(3) #177 Minnesota-Duluth (7-11)

1155.35 (25)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
103 Berry Loss 10-11 9.61 18 5.52% Counts Mar 15th Tally Classic XIX
128 Central Florida Loss 13-15 -2.44 19 5.52% Counts Mar 15th Tally Classic XIX
229 Harvard Loss 10-12 -26.53 18 5.52% Counts Mar 15th Tally Classic XIX
158 Grinnell Loss 7-13 -32.52 14 6.19% Counts Mar 29th Old Capitol Open 2025
171 Northern Iowa Win 13-11 16.68 8 6.19% Counts Mar 29th Old Capitol Open 2025
156 Wisconsin-La Crosse Loss 9-13 -22.32 17 6.19% Counts Mar 29th Old Capitol Open 2025
258 DePaul Win 11-10 -12.51 16 6.19% Counts Mar 30th Old Capitol Open 2025
200 Nebraska Win 13-12 1.73 62 6.19% Counts Mar 30th Old Capitol Open 2025
206 Winona State Loss 7-9 -24.25 14 5.68% Counts Mar 30th Old Capitol Open 2025
5 Carleton College** Loss 6-15 0 27 0% Ignored (Why) Apr 13th Northwoods D I Mens Conferences 2025
28 Minnesota** Loss 6-15 0 25 0% Ignored (Why) Apr 13th Northwoods D I Mens Conferences 2025
5 Carleton College** Loss 2-15 0 27 0% Ignored (Why) Apr 26th North Central D I College Mens Regionals 2025
110 Iowa Loss 11-12 10.61 18 7.8% Counts Apr 26th North Central D I College Mens Regionals 2025
145 Wisconsin-Milwaukee Win 15-11 41.95 19 7.8% Counts Apr 26th North Central D I College Mens Regionals 2025
200 Nebraska Win 15-6 42.41 62 7.8% Counts (Why) Apr 26th North Central D I College Mens Regionals 2025
320 Minnesota-C Win 15-8 0.49 20 7.8% Counts (Why) Apr 27th North Central D I College Mens Regionals 2025
200 Nebraska Loss 9-11 -29.45 62 7.8% Counts Apr 27th North Central D I College Mens Regionals 2025
223 Wisconsin-B Win 15-9 28.45 14 7.8% Counts Apr 27th North Central D I College Mens Regionals 2025
**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.