(5) #63 Arizona (12-8)

1786.23 (416)

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# Opponent Result Effect % of Ranking Status Date Event
33 UCLA Win 12-9 34.67 5.28% Jan 27th Santa Barbara Invitational 2018
17 California-Santa Barbara Loss 3-13 -3.68 5.28% Jan 27th Santa Barbara Invitational 2018
79 Chicago Loss 10-12 -20.68 5.28% Jan 27th Santa Barbara Invitational 2018
19 Vermont Loss 10-13 8.31 5.28% Jan 27th Santa Barbara Invitational 2018
33 UCLA Win 12-9 34.67 5.28% Jan 28th Santa Barbara Invitational 2018
28 Washington University Loss 4-13 -15.19 5.28% Jan 28th Santa Barbara Invitational 2018
111 California-Irvine Win 12-6 13.04 5.45% Feb 3rd 2018 Presidents Day Qualifying Tournament
120 California-San Diego-B Win 7-5 -3.69 4.45% Feb 3rd 2018 Presidents Day Qualifying Tournament
17 California-Santa Barbara Loss 5-13 -3.91 5.6% Feb 3rd 2018 Presidents Day Qualifying Tournament
73 San Diego State Win 10-7 18.28 5.29% Feb 3rd 2018 Presidents Day Qualifying Tournament
189 Sonoma State** Win 13-4 0 0% Ignored Feb 4th 2018 Presidents Day Qualifying Tournament
35 Cal Poly-SLO Loss 7-9 -1.57 5.13% Feb 4th 2018 Presidents Day Qualifying Tournament
100 Arizona State Win 11-7 10.77 5.45% Feb 4th 2018 Presidents Day Qualifying Tournament
120 California-San Diego-B Loss 5-8 -64.17 6.93% Mar 24th Trouble in Vegas 2018
105 Chico State Win 10-2 23.16 7.32% Mar 24th Trouble in Vegas 2018
73 San Diego State Loss 5-9 -45.87 7.2% Mar 24th Trouble in Vegas 2018
200 Nevada-Reno** Win 13-1 0 0% Ignored Mar 24th Trouble in Vegas 2018
98 Northern Arizona Win 12-2 29.54 8.05% Mar 25th Trouble in Vegas 2018
176 Occidental** Win 11-1 0 0% Ignored Mar 25th Trouble in Vegas 2018
105 Chico State Win 8-7 -14.64 7.45% Mar 25th Trouble in Vegas 2018
**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.