(1) #3 Oregon (20-5) NW 1

2188.99 (34)

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# Opponent Result Effect % of Ranking Status Date Event
121 Puget Sound** Win 15-3 0 0% Ignored Jan 26th Flat Tail Open 2019 Mens
162 Washington State** Win 15-4 0 0% Ignored Jan 26th Flat Tail Open 2019 Mens
326 Western Washington University-B** Win 15-2 0 0% Ignored Jan 26th Flat Tail Open 2019 Mens
59 Oregon State Win 15-8 -2.22 3.45% Jan 27th Flat Tail Open 2019 Mens
58 Whitman Win 15-8 -1.59 3.45% Jan 27th Flat Tail Open 2019 Mens
100 California-Santa Cruz** Win 13-3 0 0% Ignored Feb 16th Presidents Day Invite 2019
34 UCLA Win 13-9 -1.78 4.1% Feb 16th Presidents Day Invite 2019
56 California-San Diego Win 12-4 0.15 3.94% Feb 17th Presidents Day Invite 2019
51 Western Washington Win 13-4 1.74 4.1% Feb 17th Presidents Day Invite 2019
16 Southern California Win 10-8 2.07 3.99% Feb 18th Presidents Day Invite 2019
5 Cal Poly-SLO Loss 9-10 -7.25 4.1% Feb 18th Presidents Day Invite 2019
6 Brigham Young Loss 14-15 -8.65 4.61% Mar 2nd Stanford Invite 2019
21 California Win 13-7 10.24 4.61% Mar 2nd Stanford Invite 2019
49 Northwestern Win 13-8 -2.66 4.61% Mar 2nd Stanford Invite 2019
14 Ohio State Win 13-11 1.54 4.61% Mar 2nd Stanford Invite 2019
5 Cal Poly-SLO Loss 12-13 -8.18 4.61% Mar 3rd Stanford Invite 2019
8 Colorado Loss 10-11 -10.55 4.61% Mar 3rd Stanford Invite 2019
14 Ohio State Win 13-6 19.46 4.61% Mar 3rd Stanford Invite 2019
17 Minnesota Win 13-5 22.3 5.8% Mar 30th Easterns 2019 Men
45 California-Santa Barbara Win 13-8 -1.82 5.8% Mar 30th Easterns 2019 Men
9 Massachusetts Win 15-13 5.59 5.8% Mar 30th Easterns 2019 Men
44 Virginia Win 13-8 -1.32 5.8% Mar 30th Easterns 2019 Men
4 Pittsburgh Win 14-13 7.45 5.8% Mar 31st Easterns 2019 Men
7 Carleton College-CUT Loss 12-15 -22.84 5.8% Mar 31st Easterns 2019 Men
20 Tufts Win 15-12 -1.5 5.8% Mar 31st Easterns 2019 Men
**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.