(6) #30 Ottawa (17-1) ME 1

1872.88 (39)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
182 Carleton University** Win 13-2 0 108 0% Ignored (Why) Mar 22nd Salt City Classic
99 Syracuse Win 13-5 11.94 208 5.75% Counts (Why) Mar 22nd Salt City Classic
97 SUNY-Buffalo Win 13-7 9.73 154 5.75% Counts (Why) Mar 22nd Salt City Classic
152 Rhode Island Win 13-6 -0.27 217 5.75% Counts (Why) Mar 22nd Salt City Classic
76 Williams Win 15-10 10.58 207 5.75% Counts Mar 23rd Salt City Classic
81 Rochester Win 15-6 17.23 215 5.75% Counts (Why) Mar 23rd Salt City Classic
115 RIT Win 15-10 -2.58 291 6.84% Counts Apr 12th Western NY D I Mens Conferences 2025
147 Toronto Win 14-12 -27.32 420 6.84% Counts Apr 12th Western NY D I Mens Conferences 2025
97 SUNY-Buffalo Win 15-7 14.82 154 6.84% Counts (Why) Apr 12th Western NY D I Mens Conferences 2025
146 SUNY-Binghamton Win 15-5 0.79 156 6.84% Counts (Why) Apr 12th Western NY D I Mens Conferences 2025
51 Cornell Loss 8-13 -47.4 200 6.84% Counts Apr 13th Western NY D I Mens Conferences 2025
97 SUNY-Buffalo Win 9-7 -7.97 154 6.28% Counts Apr 13th Western NY D I Mens Conferences 2025
248 NYU** Win 15-0 0 238 0% Ignored (Why) Apr 26th Metro East D I College Mens Regionals 2025
313 Rowan** Win 15-3 0 311 0% Ignored (Why) Apr 26th Metro East D I College Mens Regionals 2025
115 RIT Win 15-8 6.34 291 7.68% Counts (Why) Apr 26th Metro East D I College Mens Regionals 2025
51 Cornell Win 15-8 34.6 200 7.68% Counts (Why) Apr 27th Metro East D I College Mens Regionals 2025
147 Toronto Win 15-3 0.6 420 7.68% Counts (Why) Apr 27th Metro East D I College Mens Regionals 2025
92 Yale Win 11-10 -21.44 228 7.68% Counts Apr 27th Metro East 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.