() #121 Tennessee-Chattanooga (7-10)

1109.13 (17)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
91 Princeton Loss 6-10 -22.02 46 5.48% Counts Mar 26th Rodeo
85 Richmond Loss 8-9 0.8 27 5.65% Counts Mar 26th Rodeo
36 St. Olaf Loss 8-13 -3.09 10 5.98% Counts Mar 26th Rodeo
211 North Carolina-B Win 13-4 14.43 38 5.98% Counts (Why) Mar 26th Rodeo
211 North Carolina-B Win 13-7 11.73 38 5.98% Counts (Why) Mar 27th Rodeo
143 SUNY-Binghamton Loss 9-11 -23.1 47 5.98% Counts Mar 27th Rodeo
9 Georgia Loss 6-13 18.03 21 6.71% Counts (Why) Apr 9th Southern Appalachian D I College Mens CC 2022
274 Georgia Southern** Win 12-4 0 13 0% Ignored (Why) Apr 9th Southern Appalachian D I College Mens CC 2022
45 Georgia Tech Loss 7-11 -3.92 23 6.53% Counts Apr 9th Southern Appalachian D I College Mens CC 2022
132 Tennessee Win 12-11 4.42 37 6.71% Counts Apr 9th Southern Appalachian D I College Mens CC 2022
161 Kennesaw State Win 13-10 10.16 23 6.71% Counts Apr 10th Southern Appalachian D I College Mens CC 2022
9 Georgia** Loss 5-15 0 21 0% Ignored (Why) Apr 10th Southern Appalachian D I College Mens CC 2022
102 Emory Loss 8-13 -30.94 3 6.71% Counts Apr 10th Southern Appalachian D I College Mens CC 2022
156 South Florida Win 13-11 5.47 24 7.98% Counts Apr 30th Southeast D I College Mens Regionals 2022
92 Alabama-Huntsville Loss 9-10 -0.87 27 7.98% Counts Apr 30th Southeast D I College Mens Regionals 2022
217 Florida Gulf Coast Win 12-5 17.1 18 7.66% Counts (Why) Apr 30th Southeast D I College Mens Regionals 2022
53 Tulane Loss 10-13 2.36 31 7.98% 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.