11-4 19.1 48 6.22% Counts (Why) Jan 26th New Year Fest 2020
157 Humboldt State Win 13-1 3.72 83 7.55% Counts (Why) Feb 8th Stanford Open 2020
175 Lewis & Clark** Win 13-0 0 78 0% Ignored (Why) Feb 8th Stanford Open 2020
58 California-Santa Cruz Loss 4-13 -33.25 91 7.55% Counts (Why) Feb 8th Stanford Open 2020
67 Carleton College Win 11-0 53.98 26 6.92% Counts (Why) Feb 9th Stanford Open 2020
76 Portland Loss 6-7 -3.1 100 6.24% Counts Feb 9th Stanford Open 2020
140 Santa Clara Win 9-3 19.09 232 7.73% Counts (Why) Mar 7th Santa Clara Rage Home Tournament 2020
196 California-B Win 10-5 -26.98 122 8.3% Counts (Why) Mar 7th Santa Clara Rage Home Tournament 2020
124 California-San Diego-B Win 8-5 15.78 116 7.73% Counts (Why) Mar 7th Santa Clara Rage Home Tournament 2020
144 Nevada-Reno Win 9-5 11.45 203 8.02% Counts (Why) Mar 7th Santa Clara Rage Home Tournament 2020
100 Cal State-Long Beach Loss 6-7 -19.61 51 7.73% Counts Mar 8th Santa Clara Rage Home Tournament 2020
124 California-San Diego-B Win 10-6 21.66 116 8.57% Counts (Why) Mar 8th Santa Clara Rage Home Tournament 2020
**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.