(54) #343 Connecticut-B (8-10)

549.33 (139)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
188 Brown-B** Loss 0-4 0 97 0% Ignored (Why) Mar 2nd Philly Special 2024
310 Stevens Tech Loss 6-7 0.2 181 4.21% Counts Mar 2nd Philly Special 2024
397 SUNY-Albany-B Win 6-0 5.06 110 3.41% Counts (Why) Mar 2nd Philly Special 2024
359 Bentley Win 14-7 26.17 172 5.09% Counts (Why) Mar 3rd Philly Special 2024
401 Siena Win 15-3 4.87 174 5.09% Counts (Why) Mar 3rd Philly Special 2024
397 SUNY-Albany-B Win 15-4 7.7 110 5.09% Counts (Why) Mar 3rd Philly Special 2024
138 Tufts-B Loss 5-11 13.42 279 5.89% Counts (Why) Mar 30th New England Open 2024 Open Division
210 Northeastern-B Loss 1-13 -4.71 183 6.42% Counts (Why) Mar 30th New England Open 2024 Open Division
312 Western New England Loss 6-10 -23.27 161 5.89% Counts Mar 30th New England Open 2024 Open Division
317 Northeastern-C Loss 7-10 -18.3 148 6.07% Counts Mar 30th New England Open 2024 Open Division
199 Connecticut College Loss 6-13 -1.62 237 6.42% Counts (Why) Mar 31st New England Open 2024 Open Division
329 Harvard-B Win 9-7 20.66 231 5.89% Counts Mar 31st New England Open 2024 Open Division
317 Northeastern-C Loss 5-6 -0.95 148 4.88% Counts Mar 31st New England Open 2024 Open Division
411 RIT-B Win 15-7 -16.72 102 7.2% Counts (Why) Apr 13th Metro East Dev Mens Conferences 2024
331 Rutgers-B Loss 6-12 -40.14 308 7.01% Counts Apr 13th Metro East Dev Mens Conferences 2024
378 SUNY-Buffalo-B Win 14-8 22.39 276 7.2% Counts (Why) Apr 14th Metro East Dev Mens Conferences 2024
327 SUNY-Binghamton-B Loss 9-11 -14.31 359 7.2% Counts Apr 14th Metro East Dev Mens Conferences 2024
345 Cornell-B Win 10-8 18.66 35 7.01% Counts Apr 14th Metro East 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.