(2) #323 Cincinnati -B (6-11)

590.77 (11)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
167 Dayton** Loss 3-15 0 19 0% Ignored (Why) Mar 1st The Dayton Ultimate Disc Experience DUDE
348 Wright State Loss 7-15 -52.64 15 6.68% Counts (Why) Mar 1st The Dayton Ultimate Disc Experience DUDE
272 Wooster Loss 7-9 -5.07 14 6.13% Counts Mar 1st The Dayton Ultimate Disc Experience DUDE
218 Miami (Ohio) Loss 8-13 -6.5 16 6.68% Counts Mar 2nd The Dayton Ultimate Disc Experience DUDE
365 SUNY-Buffalo-B Win 13-8 19.84 2 6.68% Counts Mar 2nd The Dayton Ultimate Disc Experience DUDE
263 Toledo Win 9-8 27.08 17 7.09% Counts Mar 15th Spring Spook
153 Kentucky Loss 6-8 24.58 17 6.44% Counts Mar 15th Spring Spook
244 Kent State Loss 7-15 -24.29 16 7.5% Counts (Why) Mar 16th Spring Spook
263 Toledo Loss 8-15 -27.17 17 7.5% Counts Mar 16th Spring Spook
348 Wright State Win 13-7 34.23 15 7.5% Counts (Why) Mar 16th Spring Spook
328 Case Western Reserve-B Loss 8-13 -53.35 9 9.45% Counts Apr 12th Ohio Valley Dev Mens Conferences 2025
142 Pittsburgh-B** Loss 3-13 0 13 0% Ignored (Why) Apr 12th Ohio Valley Dev Mens Conferences 2025
384 West Chester-B Win 13-7 19.96 2 9.45% Counts (Why) Apr 12th Ohio Valley Dev Mens Conferences 2025
209 Penn State-B Loss 3-13 -17.33 3 9.45% Counts (Why) Apr 12th Ohio Valley Dev Mens Conferences 2025
412 Carnegie Mellon-B** Win 13-4 0 2 0% Ignored (Why) Apr 13th Ohio Valley Dev Mens Conferences 2025
328 Case Western Reserve-B Win 13-5 61.03 9 9.45% Counts (Why) Apr 13th Ohio Valley Dev Mens Conferences 2025
142 Pittsburgh-B** Loss 5-15 0 13 0% Ignored (Why) Apr 13th Ohio Valley Dev Mens Conferences 2025
**Blowout Eligible. Learn more about how this works here.

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.