(1) #2 Brigham Young (20-1) NW 1

2318.3 (3)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
73 California-Santa Barbara** Win 13-1 0 11 0% Ignored (Why) Jan 27th Santa Barbara Invitational 2023
7 Cal Poly-SLO Win 13-7 22.07 14 5.05% Counts (Why) Jan 27th Santa Barbara Invitational 2023
109 Southern California** Win 15-6 0 12 0% Ignored (Why) Jan 28th Santa Barbara Invitational 2023
44 Victoria Win 14-9 -7.86 112 5.05% Counts Jan 28th Santa Barbara Invitational 2023
50 Case Western Reserve Win 15-7 -4.17 15 5.05% Counts (Why) Jan 28th Santa Barbara Invitational 2023
15 UCLA Win 15-11 4.85 3 5.05% Counts Jan 28th Santa Barbara Invitational 2023
14 Carleton College Win 13-11 -2.24 20 5.35% Counts Feb 3rd Florida Warm Up 2023
72 Auburn Win 13-7 -14.88 54 5.35% Counts (Why) Feb 3rd Florida Warm Up 2023
8 Pittsburgh Win 13-12 -2.16 17 5.35% Counts Feb 3rd Florida Warm Up 2023
67 Virginia Tech** Win 13-4 0 99 0% Ignored (Why) Feb 3rd Florida Warm Up 2023
5 Vermont Win 13-11 6.82 7 5.35% Counts Feb 4th Florida Warm Up 2023
11 Brown Win 13-6 20.16 50 5.35% Counts (Why) Feb 4th Florida Warm Up 2023
104 Florida State** Win 13-4 0 17 0% Ignored (Why) Feb 4th Florida Warm Up 2023
112 Illinois** Win 13-2 0 10 0% Ignored (Why) Feb 4th Florida Warm Up 2023
14 Carleton College Win 13-10 4.89 20 7.57% Counts Mar 17th Centex 2023
23 Wisconsin Win 13-10 -7.84 11 7.57% Counts Mar 17th Centex 2023
4 Texas Loss 12-13 -18.73 1 7.57% Counts Mar 17th Centex 2023
13 Tufts Win 13-10 6.4 37 7.57% Counts Mar 18th Centex 2023
6 Colorado Win 13-11 8.86 13 7.57% Counts Mar 18th Centex 2023
51 Virginia Win 13-6 -6.79 1 7.57% Counts (Why) Mar 18th Centex 2023
26 Georgia Tech Win 13-10 -9.98 1 7.57% Counts Mar 18th Centex 2023
**Blowout Eligible. Learn more about how this works here.

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.