(4) #156 South Florida (5-17)

943.36 (24)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
4 Brigham Young Loss 6-13 19.47 5 3.64% Counts (Why) Feb 5th Warm Up 2022
8 Massachusetts Loss 6-13 16.12 82 3.64% Counts (Why) Feb 5th Warm Up 2022
26 Auburn Loss 6-13 4.44 24 3.64% Counts (Why) Feb 5th Warm Up 2022
5 Pittsburgh** Loss 5-13 0 1 0% Ignored (Why) Feb 5th Warm Up 2022
110 Georgia State Loss 10-12 -1.72 33 3.64% Counts Feb 5th Warm Up 2022
64 Temple Win 10-8 25.94 19 3.54% Counts Feb 5th Warm Up 2022
68 Virginia Tech Loss 8-13 -2.88 36 3.64% Counts Feb 5th Warm Up 2022
76 Texas A&M Loss 7-13 -7.31 30 3.64% Counts Feb 5th Warm Up 2022
60 Notre Dame Loss 4-13 -5.87 25 4.86% Counts (Why) Mar 12th Tally Classic XVI
88 Harvard Loss 3-11 -14.65 50 4.46% Counts (Why) Mar 12th Tally Classic XVI
114 Florida State Loss 8-12 -13.4 22 4.86% Counts Mar 12th Tally Classic XVI
110 Georgia State Loss 7-15 -20.79 33 4.86% Counts (Why) Mar 13th Tally Classic XVI
274 Georgia Southern Win 15-8 6.09 13 4.86% Counts (Why) Mar 13th Tally Classic XVI
297 Miami Win 12-6 1.17 17 5.95% Counts (Why) Apr 9th Florida D I College Mens CC 2022
47 Florida Loss 4-13 -2.66 38 6.12% Counts (Why) Apr 9th Florida D I College Mens CC 2022
217 Florida Gulf Coast Loss 4-8 -40.52 18 4.86% Counts Apr 9th Florida D I College Mens CC 2022
89 Central Florida Loss 5-13 -20.52 23 6.12% Counts (Why) Apr 10th Florida D I College Mens CC 2022
302 North Florida Win 12-2 0.62 17 5.87% Counts (Why) Apr 10th Florida D I College Mens CC 2022
105 LSU Win 13-4 64.41 23 7.28% Counts (Why) Apr 30th Southeast D I College Mens Regionals 2022
9 Georgia** Loss 5-13 0 21 0% Ignored (Why) Apr 30th Southeast D I College Mens Regionals 2022
108 Vanderbilt Loss 11-13 -2.5 22 7.28% Counts Apr 30th Southeast D I College Mens Regionals 2022
121 Tennessee-Chattanooga Loss 11-13 -4.95 17 7.28% Counts Apr 30th Southeast 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.