(3) #66 Dartmouth (10-11)

1452.25 (18)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
39 Cincinnati Loss 10-11 2.12 228 3.87% Counts Jan 25th Mid Atlantic Warm Up 2025
157 Johns Hopkins Win 13-3 6.18 47 3.87% Counts (Why) Jan 25th Mid Atlantic Warm Up 2025
159 George Mason Win 13-4 5.86 43 3.87% Counts (Why) Jan 25th Mid Atlantic Warm Up 2025
75 Carnegie Mellon Loss 13-14 -8.29 26 3.87% Counts Jan 26th Mid Atlantic Warm Up 2025
138 RIT Win 15-4 9.69 21 3.87% Counts (Why) Jan 26th Mid Atlantic Warm Up 2025
78 Richmond Loss 10-11 -9.23 68 3.87% Counts Jan 26th Mid Atlantic Warm Up 2025
115 Vermont-B Win 13-10 2.84 35 3.87% Counts Jan 26th Mid Atlantic Warm Up 2025
96 Appalachian State Win 13-6 21.63 68 4.87% Counts (Why) Feb 22nd Easterns Qualifier 2025
67 Indiana Loss 11-12 -6.87 51 4.87% Counts Feb 22nd Easterns Qualifier 2025
49 North Carolina State Loss 9-12 -11.93 21 4.87% Counts Feb 22nd Easterns Qualifier 2025
52 William & Mary Loss 8-13 -20.34 101 4.87% Counts Feb 22nd Easterns Qualifier 2025
69 Auburn Win 15-10 22.02 76 4.87% Counts Feb 23rd Easterns Qualifier 2025
97 Duke Loss 14-15 -15.57 42 4.87% Counts Feb 23rd Easterns Qualifier 2025
88 Georgetown Win 15-7 23.39 78 4.87% Counts (Why) Feb 23rd Easterns Qualifier 2025
17 Tufts Loss 5-13 -9.55 80 5.8% Counts (Why) Mar 15th Mens Centex 2025
46 Middlebury Loss 7-13 -25.35 7 5.8% Counts Mar 15th Mens Centex 2025
40 Wisconsin Loss 7-8 2.76 19 5.15% Counts Mar 15th Mens Centex 2025
57 Illinois Loss 6-8 -12.43 44 4.98% Counts Mar 15th Mens Centex 2025
129 Arizona State Win 15-12 -1.67 8 5.8% Counts Mar 16th Mens Centex 2025
46 Middlebury Win 15-14 16.65 7 5.8% Counts Mar 16th Mens Centex 2025
62 Tulane Win 9-8 7.84 4 5.48% Counts Mar 16th Mens Centex 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.