(17) #80 Cincinnati (14-7)

1354.51 (143)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
31 Alabama-Huntsville Loss 2-11 -5.61 131 4.38% Counts (Why) Feb 10th College Womens Huckfest
33 Union (Tennessee) Loss 4-9 -6.31 83 3.95% Counts (Why) Feb 10th College Womens Huckfest
211 Vanderbilt** Win 11-1 0 195 0% Ignored (Why) Feb 10th College Womens Huckfest
202 Alabama** Win 11-2 0 8 0% Ignored (Why) Feb 11th College Womens Huckfest
233 Alabama-Birmingham** Win 11-1 0 249 0% Ignored (Why) Feb 11th College Womens Huckfest
129 Illinois Win 10-6 8.21 50 4.38% Counts (Why) Feb 11th College Womens Huckfest
129 Illinois Win 7-5 0.44 50 3.8% Counts Feb 11th College Womens Huckfest
49 Ohio Win 9-5 40.06 191 4.88% Counts (Why) Mar 2nd Huckleberry Flick
243 Dayton** Win 15-0 0 367 0% Ignored (Why) Mar 2nd Huckleberry Flick
159 Kenyon Win 14-3 4.29 389 5.68% Counts (Why) Mar 2nd Huckleberry Flick
224 Butler** Win 15-0 0 343 0% Ignored (Why) Mar 3rd Huckleberry Flick
134 Franciscan Win 10-9 -14.97 329 5.68% Counts Mar 3rd Huckleberry Flick
49 Ohio Loss 4-10 -18.16 191 4.96% Counts (Why) Mar 3rd Huckleberry Flick
158 Case Western Reserve Win 11-1 6.78 65 7.81% Counts (Why) Apr 20th Ohio D I Womens Conferences 2024
24 Ohio State Win 7-3 75 188 6.18% Counts (Why) Apr 20th Ohio D I Womens Conferences 2024
49 Ohio Loss 4-8 -22.68 191 6.77% Counts Apr 20th Ohio D I Womens Conferences 2024
112 Carnegie Mellon Loss 6-9 -55.67 157 8.01% Counts Apr 27th Ohio Valley D I College Womens Regionals 2024
22 Pittsburgh Loss 4-9 -1.64 29 7.46% Counts (Why) Apr 27th Ohio Valley D I College Womens Regionals 2024
121 Temple Win 8-7 -13.16 444 8.01% Counts Apr 27th Ohio Valley D I College Womens Regionals 2024
24 Ohio State Loss 4-13 -6.01 188 9.02% Counts (Why) Apr 27th Ohio Valley D I College Womens Regionals 2024
158 Case Western Reserve Win 15-5 7.93 65 9.02% 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.