(10) #37 McGill (11-9)

1818.23 (227)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
68 Alabama-Huntsville Win 12-11 -2.86 166 4.32% Counts Jan 31st Florida Warm Up 2025
45 Virginia Tech Loss 9-10 -7.58 163 4.32% Counts Jan 31st Florida Warm Up 2025
27 Minnesota Loss 4-13 -23.79 178 4.32% Counts (Why) Jan 31st Florida Warm Up 2025
17 Brown Loss 12-13 5.06 136 4.32% Counts Feb 1st Florida Warm Up 2025
132 Florida State Win 13-1 4.94 244 4.32% Counts (Why) Feb 1st Florida Warm Up 2025
90 Texas A&M Win 13-10 0.29 256 4.32% Counts Feb 1st Florida Warm Up 2025
50 Tulane Loss 8-10 -15.45 267 4.21% Counts Feb 2nd Florida Warm Up 2025
32 Utah State Loss 9-10 -4.12 220 4.32% Counts Feb 2nd Florida Warm Up 2025
63 Duke Win 13-8 17.71 375 5.14% Counts Feb 22nd Easterns Qualifier 2025
196 Kennesaw State Win 12-8 -15.96 192 5.14% Counts Feb 22nd Easterns Qualifier 2025
77 Ohio State Win 10-4 17.39 268 4.49% Counts (Why) Feb 22nd Easterns Qualifier 2025
28 Virginia Win 12-10 16.37 202 5.14% Counts Feb 22nd Easterns Qualifier 2025
56 Indiana Win 15-7 24.97 236 5.14% Counts (Why) Feb 23rd Easterns Qualifier 2025
48 North Carolina-Wilmington Loss 9-11 -17.1 117 5.14% Counts Feb 23rd Easterns Qualifier 2025
35 South Carolina Loss 11-14 -16.12 55 5.14% Counts Feb 23rd Easterns Qualifier 2025
1 Massachusetts Loss 6-10 7.78 158 7.07% Counts Apr 12th Greater New England D I Mens Conferences 2025
134 Maine Win 14-2 8.88 186 7.7% Counts (Why) Apr 12th Greater New England D I Mens Conferences 2025
17 Brown Loss 11-12 9.35 136 7.7% Counts Apr 13th Greater New England D I Mens Conferences 2025
152 Rhode Island Win 12-8 -9.07 217 7.7% Counts Apr 13th Greater New England D I Mens Conferences 2025
385 New Hampshire** Win 15-3 0 475 0% Ignored (Why) Apr 13th Greater New England D I Mens Conferences 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.