(9) #253 Brown-B (9-11)

857.11 (28)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
90 Bowdoin** Loss 1-4 0 1 0% Ignored (Why) Mar 1st Garden State 2025
196 Haverford Loss 7-10 -10.12 10 5.37% Counts Mar 1st Garden State 2025
209 Penn State-B Loss 5-7 -7.58 3 4.51% Counts Mar 1st Garden State 2025
148 Rhode Island Loss 4-9 -9.42 3 4.69% Counts (Why) Mar 1st Garden State 2025
355 Army Win 12-7 4.65 3 5.67% Counts (Why) Mar 2nd Garden State 2025
209 Penn State-B Loss 6-9 -13.32 3 5.04% Counts Mar 2nd Garden State 2025
293 Amherst Win 5-4 -0.48 92 4.14% Counts Mar 8th Grand Northeast Kickoff 2025
231 Colby Win 8-4 31.85 56 4.78% Counts (Why) Mar 8th Grand Northeast Kickoff 2025
185 Northeastern-B Loss 4-7 -11.36 100 4.57% Counts Mar 8th Grand Northeast Kickoff 2025
123 Bates Loss 5-8 1.77 19 4.97% Counts Mar 9th Grand Northeast Kickoff 2025
376 New Hampshire Win 10-1 0.98 72 5.25% Counts (Why) Mar 9th Grand Northeast Kickoff 2025
392 Middlebury-B** Win 13-0 0 52 0% Ignored (Why) Mar 9th Grand Northeast Kickoff 2025
231 Colby Loss 5-15 -33.89 56 6.01% Counts (Why) Mar 9th Grand Northeast Kickoff 2025
411 Boston University-B** Win 13-5 0 8 0% Ignored (Why) Apr 12th New England Dev Mens Conferences 2025
374 Harvard-B Win 11-4 2.39 20 7.36% Counts (Why) Apr 12th New England Dev Mens Conferences 2025
91 Vermont-B Loss 7-12 8.78 60 8.02% Counts Apr 12th New England Dev Mens Conferences 2025
185 Northeastern-B Loss 7-10 -10.72 100 7.59% Counts Apr 12th New England Dev Mens Conferences 2025
361 MIT-B Win 9-3 9.09 17 6.64% Counts (Why) Apr 13th New England Dev Mens Conferences 2025
201 Tufts-B Win 9-7 37.34 5 7.36% Counts Apr 13th New England Dev Mens Conferences 2025
185 Northeastern-B Loss 9-11 0.87 100 8.02% Counts Apr 13th New England Dev Mens Conferences 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.