(1) #56 Tennessee (13-5)

1340.42 (128)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
195 Georgia Tech-B** Win 11-2 0 102 0% Ignored (Why) Feb 11th 2023 TOTS The Only Tenn I See
179 LSU** Win 11-1 0 160 0% Ignored (Why) Feb 11th 2023 TOTS The Only Tenn I See
114 Union (Tennessee) Win 9-3 8.58 136 5.12% Counts (Why) Feb 11th 2023 TOTS The Only Tenn I See
206 Vanderbilt** Win 11-0 0 140 0% Ignored (Why) Feb 11th 2023 TOTS The Only Tenn I See
54 Georgia Tech Win 10-3 35.01 140 5.41% Counts (Why) Feb 12th 2023 TOTS The Only Tenn I See
77 Tennessee-Chattanooga Win 13-3 28.97 135 6.19% Counts (Why) Feb 12th 2023 TOTS The Only Tenn I See
30 South Carolina Loss 1-15 -20.88 133 6.95% Counts (Why) Feb 25th Commonwealth Cup Weekend2 2023
65 Carnegie Mellon Loss 9-14 -40.35 115 6.95% Counts Feb 25th Commonwealth Cup Weekend2 2023
26 Notre Dame Loss 4-15 -18.83 132 6.95% Counts (Why) Feb 25th Commonwealth Cup Weekend2 2023
47 Florida Loss 8-9 0.17 142 6.57% Counts Feb 26th Commonwealth Cup Weekend2 2023
129 Maryland Win 12-7 -3.27 9 6.95% Counts (Why) Feb 26th Commonwealth Cup Weekend2 2023
95 Temple Loss 6-8 -38.81 173 5.96% Counts Feb 26th Commonwealth Cup Weekend2 2023
73 St. Olaf Win 13-6 46.68 95 8.75% Counts (Why) Mar 25th Needle in a Ho Stack2
94 Boston College Win 13-3 28.6 171 8.75% Counts (Why) Mar 25th Needle in a Ho Stack2
114 Union (Tennessee) Win 9-8 -28.53 136 8.28% Counts Mar 25th Needle in a Ho Stack2
201 Wake Forest** Win 13-1 0 150 0% Ignored (Why) Mar 25th Needle in a Ho Stack2
64 Appalachian State Win 12-4 48.98 136 8.4% Counts (Why) Mar 26th Needle in a Ho Stack2
186 Richmond Win 13-6 -45.98 145 8.75% Counts (Why) Mar 26th Needle in a Ho Stack2
**Blowout Eligible. Learn more about how this works here.

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.