(6) #87 Alabama (15-6)

1054.83 (48)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
217 Vanderbilt** Win 9-3 0 53 0% Ignored (Why) Feb 26th TOTS
127 Jacksonville State Win 11-3 12.78 49 4.02% Counts (Why) Feb 26th TOTS
42 Tennessee Loss 6-12 -11.85 41 4.26% Counts Feb 26th TOTS
108 Tennessee-Chattanooga Win 11-8 8.09 69 4.38% Counts Feb 26th TOTS
69 Union (Tennessee) Loss 8-11 -12.3 44 4.38% Counts Feb 27th TOTS
108 Tennessee-Chattanooga Win 13-10 6.38 69 4.38% Counts Feb 27th TOTS
148 Alabama-Huntsville Win 7-5 -3.07 38 3.48% Counts Feb 27th TOTS
163 LSU Win 12-8 -5.86 55 5.51% Counts Mar 26th T Town Throwdown 2022
148 Alabama-Huntsville Win 10-5 8.28 38 4.9% Counts (Why) Mar 26th T Town Throwdown 2022
51 Central Florida Loss 0-11 -19.6 40 5.06% Counts (Why) Mar 26th T Town Throwdown 2022
220 Mississippi State** Win 11-4 0 45 0% Ignored (Why) Mar 27th T Town Throwdown 2022
163 LSU Win 12-6 2.14 55 5.37% Counts (Why) Mar 27th T Town Throwdown 2022
51 Central Florida Loss 3-9 -17.58 40 4.56% Counts (Why) Mar 27th T Town Throwdown 2022
217 Vanderbilt** Win 13-3 0 53 0% Ignored (Why) Apr 23rd Gulf Coast D I College Womens CC 2022
124 Tulane Win 11-7 14.19 53 6.76% Counts Apr 23rd Gulf Coast D I College Womens CC 2022
140 Auburn Win 13-10 -2.44 49 6.95% Counts Apr 24th Gulf Coast D I College Womens CC 2022
127 Jacksonville State Win 15-7 22.81 49 6.95% Counts (Why) Apr 24th Gulf Coast D I College Womens CC 2022
127 Jacksonville State Win 10-9 -13.47 49 7.36% Counts Apr 30th Southeast D I College Womens Regionals 2022
15 Florida State Loss 6-13 8.1 25 7.36% Counts (Why) Apr 30th Southeast D I College Womens Regionals 2022
108 Tennessee-Chattanooga Win 11-6 26.78 69 6.96% Counts (Why) Apr 30th Southeast D I College Womens Regionals 2022
42 Tennessee Loss 5-13 -22.81 41 7.36% Counts (Why) Apr 30th 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.