(35) #172 Union (Tennessee) (7-11)

1233.68 (143)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
47 Alabama Loss 7-11 3.7 283 4.21% Counts Feb 10th Golden Triangle Invitational
277 Jacksonville State Win 13-7 7.52 301 4.32% Counts (Why) Feb 10th Golden Triangle Invitational
164 Kennesaw State Win 11-7 22.07 258 4.21% Counts Feb 10th Golden Triangle Invitational
242 Mississippi State -B Win 15-7 15.92 250 4.32% Counts (Why) Feb 10th Golden Triangle Invitational
48 Auburn Loss 8-13 2.37 335 4.32% Counts Feb 11th Golden Triangle Invitational
176 Navy Win 13-9 21.53 178 5.14% Counts Mar 2nd FCS D III Tune Up 2024
179 North Carolina-Asheville Loss 12-13 -8.63 263 5.14% Counts Mar 2nd FCS D III Tune Up 2024
173 Xavier Win 13-5 32.5 260 5.14% Counts (Why) Mar 2nd FCS D III Tune Up 2024
81 Lewis & Clark Loss 4-13 -13.34 248 5.14% Counts (Why) Mar 2nd FCS D III Tune Up 2024
68 Franciscan Loss 11-13 10.73 164 5.14% Counts Mar 3rd FCS D III Tune Up 2024
123 Oberlin Loss 8-13 -18.05 244 5.14% Counts Mar 3rd FCS D III Tune Up 2024
65 Richmond Loss 6-13 -8.6 311 5.14% Counts (Why) Mar 3rd FCS D III Tune Up 2024
73 Ave Maria Loss 5-13 -17.17 255 7.27% Counts (Why) Apr 13th Southeast D III Mens Conferences 2024
206 Embry-Riddle Win 10-9 -0.77 369 7.27% Counts Apr 13th Southeast D III Mens Conferences 2024
265 Georgia College Win 11-6 14.96 242 6.88% Counts (Why) Apr 13th Southeast D III Mens Conferences 2024
73 Ave Maria Loss 11-15 -0.01 255 7.27% Counts Apr 14th Southeast D III Mens Conferences 2024
88 Berry Loss 8-13 -13.97 379 7.27% Counts Apr 14th Southeast D III Mens Conferences 2024
206 Embry-Riddle Loss 4-11 -52.53 369 6.67% Counts (Why) Apr 14th Southeast D III Mens Conferences 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.