(45) #317 Northeastern-C (7-11)

655.82 (148)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
359 Bentley Win 9-6 14.05 172 6.09% Counts Mar 23rd Ocean State Invite
259 Brandeis Loss 4-10 -21.71 185 5.99% Counts (Why) Mar 23rd Ocean State Invite
329 Harvard-B Win 6-5 3.82 231 5.22% Counts Mar 23rd Ocean State Invite
331 Rutgers-B Loss 6-9 -31.01 308 6.09% Counts Mar 23rd Ocean State Invite
112 Boston College** Loss 3-10 0 266 0% Ignored (Why) Mar 24th Ocean State Invite
275 Central Connecticut State Win 9-6 39.65 93 6.09% Counts Mar 24th Ocean State Invite
259 Brandeis Loss 7-10 -9.63 185 6.87% Counts Mar 30th New England Open 2024 Open Division
142 Bryant** Loss 3-8 0 266 0% Ignored (Why) Mar 30th New England Open 2024 Open Division
199 Connecticut College Loss 10-11 27.01 237 7.26% Counts Mar 30th New England Open 2024 Open Division
343 Connecticut-B Win 10-7 20.89 139 6.87% Counts Mar 30th New England Open 2024 Open Division
343 Connecticut-B Win 6-5 1.08 139 5.53% Counts Mar 31st New England Open 2024 Open Division
293 Maine Loss 7-9 -13.53 212 6.66% Counts Mar 31st New England Open 2024 Open Division
329 Harvard-B Win 10-7 27.9 231 7.71% Counts Apr 13th Metro Boston Dev Mens Conferences 2024
377 MIT-B Loss 5-6 -31.29 6.2% Counts Apr 13th Metro Boston Dev Mens Conferences 2024
138 Tufts-B** Loss 5-13 0 279 0% Ignored (Why) Apr 13th Metro Boston Dev Mens Conferences 2024
329 Harvard-B Loss 9-10 -16.03 231 8.15% Counts Apr 14th Metro Boston Dev Mens Conferences 2024
210 Northeastern-B Loss 4-10 -13.43 183 7.12% Counts (Why) Apr 14th Metro Boston Dev Mens Conferences 2024
398 Tufts-C Win 13-6 2.56 8.15% 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.