(1) #16 Oregon (9-11) NW 3

2017.73 (30)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
23 California Win 10-9 1.16 69 4.39% Counts Feb 16th Presidents Day Invite 2019
17 Vermont Win 10-8 11.53 11 4.28% Counts Feb 16th Presidents Day Invite 2019
12 Minnesota Win 8-7 7.19 14 3.9% Counts Feb 17th Presidents Day Invite 2019
7 Western Washington Loss 7-8 2.31 27 3.9% Counts Feb 17th Presidents Day Invite 2019
2 California-San Diego Loss 7-12 -5.48 110 4.39% Counts Feb 17th Presidents Day Invite 2019
13 Stanford Win 9-5 22.22 82 3.77% Counts (Why) Feb 18th Presidents Day Invite 2019
14 Colorado Win 11-6 24.96 14 4.16% Counts (Why) Feb 18th Presidents Day Invite 2019
19 UCLA Win 8-7 3.35 14 4.38% Counts Mar 2nd Stanford Invite 2019
50 Whitman Loss 6-7 -26.95 33 4.08% Counts Mar 2nd Stanford Invite 2019
5 Carleton College-Syzygy Loss 5-12 -17.5 29 4.73% Counts (Why) Mar 2nd Stanford Invite 2019
14 Colorado Loss 8-12 -21.37 14 4.93% Counts Mar 2nd Stanford Invite 2019
9 Texas Loss 8-9 0.06 14 4.66% Counts Mar 3rd Stanford Invite 2019
6 British Columbia Loss 9-11 -1.82 270 4.93% Counts Mar 3rd Stanford Invite 2019
32 Brigham Young Win 15-11 4.9 25 6.21% Counts Mar 29th NW Challenge Tier 1 Womens
24 Washington Win 15-10 20.43 4 6.21% Counts Mar 29th NW Challenge Tier 1 Womens
11 Pittsburgh Win 15-12 24.25 37 6.21% Counts Mar 30th NW Challenge Tier 1 Womens
1 North Carolina Loss 7-15 -5.81 118 6.21% Counts (Why) Mar 30th NW Challenge Tier 1 Womens
2 California-San Diego Loss 10-11 18.3 110 6.21% Counts Mar 30th NW Challenge Tier 1 Womens
7 Western Washington Loss 5-14 -27.69 27 6.21% Counts (Why) Mar 31st NW Challenge Tier 1 Womens
13 Stanford Loss 7-13 -34.42 82 6.21% Counts Mar 31st NW Challenge Tier 1 Womens
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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.