(1) #85 Dayton (11-8)

1243.31 (6)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
286 Oberlin-B** Win 13-0 0 148 0% Ignored (Why) Mar 2nd 2nd Annual 7th Annual Bens Bar Mitzvah
245 Ohio State-B** Win 13-2 0 130 0% Ignored (Why) Mar 2nd 2nd Annual 7th Annual Bens Bar Mitzvah
228 Xavier** Win 13-3 0 167 0% Ignored (Why) Mar 2nd 2nd Annual 7th Annual Bens Bar Mitzvah
140 Cincinnati Win 12-5 17.69 7 6.16% Counts (Why) Mar 2nd 2nd Annual 7th Annual Bens Bar Mitzvah
284 Miami (Ohio)** Win 13-0 0 117 0% Ignored (Why) Mar 3rd 2nd Annual 7th Annual Bens Bar Mitzvah
140 Cincinnati Win 9-7 -3.2 7 5.89% Counts Mar 3rd 2nd Annual 7th Annual Bens Bar Mitzvah
111 Michigan State Loss 8-9 -24.13 33 7.22% Counts Mar 23rd CWRUL Memorial 2019
75 Purdue Loss 4-9 -37.42 12 6.31% Counts (Why) Mar 23rd CWRUL Memorial 2019
94 Carnegie Mellon Win 9-8 5.17 126 7.22% Counts Mar 23rd CWRUL Memorial 2019
151 Kentucky Win 10-3 15.63 44 6.67% Counts (Why) Mar 23rd CWRUL Memorial 2019
79 Ball State Win 10-9 12.81 19 7.63% Counts Mar 24th CWRUL Memorial 2019
58 Penn State Loss 5-10 -26.64 2 6.78% Counts Mar 24th CWRUL Memorial 2019
91 Case Western Reserve Win 10-4 39.96 21 6.67% Counts (Why) Mar 24th CWRUL Memorial 2019
28 North Carolina State Loss 4-13 -6.13 57 8.09% Counts (Why) Mar 30th I 85 Rodeo 2019
36 Vanderbilt Loss 4-12 -14.31 85 7.76% Counts (Why) Mar 30th I 85 Rodeo 2019
105 Liberty Win 11-2 35.58 15 7.42% Counts (Why) Mar 30th I 85 Rodeo 2019
46 Middlebury Loss 8-11 -6.2 5 8.09% Counts Mar 31st I 85 Rodeo 2019
26 Georgia** Loss 1-15 0 55 0% Ignored (Why) Mar 31st I 85 Rodeo 2019
82 Georgetown Loss 9-10 -8.96 112 8.09% Counts Mar 31st I 85 Rodeo 2019
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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.