(3) #169 APEX (7-15)

737.9 (5)

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# Opponent Result Effect % of Ranking Status Date Event
166 Piedmont United Loss 7-10 -14.37 3.83% Counts Jun 22nd Summer Glazed Daze 2019
77 Tyrannis Loss 3-13 -3.25 4.05% Counts (Why) Jun 22nd Summer Glazed Daze 2019
173 LORD Loss 6-13 -25.86 4.05% Counts (Why) Jun 22nd Summer Glazed Daze 2019
200 District Cocktails Loss 10-12 -14.83 4.05% Counts Jun 23rd Summer Glazed Daze 2019
231 Stormborn Win 9-6 1.28 3.6% Counts Jun 23rd Summer Glazed Daze 2019
258 ThunderCats Win 13-6 -2.96 4.05% Counts (Why) Jun 23rd Summer Glazed Daze 2019
80 Ant Madness Loss 5-13 -4.71 4.05% Counts (Why) Jun 23rd Summer Glazed Daze 2019
115 NC Galaxy Loss 4-13 -15.26 4.76% Counts (Why) Jul 13th Hometown Mix Up 2019
236 RnB Win 12-9 -3.59 4.76% Counts Jul 13th Hometown Mix Up 2019
148 Rowdy Loss 9-10 -0.15 4.76% Counts Jul 13th Hometown Mix Up 2019
35 Superlame Loss 7-13 15.66 4.76% Counts Jul 13th Hometown Mix Up 2019
166 Piedmont United Loss 6-13 -28.5 4.76% Counts (Why) Jul 14th Hometown Mix Up 2019
84 FlyTrap Loss 5-13 -6 4.76% Counts (Why) Jul 14th Hometown Mix Up 2019
155 Possum Loss 6-7 -1.84 3.93% Counts Jul 14th Hometown Mix Up 2019
246 Rampage Win 11-6 1.15 4.5% Counts (Why) Jul 14th Hometown Mix Up 2019
73 Bexar Loss 8-13 3.37 5.89% Counts Aug 10th HoDown ShowDown 23 GOAT
148 Rowdy Loss 12-13 -0.19 5.89% Counts Aug 10th HoDown ShowDown 23 GOAT
55 Malice in Wonderland** Loss 1-13 0 0% Ignored (Why) Aug 10th HoDown ShowDown 23 GOAT
79 Auburn HeyDay Loss 9-13 5.6 5.89% Counts Aug 10th HoDown ShowDown 23 GOAT
137 BATL Cows Win 15-13 25.7 5.89% Counts Aug 11th HoDown ShowDown 23 GOAT
110 sKNO cone Win 13-12 27.4 5.89% Counts Aug 11th HoDown ShowDown 23 GOAT
138 Goose Lee Win 13-8 43.12 5.89% Counts Aug 11th HoDown ShowDown 23 GOAT
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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.