(9) #244 College of New Jersey (10-7)

902.61 (247)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
298 Maryland-Baltimore County Win 13-2 25.1 301 5.94% Counts (Why) Mar 8th First State Invite
277 Salisbury Loss 7-9 -23.18 95 5.45% Counts Mar 8th First State Invite
161 Delaware Loss 2-13 -17.18 292 5.94% Counts (Why) Mar 8th First State Invite
352 Army Win 13-4 8.71 299 7.06% Counts (Why) Mar 29th Northeast Classic 2025
210 Penn State-B Loss 10-11 -0.01 371 7.06% Counts Mar 29th Northeast Classic 2025
197 Haverford Win 11-9 32.29 337 7.06% Counts Mar 29th Northeast Classic 2025
328 SUNY-Cortland Win 13-4 21.21 390 7.06% Counts (Why) Mar 29th Northeast Classic 2025
192 Vassar Loss 7-11 -20.4 300 6.87% Counts Mar 30th Northeast Classic 2025
212 SUNY-Albany Win 13-7 51.53 326 7.06% Counts (Why) Mar 30th Northeast Classic 2025
197 Haverford Loss 5-13 -32.22 337 7.06% Counts (Why) Mar 30th Northeast Classic 2025
- Manhattan** Win 15-1 0 0% Ignored (Why) Apr 13th Metro NY D III Mens Conferences 2025
389 Stevens Tech** Win 15-4 0 204 0% Ignored (Why) Apr 13th Metro NY D III Mens Conferences 2025
347 Rensselaer Polytech Win 12-5 14.81 85 8.54% Counts (Why) Apr 26th Metro East D III College Mens Regionals 2025
192 Vassar Loss 5-8 -20.9 300 7.36% Counts Apr 26th Metro East D III College Mens Regionals 2025
328 SUNY-Cortland Loss 8-10 -55.3 390 8.66% Counts Apr 26th Metro East D III College Mens Regionals 2025
389 Stevens Tech** Win 12-2 0 204 0% Ignored (Why) Apr 26th Metro East D III College Mens Regionals 2025
347 Rensselaer Polytech Win 14-5 15.49 85 8.9% Counts (Why) Apr 27th Metro East D III College Mens Regionals 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.