(8) #75 Nevada-Reno (13-7)

1360.69 (21)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
192 Montana Win 15-13 -13.43 47 4.57% Counts Jan 25th Pacific Confrontational Invite 2020
19 Oregon State Loss 11-15 4.13 5 4.57% Counts Jan 25th Pacific Confrontational Invite 2020
168 Lewis & Clark Win 15-7 9.8 15 4.57% Counts (Why) Jan 25th Pacific Confrontational Invite 2020
85 Humboldt State Loss 11-12 -8.54 10 4.57% Counts Jan 25th Pacific Confrontational Invite 2020
192 Montana Win 15-10 -1.96 47 4.57% Counts Jan 26th Pacific Confrontational Invite 2020
142 Washington-B Win 15-6 13.3 34 4.57% Counts (Why) Jan 26th Pacific Confrontational Invite 2020
76 Puget Sound Loss 8-9 -6.97 190 4.81% Counts Feb 8th Stanford Open 2020
54 California-Davis Win 11-9 19.64 11 5.09% Counts Feb 8th Stanford Open 2020
148 Sonoma State Win 13-7 10.96 10 5.09% Counts (Why) Feb 8th Stanford Open 2020
27 Western Washington Loss 7-10 -1.58 13 4.81% Counts Feb 8th Stanford Open 2020
89 Carleton College-GoP Win 6-5 1.72 36 3.87% Counts Feb 9th Stanford Open 2020
36 California-Santa Cruz Loss 7-10 -6.14 2 4.81% Counts Feb 9th Stanford Open 2020
126 Chico State Loss 1-9 -36.81 6 4.21% Counts (Why) Feb 9th Stanford Open 2020
192 Montana Win 11-6 3.12 47 5.64% Counts (Why) Feb 29th Big Sky Brawl 2020
59 Whitman Win 11-10 12.97 3 5.97% Counts Feb 29th Big Sky Brawl 2020
149 Brigham Young-B Win 11-8 0.78 36 5.97% Counts Feb 29th Big Sky Brawl 2020
109 Washington State Win 11-9 4.29 89 5.97% Counts Feb 29th Big Sky Brawl 2020
298 Western Washington-B Win 11-6 -27.86 150 5.64% Counts (Why) Feb 29th Big Sky Brawl 2020
74 Montana State Win 7-5 16.4 49 4.74% Counts Mar 1st Big Sky Brawl 2020
44 Utah State Loss 11-12 6.53 33 5.97% Counts Mar 1st Big Sky Brawl 2020
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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.