(5) #312 Amherst (8-11)

628.94 (289)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
399 Middlebury-B Win 12-0 3.01 812 5.01% Counts (Why) Mar 8th Grand Northeast Kickoff 2025
385 New Hampshire Win 8-1 7.35 475 4.06% Counts (Why) Mar 8th Grand Northeast Kickoff 2025
262 Brown-B Loss 4-5 2.8 363 3.59% Counts Mar 8th Grand Northeast Kickoff 2025
223 Colby Loss 2-8 -10.42 276 4.06% Counts (Why) Mar 8th Grand Northeast Kickoff 2025
385 New Hampshire Win 9-3 7.83 475 4.32% Counts (Why) Mar 9th Grand Northeast Kickoff 2025
215 Northeastern-B Loss 4-8 -7.7 249 4.15% Counts Mar 9th Grand Northeast Kickoff 2025
133 Bates Loss 7-9 20.98 266 4.79% Counts Mar 9th Grand Northeast Kickoff 2025
382 Wentworth Win 8-7 -15.41 272 5.51% Counts Mar 29th New England Open 2025
362 Western New England Win 11-10 -8.93 250 6.21% Counts Mar 29th New England Open 2025
363 Clark Win 8-5 10.46 91 5.13% Counts (Why) Mar 29th New England Open 2025
281 Worcester Polytechnic Loss 8-9 0.63 350 5.87% Counts Mar 29th New England Open 2025
162 Brandeis Loss 7-13 2.76 256 6.21% Counts Mar 30th New England Open 2025
215 Northeastern-B Loss 9-12 2.74 249 6.21% Counts Mar 30th New England Open 2025
294 Northeastern-C Loss 11-13 -8.58 369 6.21% Counts Mar 30th New England Open 2025
363 Clark Win 15-8 24.26 91 7.38% Counts (Why) Apr 19th South New England D III Mens Conferences 2025
362 Western New England Win 15-9 20.36 250 7.38% Counts Apr 19th South New England D III Mens Conferences 2025
76 Williams** Loss 3-15 0 207 0% Ignored (Why) Apr 19th South New England D III Mens Conferences 2025
306 Bryant Loss 6-9 -26.76 6.56% Counts Apr 20th South New England D III Mens Conferences 2025
287 Roger Williams Loss 8-12 -25.6 312 7.38% Counts Apr 20th South New England D III Mens Conferences 2025
**Blowout Eligible. Learn more about how this works here.

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.