(18) #145 Dartmouth (8-12)

906.1 (42)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
130 Boston University Loss 6-7 -0.38 6 3.58% Counts Feb 24th Bring The Huckus 2024
94 Lehigh Loss 5-8 -3.12 105 3.58% Counts Feb 24th Bring The Huckus 2024
193 SUNY-Geneseo Win 7-6 -9.03 60 3.58% Counts Feb 24th Bring The Huckus 2024
52 Haverford/Bryn Mawr Loss 5-11 2.64 8 3.97% Counts (Why) Feb 24th Bring The Huckus 2024
193 SUNY-Geneseo Win 8-6 -2.61 60 3.72% Counts Feb 25th Bring The Huckus 2024
52 Haverford/Bryn Mawr Loss 8-11 13.49 8 4.33% Counts Feb 25th Bring The Huckus 2024
108 West Chester Win 9-6 27.68 119 3.85% Counts Feb 25th Bring The Huckus 2024
138 Liberty Loss 4-9 -26.43 32 4.51% Counts (Why) Mar 23rd Rodeo 2024
86 Williams Loss 4-11 -9.08 10 5% Counts (Why) Mar 23rd Rodeo 2024
62 Duke Loss 7-9 15.73 216 5% Counts Mar 23rd Rodeo 2024
151 North Carolina-B Win 9-7 12.71 75 5% Counts Mar 23rd Rodeo 2024
151 North Carolina-B Win 11-6 27.66 75 5.16% Counts (Why) Mar 24th Rodeo 2024
138 Liberty Loss 7-8 -4.3 32 4.85% Counts Mar 24th Rodeo 2024
87 Bates Win 7-6 31.25 40 5.36% Counts Apr 13th North New England D III Womens Conferences 2024
185 Bowdoin Win 5-4 -9.14 67 4.46% Counts Apr 13th North New England D III Womens Conferences 2024
92 Middlebury Loss 3-6 -6.91 159 4.46% Counts Apr 13th North New England D III Womens Conferences 2024
175 Amherst Loss 8-9 -26.41 339 7.3% Counts May 4th New England D III College Womens Regionals 2024
185 Bowdoin Win 11-8 3.77 67 7.71% Counts May 4th New England D III College Womens Regionals 2024
92 Middlebury Loss 5-13 -16.81 159 7.71% Counts (Why) May 4th New England D III College Womens Regionals 2024
173 Bentley Loss 7-8 -22.22 151 6.85% Counts May 5th New England D III College Womens Regionals 2024
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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.