(4) #214 MIT (11-11)

1020.68 (281)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
223 Colby Win 8-6 10.63 276 3.89% Counts Feb 22nd Bring The Huckus 2025
179 Dickinson Loss 7-9 -5.8 213 4.16% Counts Feb 22nd Bring The Huckus 2025
113 West Chester Loss 7-8 10.36 200 4.03% Counts Feb 22nd Bring The Huckus 2025
267 SUNY-Geneseo Win 10-9 -4.08 329 4.53% Counts Feb 22nd Bring The Huckus 2025
161 Delaware Loss 5-11 -16.94 292 4.16% Counts (Why) Feb 23rd Bring The Huckus 2025
197 Haverford Loss 8-10 -9.45 337 4.41% Counts Feb 23rd Bring The Huckus 2025
235 Skidmore Win 13-12 1.49 297 4.53% Counts Feb 23rd Bring The Huckus 2025
230 Harvard Win 7-6 2.71 247 4.21% Counts Mar 9th MIT Invite
375 Harvard-B** Win 13-1 0 239 0% Ignored (Why) Mar 9th MIT Invite
294 Northeastern-C Loss 8-9 -21.11 369 4.81% Counts Mar 9th MIT Invite
364 MIT-B** Win 13-3 0 209 0% Ignored (Why) Mar 9th MIT Invite
133 Bates Win 11-10 26.03 266 5.71% Counts Mar 22nd PBR State Open
105 Boston University Loss 6-12 -8.85 282 5.56% Counts Mar 22nd PBR State Open
162 Brandeis Loss 9-10 5 256 5.71% Counts Mar 22nd PBR State Open
281 Worcester Polytechnic Win 13-7 18.22 350 5.71% Counts (Why) Mar 22nd PBR State Open
348 Bentley Win 15-1 2.27 264 5.71% Counts (Why) Mar 23rd PBR State Open
95 Bowdoin Loss 5-13 -8.51 276 5.71% Counts (Why) Mar 23rd PBR State Open
105 Boston University Loss 6-15 -12.46 282 6.79% Counts (Why) Apr 12th Metro Boston D I Mens Conferences 2025
16 Northeastern** Loss 0-15 0 161 0% Ignored (Why) Apr 12th Metro Boston D I Mens Conferences 2025
85 Boston College Loss 1-15 -7.61 244 6.79% Counts (Why) Apr 13th Metro Boston D I Mens Conferences 2025
230 Harvard Win 12-11 4.49 247 6.79% Counts Apr 13th Metro Boston D I Mens Conferences 2025
318 Massachusetts-Lowell Win 15-7 13.96 227 6.79% Counts (Why) Apr 13th Metro Boston D I 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.