(4) #162 Brandeis (14-8)

1228.22 (256)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
81 Rochester Loss 10-11 11.92 215 5.58% Counts Mar 1st D III River City Showdown 2025
33 Elon** Loss 4-13 0 238 0% Ignored (Why) Mar 1st D III River City Showdown 2025
241 Xavier Win 13-6 16.83 269 5.58% Counts (Why) Mar 1st D III River City Showdown 2025
102 North Carolina-Asheville Loss 11-13 0.17 158 5.58% Counts Mar 1st D III River City Showdown 2025
170 Messiah Loss 10-11 -9.34 213 5.58% Counts Mar 2nd D III River City Showdown 2025
282 Navy Win 11-3 7.32 375 5.12% Counts (Why) Mar 2nd D III River City Showdown 2025
169 Michigan Tech Loss 7-11 -28.18 141 5.43% Counts Mar 2nd D III River City Showdown 2025
348 Bentley** Win 15-3 0 264 0% Ignored (Why) Mar 22nd PBR State Open
281 Worcester Polytechnic Win 15-2 9.64 350 6.63% Counts (Why) Mar 22nd PBR State Open
214 MIT Win 10-9 -5.86 281 6.63% Counts Mar 22nd PBR State Open
133 Bates Win 10-8 24.83 266 6.45% Counts Mar 23rd PBR State Open
105 Boston University Loss 10-11 6.85 282 6.63% Counts Mar 23rd PBR State Open
95 Bowdoin Loss 5-9 -16.72 276 5.69% Counts Mar 23rd PBR State Open
281 Worcester Polytechnic Win 13-5 10.26 350 7.02% Counts (Why) Mar 29th New England Open 2025
382 Wentworth** Win 13-3 0 272 0% Ignored (Why) Mar 29th New England Open 2025
362 Western New England** Win 13-1 0 250 0% Ignored (Why) Mar 29th New England Open 2025
363 Clark Win 13-8 -27.46 91 7.02% Counts Mar 29th New England Open 2025
312 Amherst Win 13-7 -3.15 289 7.02% Counts (Why) Mar 30th New England Open 2025
95 Bowdoin Loss 7-15 -26.3 276 7.02% Counts (Why) Mar 30th New England Open 2025
215 Northeastern-B Win 15-7 29.28 249 7.02% Counts (Why) Mar 30th New England Open 2025
382 Wentworth** Win 15-4 0 272 0% Ignored (Why) Apr 13th Metro Boston D III Mens Conferences 2025
342 Stonehill** Win 15-5 0 0% Ignored (Why) Apr 13th Metro Boston D III Mens Conferences 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.