() #81 North Carolina-Charlotte (7-11)

1322.26 (26)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
54 Carleton College-CHOP Win 13-11 24.68 41 5.26% Counts Feb 1st Carolina Kickoff mens 2025
49 North Carolina State Loss 11-13 0.76 21 5.26% Counts Feb 1st Carolina Kickoff mens 2025
87 Temple Win 13-8 26.91 179 5.26% Counts Feb 1st Carolina Kickoff mens 2025
27 South Carolina Loss 12-15 8.71 50 5.26% Counts Feb 2nd Carolina Kickoff mens 2025
32 Virginia Loss 10-12 6.63 9 5.26% Counts Feb 2nd Carolina Kickoff mens 2025
37 North Carolina-Wilmington Loss 8-14 -12.39 84 5.26% Counts Feb 2nd Carolina Kickoff mens 2025
69 Auburn Loss 9-12 -15.01 76 5.9% Counts Feb 15th Queen City Tune Up 2025
3 North Carolina** Loss 2-13 0 5 0% Ignored (Why) Feb 15th Queen City Tune Up 2025
101 Yale Loss 7-13 -38.72 46 5.9% Counts Feb 15th Queen City Tune Up 2025
60 Michigan State Loss 6-8 -7.28 88 5.07% Counts Feb 16th Queen City Tune Up 2025
52 William & Mary Loss 7-10 -9.49 101 5.58% Counts Feb 16th Queen City Tune Up 2025
180 American Win 11-6 10.48 31 7.45% Counts (Why) Mar 22nd Atlantic Coast Open 2025
171 Dickinson Win 11-7 8.13 75 7.67% Counts Mar 22nd Atlantic Coast Open 2025
184 East Carolina Win 12-6 12.19 0 7.67% Counts (Why) Mar 22nd Atlantic Coast Open 2025
357 George Washington-B** Win 15-1 0 246 0% Ignored (Why) Mar 22nd Atlantic Coast Open 2025
105 Liberty Loss 6-11 -51.22 86 7.45% Counts Mar 23rd Atlantic Coast Open 2025
115 Vermont-B Win 15-11 21.69 35 7.88% Counts Mar 23rd Atlantic Coast Open 2025
43 Virginia Tech Loss 12-13 14.11 19 7.88% Counts Mar 23rd Atlantic Coast Open 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.