(5) #179 North Carolina-Asheville (11-8)

1199.53 (263)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
168 Kenyon Win 13-10 18.42 493 4.61% Counts Mar 2nd FCS D III Tune Up 2024
198 Messiah Win 13-9 16.82 297 4.61% Counts Mar 2nd FCS D III Tune Up 2024
129 Michigan Tech Loss 7-13 -18.13 208 4.61% Counts Mar 2nd FCS D III Tune Up 2024
172 Union (Tennessee) Win 13-12 7.69 143 4.61% Counts Mar 2nd FCS D III Tune Up 2024
274 Air Force Win 13-5 12.13 279 4.61% Counts (Why) Mar 3rd FCS D III Tune Up 2024
206 Embry-Riddle Win 13-8 19.12 369 4.61% Counts Mar 3rd FCS D III Tune Up 2024
123 Oberlin Loss 8-13 -14.45 244 4.61% Counts Mar 3rd FCS D III Tune Up 2024
196 Charleston Loss 6-11 -33.52 357 5.19% Counts Mar 23rd Needle in a Ho Stack 2024
98 Georgia State Loss 4-11 -14.77 109 5.03% Counts (Why) Mar 23rd Needle in a Ho Stack 2024
346 Coastal Carolina** Win 11-4 0 305 0% Ignored (Why) Mar 24th Needle in a Ho Stack 2024
306 High Point Win 12-5 5.17 181 5.26% Counts (Why) Mar 24th Needle in a Ho Stack 2024
251 North Carolina State-B Win 10-9 -7.63 183 5.48% Counts Mar 24th Needle in a Ho Stack 2024
241 Wake Forest Win 11-8 8.88 312 5.48% Counts Mar 24th Needle in a Ho Stack 2024
209 Christopher Newport Loss 10-13 -33.12 369 6.91% Counts Apr 20th Atlantic Coast D III Mens Conferences 2024
84 Elon Loss 3-15 -16.56 334 6.91% Counts (Why) Apr 20th Atlantic Coast D III Mens Conferences 2024
176 Navy Win 12-6 42.7 178 6.72% Counts (Why) Apr 20th Atlantic Coast D III Mens Conferences 2024
114 Davidson Loss 9-10 8.37 291 6.91% Counts Apr 21st Atlantic Coast D III Mens Conferences 2024
306 High Point Win 15-5 6.9 181 6.91% Counts (Why) Apr 21st Atlantic Coast D III Mens Conferences 2024
176 Navy Loss 9-10 -8.32 178 6.91% Counts Apr 21st Atlantic Coast 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.