() #191 DCVIII (7-13)

735.58 (78)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
130 Diesel Loss 10-13 -1.02 151 4.22% Counts Jul 8th Heavyweights 2023
143 STL Moonar Loss 11-12 4.67 67 4.22% Counts Jul 8th Heavyweights 2023
73 Knights of Ni Loss 6-13 2.21 81 4.22% Counts (Why) Jul 8th Heavyweights 2023
135 Trident II Loss 11-12 7.18 100 4.22% Counts Jul 8th Heavyweights 2023
253 Scoop Win 13-7 -5.7 73 4.22% Counts (Why) Jul 9th Heavyweights 2023
156 NOMAD Loss 10-11 1.29 75 4.22% Counts Jul 9th Heavyweights 2023
170 Rubicon Rapids Loss 10-11 -1.87 77 5.81% Counts Aug 19th Cooler Classic 34
179 Timber Win 12-7 36.7 73 5.81% Counts (Why) Aug 19th Cooler Classic 34
106 MKE Loss 6-13 -9.44 61 5.81% Counts (Why) Aug 19th Cooler Classic 34
204 Loaded Panda Loss 8-13 -36.33 71 5.81% Counts Aug 19th Cooler Classic 34
179 Timber Loss 9-13 -21.18 73 5.81% Counts Aug 20th Cooler Classic 34
148 Minnesota Superior A Win 14-13 19.66 78 5.81% Counts Aug 20th Cooler Classic 34
241 Middleton High School Win 13-8 0.64 75 5.81% Counts Aug 20th Cooler Classic 34
245 Milwaukee Revival Win 13-3 4.74 78 6.81% Counts (Why) Sep 9th 2023 Mens Northwest Plains Sectional Championship
73 Knights of Ni** Loss 5-13 0 81 0% Ignored (Why) Sep 9th 2023 Mens Northwest Plains Sectional Championship
127 Nomads Loss 9-13 -7.54 73 6.81% Counts Sep 9th 2023 Mens Northwest Plains Sectional Championship
29 Mallard** Loss 4-13 0 75 0% Ignored (Why) Sep 9th 2023 Mens Northwest Plains Sectional Championship
175 Dinkytown Doughboys Win 15-14 15.32 77 6.81% Counts Sep 10th 2023 Mens Northwest Plains Sectional Championship
219 THE BODY Win 14-12 1.24 76 6.81% Counts Sep 10th 2023 Mens Northwest Plains Sectional Championship
156 NOMAD Loss 12-15 -10.68 75 6.81% Counts Sep 10th 2023 Mens Northwest Plains Sectional Championship
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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.