(1) #159 George Mason (11-8)

997.77 (43)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
39 Cincinnati Loss 11-12 21.78 228 4.12% Counts Jan 25th Mid Atlantic Warm Up 2025
66 Dartmouth Loss 4-13 -6.25 18 4.12% Counts (Why) Jan 25th Mid Atlantic Warm Up 2025
157 Johns Hopkins Win 9-8 5.39 47 3.89% Counts Jan 25th Mid Atlantic Warm Up 2025
206 Christopher Newport Win 13-7 14.51 84 4.12% Counts (Why) Jan 26th Mid Atlantic Warm Up 2025
78 Richmond Loss 10-11 9.67 68 4.12% Counts Jan 26th Mid Atlantic Warm Up 2025
101 Yale Loss 8-14 -11.65 46 4.12% Counts Jan 26th Mid Atlantic Warm Up 2025
294 Maryland-Baltimore County Win 12-7 -4.23 87 5.19% Counts (Why) Feb 22nd Monument Melee 2025
272 Virginia Commonwealth Win 9-6 -3.28 44 4.61% Counts Feb 22nd Monument Melee 2025
157 Johns Hopkins Win 9-7 14.37 47 4.76% Counts Feb 22nd Monument Melee 2025
180 American Win 11-10 1.8 31 5.19% Counts Feb 23rd Monument Melee 2025
184 East Carolina Win 12-10 7.12 0 5.19% Counts Feb 23rd Monument Melee 2025
324 Villanova Win 10-9 -33.52 44 5.19% Counts Feb 23rd Monument Melee 2025
97 Duke Loss 11-12 10.53 42 6.54% Counts Mar 22nd Atlantic Coast Open 2025
105 Liberty Loss 8-10 -1.87 86 6.36% Counts Mar 22nd Atlantic Coast Open 2025
170 Massachusetts -B Win 11-9 14.78 70 6.54% Counts Mar 22nd Atlantic Coast Open 2025
276 Virginia Tech-B Win 11-7 -3.62 86 6.36% Counts Mar 22nd Atlantic Coast Open 2025
180 American Loss 11-15 -33.09 31 6.54% Counts Mar 23rd Atlantic Coast Open 2025
184 East Carolina Loss 9-10 -16.3 0 6.54% Counts Mar 23rd Atlantic Coast Open 2025
255 Wake Forest Win 15-5 13.2 9 6.54% Counts (Why) Mar 23rd Atlantic Coast Open 2025
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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.