(3) #66 Texas-Dallas (10-7)

1344.32 (15)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
81 Santa Clara Loss 8-9 -12.3 13 5.18% Counts Mar 5th Stanford Invite 2022
10 California Loss 8-13 0.53 20 5.47% Counts Mar 5th Stanford Invite 2022
47 Florida Loss 8-13 -21.04 26 5.47% Counts Mar 5th Stanford Invite 2022
53 Tulane Win 8-6 19.53 24 4.7% Counts Mar 5th Stanford Invite 2022
63 California-Santa Barbara Loss 8-10 -13 11 5.33% Counts Mar 6th Stanford Invite 2022
55 Grand Canyon Win 10-8 19.12 15 5.33% Counts Mar 6th Stanford Invite 2022
270 North Texas** Win 15-1 0 17 0% Ignored (Why) Apr 16th North Texas D I College Mens CC 2022
308 Texas Christian** Win 15-4 0 18 0% Ignored (Why) Apr 16th North Texas D I College Mens CC 2022
201 Baylor Win 15-3 0.27 14 7.74% Counts (Why) Apr 17th North Texas D I College Mens CC 2022
308 Texas Christian** Win 15-3 0 18 0% Ignored (Why) Apr 17th North Texas D I College Mens CC 2022
40 Colorado State Loss 10-13 -15.49 14 8.69% Counts Apr 30th South Central D I College Mens Regionals 2022
135 Colorado-B Win 13-6 25.78 18 8.69% Counts (Why) Apr 30th South Central D I College Mens Regionals 2022
124 Texas State Win 13-8 21.38 16 8.69% Counts Apr 30th South Central D I College Mens Regionals 2022
32 Washington University Loss 10-14 -14.54 20 8.69% Counts Apr 30th South Central D I College Mens Regionals 2022
78 Missouri Win 11-10 3.2 14 8.69% Counts May 1st South Central D I College Mens Regionals 2022
40 Colorado State Loss 6-15 -41.35 14 8.69% Counts (Why) May 1st South Central D I College Mens Regionals 2022
77 Arkansas Win 15-11 28.02 19 8.69% Counts May 1st South Central D I College Mens Regionals 2022
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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.