(34) #329 Harvard-B (5-11)

600.16 (231)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
359 Bentley Win 10-4 27.59 172 5.73% Counts (Why) Mar 23rd Ocean State Invite
259 Brandeis Loss 4-13 -20.03 185 6.56% Counts (Why) Mar 23rd Ocean State Invite
317 Northeastern-C Loss 5-6 -3.64 148 4.99% Counts Mar 23rd Ocean State Invite
80 Bates** Loss 5-13 0 274 0% Ignored (Why) Mar 30th New England Open 2024 Open Division
225 Colby Loss 6-8 8.57 240 5.97% Counts Mar 30th New England Open 2024 Open Division
189 Worcester Polytechnic Institute Loss 4-11 -2.77 269 6.38% Counts (Why) Mar 30th New England Open 2024 Open Division
385 New Hampshire Win 13-3 18.53 316 6.95% Counts (Why) Mar 30th New England Open 2024 Open Division
343 Connecticut-B Loss 7-9 -22.49 139 6.38% Counts Mar 31st New England Open 2024 Open Division
312 Western New England Loss 10-13 -19.02 161 6.95% Counts Mar 31st New England Open 2024 Open Division
398 Tufts-C Win 12-5 6.84 7.49% Counts (Why) Apr 13th Metro Boston Dev Mens Conferences 2024
317 Northeastern-C Loss 7-10 -26.61 148 7.38% Counts Apr 13th Metro Boston Dev Mens Conferences 2024
138 Tufts-B Loss 5-10 14.13 279 6.93% Counts Apr 13th Metro Boston Dev Mens Conferences 2024
377 MIT-B Win 10-5 20.96 6.93% Counts (Why) Apr 14th Metro Boston Dev Mens Conferences 2024
210 Northeastern-B Loss 5-12 -9.67 183 7.49% Counts (Why) Apr 14th Metro Boston Dev Mens Conferences 2024
317 Northeastern-C Win 10-9 15.29 148 7.8% Counts Apr 14th Metro Boston Dev Mens Conferences 2024
210 Northeastern-B Loss 2-8 -7.72 183 6.07% Counts (Why) Apr 14th Metro Boston Dev Mens Conferences 2024
**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.