(7) #57 Stanford (5-14)

1582.25 (8)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
9 Oregon Loss 4-15 -2.18 10 4.61% Counts (Why) Jan 28th Santa Barbara Invitational 2023
18 California Loss 7-9 4.41 37 4.23% Counts Jan 28th Santa Barbara Invitational 2023
54 Northwestern Loss 9-13 -18.57 16 4.61% Counts Jan 28th Santa Barbara Invitational 2023
46 Western Washington Win 9-8 10.53 26 4.36% Counts Jan 28th Santa Barbara Invitational 2023
73 California-Santa Barbara Win 10-5 20.62 11 4.09% Counts (Why) Jan 29th Santa Barbara Invitational 2023
109 Southern California Loss 8-9 -17.46 12 4.36% Counts Jan 29th Santa Barbara Invitational 2023
6 Colorado Loss 8-14 4.59 13 5.48% Counts Feb 18th President’s Day Invite
32 Oregon State Loss 8-10 -2.21 5 5.33% Counts Feb 18th President’s Day Invite
58 California-San Diego Loss 8-10 -14.84 2 5.33% Counts Feb 18th President’s Day Invite
29 Utah State Loss 9-12 -5.18 21 5.48% Counts Feb 19th President’s Day Invite
7 Cal Poly-SLO Loss 3-14 -0.4 14 5.48% Counts (Why) Feb 19th President’s Day Invite
10 California-Santa Cruz Loss 9-10 22.16 1 5.48% Counts Feb 19th President’s Day Invite
73 California-Santa Barbara Win 13-9 19 11 5.48% Counts Feb 20th President’s Day Invite
46 Western Washington Loss 9-13 -18.09 26 5.48% Counts Feb 20th President’s Day Invite
16 British Columbia Loss 6-13 -12.43 14 6.15% Counts (Why) Mar 4th Stanford Invite Mens
47 Colorado State Loss 8-10 -12.58 10 5.98% Counts Mar 4th Stanford Invite Mens
17 Washington Loss 10-12 11.12 38 6.15% Counts Mar 4th Stanford Invite Mens
73 California-Santa Barbara Win 9-8 2.12 11 5.81% Counts Mar 5th Stanford Invite Mens
78 Santa Clara Win 11-9 9.3 30 6.15% Counts Mar 5th Stanford Invite Mens
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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.