(30) #174 Cedarville (14-7)

1067.46 (114)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
301 Salisbury Win 13-1 8.64 241 4.45% Counts (Why) Feb 23rd Oak Creek Challenge 2019
292 Navy Win 12-10 -5.88 33 4.45% Counts Feb 23rd Oak Creek Challenge 2019
84 Brandeis Loss 6-13 -10.96 115 4.45% Counts (Why) Feb 23rd Oak Creek Challenge 2019
158 Lehigh Win 13-8 25.95 18 4.45% Counts Feb 23rd Oak Creek Challenge 2019
171 RIT Loss 8-12 -19.86 45 4.45% Counts Feb 24th Oak Creek Challenge 2019
83 Rutgers Loss 11-15 -0.73 2 4.45% Counts Feb 24th Oak Creek Challenge 2019
157 Drexel Win 13-12 8.7 57 4.45% Counts Feb 24th Oak Creek Challenge 2019
148 Michigan-B Loss 10-13 -12.68 74 5.6% Counts Mar 23rd CWRUL Memorial 2019
380 Case Western Reserve-B** Win 13-1 0 60 0% Ignored (Why) Mar 23rd CWRUL Memorial 2019
348 Western Michigan Win 11-8 -12.71 176 5.6% Counts Mar 23rd CWRUL Memorial 2019
320 Ohio State-B Win 13-3 7.42 218 5.6% Counts (Why) Mar 23rd CWRUL Memorial 2019
158 Lehigh Loss 8-15 -29.86 18 5.6% Counts Mar 24th CWRUL Memorial 2019
87 Case Western Reserve Loss 9-13 -3.77 27 5.6% Counts Mar 24th CWRUL Memorial 2019
247 Xavier Win 13-12 -4.02 2 5.6% Counts Mar 24th CWRUL Memorial 2019
145 Dayton Loss 9-10 -0.16 12 5.6% Counts Mar 24th CWRUL Memorial 2019
389 Cornell-B** Win 13-2 0 34 0% Ignored (Why) Mar 30th I 85 Rodeo 2019
349 William & Mary-B Win 13-0 1.14 106 5.93% Counts (Why) Mar 30th I 85 Rodeo 2019
318 Virginia Tech-B Win 13-4 8.14 101 5.93% Counts (Why) Mar 30th I 85 Rodeo 2019
334 James Madison-B Win 15-5 5.03 44 5.93% Counts (Why) Mar 31st I 85 Rodeo 2019
260 South Carolina-B Win 15-9 17.38 5.93% Counts Mar 31st I 85 Rodeo 2019
279 Maryland-B Win 13-5 18.33 204 5.93% Counts (Why) Mar 31st I 85 Rodeo 2019
**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.