(21) #158 Case Western Reserve (2-16)

834.5 (65)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
61 Florida Loss 6-9 16.69 56 6.49% Counts Feb 10th Queen City Tune Up 2024
8 Tufts** Loss 0-15 0 164 0% Ignored (Why) Feb 10th Queen City Tune Up 2024
40 Minnesota** Loss 1-15 0 105 0% Ignored (Why) Feb 10th Queen City Tune Up 2024
21 Northeastern** Loss 2-15 0 142 0% Ignored (Why) Feb 10th Queen City Tune Up 2024
24 Ohio State** Loss 5-15 0 188 0% Ignored (Why) Feb 11th Queen City Tune Up 2024
104 Appalachian State Loss 2-6 -17.65 24 6.91% Counts (Why) Mar 23rd Needle in a Ho Stack 2024
120 Charleston Win 7-6 34.82 165 8.55% Counts Mar 23rd 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
74 Davidson Loss 3-10 -5.89 239 9.03% Counts (Why) Mar 24th Needle in a Ho Stack 2024
89 Virginia Tech Loss 1-10 -12.66 10 9.03% Counts (Why) Mar 24th Needle in a Ho Stack 2024
49 Ohio Loss 3-6 22.19 191 8.96% Counts Apr 20th Ohio D I Womens Conferences 2024
24 Ohio State** Loss 0-9 0 188 0% Ignored (Why) Apr 20th Ohio D I Womens Conferences 2024
80 Cincinnati Loss 1-11 -10.85 143 11.94% Counts (Why) Apr 20th Ohio D I Womens Conferences 2024
49 Ohio** Loss 3-11 0 191 0% Ignored (Why) Apr 27th Ohio Valley D I College Womens Regionals 2024
108 West Chester Loss 2-11 -36.95 119 12.65% Counts (Why) Apr 27th Ohio Valley D I College Womens Regionals 2024
64 Penn State Loss 6-10 21.2 27 12.65% Counts Apr 27th Ohio Valley D I College Womens Regionals 2024
16 Pennsylvania** Loss 3-13 0 26 0% Ignored (Why) Apr 27th Ohio Valley D I College Womens Regionals 2024
80 Cincinnati Loss 5-15 -12.8 143 13.79% Counts (Why) Apr 28th Ohio Valley D I 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.