(8) #215 Minnesota-Duluth (2-17)

268.78 (97)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
172 Florida State Loss 2-15 -20.18 247 6.99% Counts (Why) Mar 15th Tally Classic XIX
221 Florida Tech Loss 5-10 -41.43 321 6.21% Counts Mar 15th Tally Classic XIX
131 Harvard Loss 4-8 -0.02 243 5.56% Counts Mar 15th Tally Classic XIX
189 LSU Loss 7-9 -3.91 165 6.42% Counts Mar 15th Tally Classic XIX
62 Chicago** Loss 0-11 0 174 0% Ignored (Why) Mar 29th Old Capitol Open 2025
102 Macalester** Loss 4-11 0 378 0% Ignored (Why) Mar 29th Old Capitol Open 2025
74 Purdue** Loss 3-13 0 397 0% Ignored (Why) Mar 29th Old Capitol Open 2025
142 Saint Louis Loss 3-13 -11.11 372 7.85% Counts (Why) Mar 29th Old Capitol Open 2025
129 Winona State Loss 6-11 2.43 253 7.43% Counts Mar 30th Old Capitol Open 2025
147 Wisconsin-La Crosse Loss 7-11 -0.89 253 7.64% Counts Mar 30th Old Capitol Open 2025
92 Iowa State Loss 4-9 19.12 217 7.29% Counts (Why) Apr 12th Western North Central D I Womens Conferences 2025
24 Minnesota** Loss 1-15 0 313 0% Ignored (Why) Apr 12th Western North Central D I Womens Conferences 2025
195 Minnesota-B Loss 6-7 5.87 7.29% Counts Apr 12th Western North Central D I Womens Conferences 2025
95 Marquette Loss 7-11 38.2 303 9.63% Counts Apr 26th North Central D I College Womens Regionals 2025
95 Marquette** Loss 2-11 0 303 0% Ignored (Why) Apr 26th North Central D I College Womens Regionals 2025
195 Minnesota-B Win 7-6 28.93 8.18% Counts Apr 26th North Central D I College Womens Regionals 2025
35 Wisconsin** Loss 3-15 0 390 0% Ignored (Why) Apr 26th North Central D I College Womens Regionals 2025
195 Minnesota-B Loss 7-11 -28.46 9.63% Counts Apr 27th North Central D I College Womens Regionals 2025
217 Wisconsin-Milwaukee Win 10-9 12.88 282 9.89% Counts Apr 27th North Central D I College Womens Regionals 2025
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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.