(1) #79 Charleston (13-5)

1252.6 (28)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
239 Radford** Win 13-3 0 34 0% Ignored (Why) Feb 26th Cutlass Classic 2022
168 Kennesaw State Win 11-9 -6.57 31 5.52% Counts Feb 26th Cutlass Classic 2022
304 Embry-Riddle** Win 13-2 0 30 0% Ignored (Why) Feb 26th Cutlass Classic 2022
198 George Washington Win 7-0 4.59 37 4% Counts (Why) Feb 26th Cutlass Classic 2022
117 Appalachian State Win 13-10 9.29 32 5.52% Counts Feb 27th Cutlass Classic 2022
299 North Florida** Win 13-1 0 25 0% Ignored (Why) Mar 19th College Southerns XX
189 Wisconsin-Eau Claire Win 12-6 8.53 5 6.39% Counts (Why) Mar 19th College Southerns XX
204 Georgia Tech-B Win 13-7 2.24 28 6.56% Counts (Why) Mar 19th College Southerns XX
35 Middlebury Loss 6-15 -22.62 60 6.56% Counts (Why) Mar 20th College Southerns XX
213 Luther Win 15-8 1.29 0 6.56% Counts (Why) Mar 20th College Southerns XX
90 Carleton College-CHOP Win 14-11 18.32 7 6.56% Counts Mar 20th College Southerns XX
191 Wake Forest Win 12-7 5.66 25 8.76% Counts (Why) Apr 23rd Carolina D I College Mens CC 2022
2 North Carolina** Loss 4-13 0 31 0% Ignored (Why) Apr 23rd Carolina D I College Mens CC 2022
42 South Carolina Loss 3-13 -34.39 45 8.76% Counts (Why) Apr 23rd Carolina D I College Mens CC 2022
23 Duke Loss 6-13 -17.49 33 8.76% Counts (Why) Apr 23rd Carolina D I College Mens CC 2022
207 Clemson Win 12-8 -8.52 31 8.76% Counts Apr 24th Carolina D I College Mens CC 2022
191 Wake Forest Win 12-6 10.98 25 8.53% Counts (Why) Apr 24th Carolina D I College Mens CC 2022
23 Duke Loss 9-10 28.11 33 8.76% Counts Apr 24th Carolina D I College Mens CC 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.