(4) #132 Cedarville (11-7)

1002.53 (56)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
183 South Carolina-B Win 8-3 7.71 34 3.64% Counts (Why) Feb 17th Commonwealth Cup Weekend 1 2024
114 Richmond Loss 6-7 -0.05 180 3.87% Counts Feb 17th Commonwealth Cup Weekend 1 2024
50 Georgetown Loss 6-13 -0.06 82 4.68% Counts (Why) Feb 17th Commonwealth Cup Weekend 1 2024
163 Catholic Win 8-4 14.49 36 3.72% Counts (Why) Feb 18th Commonwealth Cup Weekend 1 2024
217 Georgetown-B** Win 13-3 0 183 0% Ignored (Why) Feb 18th Commonwealth Cup Weekend 1 2024
204 Elon Win 11-7 -6.13 60 4.56% Counts Feb 18th Commonwealth Cup Weekend 1 2024
63 Tennessee Loss 7-13 -5.46 43 6.25% Counts Mar 23rd Needle in a Ho Stack 2024
74 Davidson Loss 3-9 -12.4 239 5.17% Counts (Why) Mar 23rd Needle in a Ho Stack 2024
183 South Carolina-B Win 13-2 13.6 34 6.25% Counts (Why) Mar 24th Needle in a Ho Stack 2024
249 Emory-B** Win 13-0 0 32 0% Ignored (Why) Mar 24th Needle in a Ho Stack 2024
120 Charleston Loss 5-10 -29.06 165 5.55% Counts Mar 24th Needle in a Ho Stack 2024
199 Xavier Win 15-5 4.41 285 7.87% Counts (Why) Apr 20th Ohio D III Womens Conferences 2024
203 Oberlin Win 15-2 2.08 424 7.87% Counts (Why) Apr 20th Ohio D III Womens Conferences 2024
159 Kenyon Win 15-6 36.17 389 7.87% Counts (Why) Apr 20th Ohio D III Womens Conferences 2024
168 Swarthmore Win 11-5 31.06 204 7.66% Counts (Why) Apr 27th Ohio Valley D III College Womens Regionals 2024
94 Lehigh Loss 6-15 -29.75 105 8.34% Counts (Why) Apr 27th Ohio Valley D III College Womens Regionals 2024
159 Kenyon Loss 10-11 -27.47 389 8.34% Counts Apr 27th Ohio Valley D III College Womens Regionals 2024
203 Oberlin Win 14-7 0.66 424 8.34% Counts (Why) Apr 28th Ohio Valley 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.