() #4 Brigham Young (22-1) NW 1

2039.12 (20)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
63 California-Santa Barbara** Win 15-5 0 11 0% Ignored (Why) Jan 28th Santa Barbara Invite 2022
6 Cal Poly-SLO Win 13-9 17.81 9 4.65% Counts Jan 28th Santa Barbara Invite 2022
70 Chicago Win 15-9 -9.57 19 4.65% Counts Jan 29th Santa Barbara Invite 2022
81 Santa Clara** Win 15-2 0 13 0% Ignored (Why) Jan 29th Santa Barbara Invite 2022
29 UCLA Win 13-11 -10.39 10 4.65% Counts Jan 29th Santa Barbara Invite 2022
10 California Win 15-13 1.21 20 4.65% Counts Jan 29th Santa Barbara Invite 2022
114 Florida State Win 13-8 -23.09 24 4.93% Counts Feb 5th Warm Up 2022
158 South Florida Win 13-6 -27.04 26 4.93% Counts (Why) Feb 5th Warm Up 2022
5 Pittsburgh Win 13-7 26.83 25 4.93% Counts (Why) Feb 5th Warm Up 2022
106 LSU Win 13-8 -20.79 23 4.93% Counts Feb 5th Warm Up 2022
72 Texas A&M** Win 13-4 0 18 0% Ignored (Why) Feb 5th Warm Up 2022
12 Northeastern Win 13-7 18.55 36 4.93% Counts (Why) Feb 5th Warm Up 2022
1 Brown Loss 11-13 -3.01 23 4.93% Counts Feb 5th Warm Up 2022
69 Virginia Tech Win 13-8 -11.04 33 4.93% Counts Feb 5th Warm Up 2022
208 Nevada-Reno** Win 11-1 0 17 0% Ignored (Why) Mar 12th Big Sky Brawl
45 Utah Win 11-5 3.04 6 6.04% Counts (Why) Mar 12th Big Sky Brawl
166 Northern Arizona** Win 11-2 0 10 0% Ignored (Why) Mar 12th Big Sky Brawl
178 Montana State Win 11-5 -38.58 26 6.04% Counts (Why) Mar 12th Big Sky Brawl
22 Carleton College Win 13-6 17.51 17 6.97% Counts (Why) Mar 18th Missouri Loves Company MLC
13 Minnesota Win 13-6 29.61 22 6.97% Counts (Why) Mar 19th Missouri Loves Company MLC
40 Colorado State Win 13-8 -2.5 14 6.97% Counts Mar 19th Missouri Loves Company MLC
49 Illinois Win 13-5 1.28 19 6.97% Counts (Why) Mar 19th Missouri Loves Company MLC
9 Vermont Win 13-7 31.41 97 6.97% Counts (Why) Mar 19th Missouri Loves Company MLC
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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.