(2) #80 Arkansas (8-9)

1276.61 (33)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
76 Texas A&M Loss 12-13 -5.05 30 5.07% Counts Mar 19th Mens College Centex
47 Florida Loss 9-13 -10.29 38 5.07% Counts Mar 19th Mens College Centex
54 Dartmouth Loss 6-11 -18.57 32 4.8% Counts Mar 19th Mens College Centex
125 Texas State Win 10-5 18.22 35 4.51% Counts (Why) Mar 19th Mens College Centex
89 Central Florida Win 8-4 21.71 23 4.03% Counts (Why) Mar 20th Mens College Centex
73 Boston College Loss 9-12 -16.69 65 5.07% Counts Mar 20th Mens College Centex
100 Iowa State Win 10-7 15.2 38 4.8% Counts Mar 20th Mens College Centex
82 Missouri Win 8-5 24.77 23 5.29% Counts (Why) Apr 16th Ozarks D I College Mens CC 2022
131 Saint Louis Win 9-6 11.54 34 5.68% Counts Apr 16th Ozarks D I College Mens CC 2022
- Wichita State University Win 12-6 -0.76 30 6.22% Counts (Why) Apr 16th Ozarks D I College Mens CC 2022
30 Washington University Loss 9-15 -12.45 32 6.39% Counts Apr 17th Ozarks D I College Mens CC 2022
82 Missouri Loss 7-12 -41 23 7.18% Counts Apr 30th South Central D I College Mens Regionals 2022
30 Washington University Loss 11-13 8.07 32 7.18% Counts Apr 30th South Central D I College Mens Regionals 2022
76 Texas A&M Win 13-12 12.02 30 7.18% Counts Apr 30th South Central D I College Mens Regionals 2022
16 Texas Loss 1-15 -5.73 29 7.18% Counts (Why) Apr 30th South Central D I College Mens Regionals 2022
136 Colorado-B Win 15-9 21.07 43 7.18% Counts May 1st South Central D I College Mens Regionals 2022
69 Texas-Dallas Loss 11-15 -23.11 29 7.18% 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.