(49) #168 Kenyon (6-11)

1252.36 (493)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
232 Butler Win 12-10 -0.19 342 4.51% Counts Mar 2nd FCS D III Tune Up 2024
129 Michigan Tech Loss 6-13 -22.2 208 4.51% Counts (Why) Mar 2nd FCS D III Tune Up 2024
179 North Carolina-Asheville Loss 10-13 -17.99 263 4.51% Counts Mar 2nd FCS D III Tune Up 2024
65 Richmond Loss 3-13 -8.37 311 4.51% Counts (Why) Mar 2nd FCS D III Tune Up 2024
88 Berry Loss 6-13 -14.2 379 4.51% Counts (Why) Mar 3rd FCS D III Tune Up 2024
84 Elon Loss 11-13 4.5 334 4.51% Counts Mar 3rd FCS D III Tune Up 2024
52 Whitman Loss 7-13 -2.51 320 4.51% Counts Mar 3rd FCS D III Tune Up 2024
86 Cedarville Loss 5-13 -20.21 200 6.38% Counts (Why) Apr 13th Ohio D III Mens Conferences 2024
68 Franciscan Loss 9-12 4.27 164 6.38% Counts Apr 13th Ohio D III Mens Conferences 2024
173 Xavier Win 13-4 39.57 260 6.38% Counts (Why) Apr 13th Ohio D III Mens Conferences 2024
123 Oberlin Win 12-8 39.87 244 6.38% Counts Apr 13th Ohio D III Mens Conferences 2024
68 Franciscan Loss 4-13 -14.79 164 7.16% Counts (Why) Apr 27th Ohio Valley D III College Mens Regionals 2024
174 Grove City Win 11-10 7.53 7.16% Counts Apr 27th Ohio Valley D III College Mens Regionals 2024
234 Haverford Win 13-9 12.77 163 7.16% Counts Apr 27th Ohio Valley D III College Mens Regionals 2024
198 Messiah Loss 12-13 -19.14 297 7.16% Counts Apr 27th Ohio Valley D III College Mens Regionals 2024
68 Franciscan Loss 5-13 -14.79 164 7.16% Counts (Why) Apr 28th Ohio Valley D III College Mens Regionals 2024
171 Scranton Win 11-8 27.09 475 7.16% Counts Apr 28th Ohio Valley D III College Mens 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.