(8) #225 Brandeis (9-11)

730.24 (57)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
103 Bowdoin Loss 2-9 -4.9 5 4.11% Counts (Why) Mar 2nd Philly Special 2024
127 College of New Jersey Loss 2-7 -6.95 80 3.61% Counts (Why) Mar 2nd Philly Special 2024
213 SUNY-Albany Loss 3-7 -21.1 111 3.61% Counts (Why) Mar 2nd Philly Special 2024
71 Penn State-B** Loss 2-13 0 67 0% Ignored (Why) Mar 3rd Philly Special 2024
213 SUNY-Albany Loss 8-10 -11.53 111 4.84% Counts Mar 3rd Philly Special 2024
277 Stevens Tech Win 12-7 15.05 21 4.97% Counts (Why) Mar 3rd Philly Special 2024
143 Brown-B Loss 6-13 -13.82 28 4.97% Counts (Why) Mar 3rd Philly Special 2024
315 Bentley Win 12-5 9.12 22 5.67% Counts (Why) Mar 23rd Ocean State Invite
295 Harvard-B Win 13-4 15.03 55 5.91% Counts (Why) Mar 23rd Ocean State Invite
272 Northeastern-C Win 10-4 20.56 47 5.16% Counts (Why) Mar 23rd Ocean State Invite
312 Rutgers-B Win 8-5 0.6 32 4.89% Counts (Why) Mar 23rd Ocean State Invite
181 Northeastern-B Loss 3-10 -23.53 70 5.16% Counts (Why) Mar 24th Ocean State Invite
182 Worcester Polytechnic Institute Loss 6-9 -14.3 194 5.25% Counts Mar 24th Ocean State Invite
183 Connecticut College Win 10-7 34.5 357 5.92% Counts Mar 30th New England Open 2024 Open Division
141 Bryant Loss 4-12 -16.36 141 6.01% Counts (Why) Mar 30th New England Open 2024 Open Division
272 Northeastern-C Win 10-7 10.54 47 5.92% Counts Mar 30th New England Open 2024 Open Division
269 Western New England Win 10-9 -6.21 96 6.26% Counts Mar 30th New England Open 2024 Open Division
86 Bates Loss 5-13 -0.88 87 6.26% Counts (Why) Mar 31st New England Open 2024 Open Division
181 Northeastern-B Win 8-7 17.25 70 5.56% Counts Mar 31st New England Open 2024 Open Division
138 Tufts-B Loss 7-10 -2.21 69 5.92% Counts Mar 31st New England Open 2024 Open Division
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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.