() #10 Washington (14-7) NW 3

2044.51 (33)

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# Opponent Result Effect % of Ranking Status Date Event
16 Southern California Loss 12-13 -7.83 3.89% Jan 26th Santa Barbara Invite 2019
219 Michigan State** Win 13-5 0 0% Ignored Jan 26th Santa Barbara Invite 2019
74 Arizona Win 13-8 -2.8 3.89% Jan 26th Santa Barbara Invite 2019
21 California Win 13-6 16.15 3.89% Jan 26th Santa Barbara Invite 2019
50 Stanford Win 12-11 -11.61 3.89% Jan 27th Santa Barbara Invite 2019
5 Cal Poly-SLO Loss 11-13 -5.22 3.89% Jan 27th Santa Barbara Invite 2019
21 California Loss 10-12 -17.78 3.89% Jan 27th Santa Barbara Invite 2019
34 UCLA Win 13-8 7.3 3.89% Jan 27th Santa Barbara Invite 2019
2 Brown Win 12-11 16.97 5.19% Mar 2nd Stanford Invite 2019
7 Carleton College-CUT Loss 11-13 -8.48 5.19% Mar 2nd Stanford Invite 2019
19 Colorado State Loss 9-10 -14.79 5.19% Mar 2nd Stanford Invite 2019
50 Stanford Win 9-6 0.33 4.62% Mar 3rd Stanford Invite 2019
17 Minnesota Win 10-9 1.73 5.19% Mar 3rd Stanford Invite 2019
49 Northwestern Win 13-5 10.58 5.19% Mar 3rd Stanford Invite 2019
59 Oregon State Win 13-7 4.95 6.18% Mar 23rd 2019 NW Challenge Mens Tier 1
30 Victoria Win 13-9 9.21 6.18% Mar 23rd 2019 NW Challenge Mens Tier 1
42 British Columbia Win 13-8 8.25 6.18% Mar 24th 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Loss 9-13 -20.98 6.18% Mar 24th 2019 NW Challenge Mens Tier 1
6 Brigham Young Loss 12-13 -2.29 6.18% Mar 25th 2019 NW Challenge Mens Tier 1
51 Western Washington Win 13-7 9.4 6.18% Mar 25th 2019 NW Challenge Mens Tier 1
58 Whitman Win 9-2 7.28 5.11% Mar 25th 2019 NW Challenge Mens Tier 1
**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.