(17) #262 Brown-B (9-11)

829 (363)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
95 Bowdoin** Loss 1-4 0 276 0% Ignored (Why) Mar 1st Garden State 2025
197 Haverford Loss 7-10 -8.42 337 5.66% Counts Mar 1st Garden State 2025
210 Penn State-B Loss 5-7 -6.48 371 4.76% Counts Mar 1st Garden State 2025
152 Rhode Island Loss 4-9 -8.37 217 4.95% Counts (Why) Mar 1st Garden State 2025
352 Army Win 12-7 6.92 299 5.99% Counts (Why) Mar 2nd Garden State 2025
210 Penn State-B Loss 6-9 -12.36 371 5.32% Counts Mar 2nd Garden State 2025
312 Amherst Win 5-4 -3.43 289 4.37% Counts Mar 8th Grand Northeast Kickoff 2025
223 Colby Win 8-4 38.15 276 5.04% Counts (Why) Mar 8th Grand Northeast Kickoff 2025
215 Northeastern-B Loss 4-7 -15.69 249 4.83% Counts Mar 8th Grand Northeast Kickoff 2025
133 Bates Loss 5-8 2.37 266 5.25% Counts Mar 9th Grand Northeast Kickoff 2025
385 New Hampshire** Win 10-1 0 475 0% Ignored (Why) Mar 9th Grand Northeast Kickoff 2025
399 Middlebury-B** Win 13-0 0 812 0% Ignored (Why) Mar 9th Grand Northeast Kickoff 2025
223 Colby Loss 5-15 -30.23 276 6.34% Counts (Why) Mar 9th Grand Northeast Kickoff 2025
411 Boston University-B** Win 13-5 0 97 0% Ignored (Why) Apr 12th New England Dev Mens Conferences 2025
375 Harvard-B Win 11-4 3.24 239 7.77% Counts (Why) Apr 12th New England Dev Mens Conferences 2025
111 Vermont-B Loss 7-12 6.37 224 8.47% Counts Apr 12th New England Dev Mens Conferences 2025
215 Northeastern-B Loss 7-10 -17.67 249 8.01% Counts Apr 12th New England Dev Mens Conferences 2025
364 MIT-B Win 9-3 10.47 209 7% Counts (Why) Apr 13th New England Dev Mens Conferences 2025
206 Tufts-B Win 9-7 41.51 4 7.77% Counts Apr 13th New England Dev Mens Conferences 2025
215 Northeastern-B Loss 9-11 -5.78 249 8.47% Counts Apr 13th New England Dev 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.