(4) #42 Tennessee (17-6)

1367.74 (41)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
87 Alabama Win 12-6 12.28 48 4.41% Counts (Why) Feb 26th TOTS
127 Jacksonville State** Win 13-1 0 49 0% Ignored (Why) Feb 26th TOTS
223 Purdue-B** Win 13-2 0 66 0% Ignored (Why) Feb 26th TOTS
217 Vanderbilt** Win 13-1 0 53 0% Ignored (Why) Feb 26th TOTS
15 Florida State Loss 6-15 -10 25 4.53% Counts (Why) Feb 27th TOTS
69 Union (Tennessee) Win 13-6 18.21 44 4.53% Counts (Why) Feb 27th TOTS
108 Tennessee-Chattanooga Win 15-7 4.66 69 4.53% Counts (Why) Feb 27th TOTS
27 Ohio Loss 4-13 -24.54 32 5.71% Counts (Why) Mar 26th Rodeo
98 Massachusetts Win 11-8 -2.03 7 5.71% Counts Mar 26th Rodeo
22 SUNY-Binghamton Win 9-8 19.77 34 5.4% Counts Mar 26th Rodeo
14 Georgia Loss 5-13 -12.24 21 5.71% Counts (Why) Mar 27th Rodeo
41 North Carolina State Win 13-4 36.48 21 5.71% Counts (Why) Mar 27th Rodeo
22 SUNY-Binghamton Win 13-4 49.71 34 5.71% Counts (Why) Mar 27th Rodeo
30 Georgia Tech Loss 6-9 -16.63 15 5.69% Counts Apr 9th Southern Appalachian D I College Womens CC 2022
181 Georgia Tech-B** Win 13-3 0 217 0% Ignored (Why) Apr 9th Southern Appalachian D I College Womens CC 2022
108 Tennessee-Chattanooga Win 9-8 -24.29 69 6.06% Counts Apr 9th Southern Appalachian D I College Womens CC 2022
237 Emory-B** Win 13-0 0 32 0% Ignored (Why) Apr 10th Southern Appalachian D I College Womens CC 2022
80 Emory Win 9-7 -0.46 70 5.88% Counts Apr 10th Southern Appalachian D I College Womens CC 2022
87 Alabama Win 13-5 23.67 48 7.62% Counts (Why) Apr 30th Southeast D I College Womens Regionals 2022
47 Florida Loss 8-13 -43.26 27 7.62% Counts Apr 30th Southeast D I College Womens Regionals 2022
163 LSU** Win 13-0 0 55 0% Ignored (Why) Apr 30th Southeast D I College Womens Regionals 2022
124 Tulane Win 13-5 1.3 53 7.62% Counts (Why) Apr 30th Southeast D I College Womens Regionals 2022
30 Georgia Tech Loss 7-13 -34.18 15 7.62% Counts May 1st Southeast D I College Womens 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.