(17) #66 Virginia (8-13)

1394.17 (111)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
91 Indiana Loss 10-11 -10.33 100 3.99% Counts Feb 10th Queen City Tune Up 2024
25 McGill Loss 7-11 -3.51 130 3.89% Counts Feb 10th Queen City Tune Up 2024
1 North Carolina Loss 7-15 12.24 18 3.99% Counts (Why) Feb 10th Queen City Tune Up 2024
28 North Carolina-Wilmington Loss 10-13 0.51 109 3.99% Counts Feb 10th Queen City Tune Up 2024
84 Appalachian State Win 10-8 7.9 40 3.89% Counts Feb 11th Queen City Tune Up 2024
48 Missouri Loss 9-15 -16.42 13 3.99% Counts Feb 11th Queen City Tune Up 2024
27 Georgia Tech Loss 7-13 -9.93 17 4.48% Counts Feb 24th Easterns Qualifier 2024
154 Harvard Win 13-5 10.74 116 4.48% Counts (Why) Feb 24th Easterns Qualifier 2024
106 Notre Dame Win 13-11 2.11 25 4.48% Counts Feb 24th Easterns Qualifier 2024
126 Lehigh Win 11-8 5.48 13 4.48% Counts Feb 24th Easterns Qualifier 2024
57 Auburn Loss 12-13 -3.38 7 4.48% Counts Feb 25th Easterns Qualifier 2024
38 Duke Win 14-11 23.92 16 4.48% Counts Feb 25th Easterns Qualifier 2024
36 North Carolina-Charlotte Win 12-9 26.72 20 4.48% Counts Feb 25th Easterns Qualifier 2024
34 Ohio State Loss 11-14 -3.08 140 4.48% Counts Feb 25th Easterns Qualifier 2024
50 Alabama Loss 7-10 -16.93 3 5.66% Counts Mar 30th Huck Finn 2024
76 Purdue Win 11-10 5.6 56 5.98% Counts Mar 30th Huck Finn 2024
49 St Olaf Loss 7-10 -16.83 64 5.66% Counts Mar 30th Huck Finn 2024
19 Washington University Loss 8-11 6.71 112 5.98% Counts Mar 30th Huck Finn 2024
50 Alabama Loss 9-12 -15.14 3 5.98% Counts Mar 31st Huck Finn 2024
67 Chicago Win 9-7 15.81 40 5.49% Counts Mar 31st Huck Finn 2024
65 Stanford Loss 7-10 -22.72 80 5.66% Counts Mar 31st Huck Finn 2024
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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.