(3) #25 North Carolina-Wilmington (16-8)

1884.26 (24)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
90 Chicago Win 12-7 2.52 6 3.47% Counts (Why) Feb 11th Queen City Tune Up1
36 Penn State Win 13-12 0.66 9 3.47% Counts Feb 11th Queen City Tune Up1
20 North Carolina State Loss 9-11 -6.77 3 3.47% Counts Feb 11th Queen City Tune Up1
22 Washington University Win 14-13 5.26 53 3.47% Counts Feb 11th Queen City Tune Up1
50 Case Western Reserve Win 10-9 -4.29 15 3.47% Counts Feb 12th Queen City Tune Up1
27 South Carolina Win 11-8 11.86 73 3.47% Counts Feb 12th Queen City Tune Up1
69 Maryland Win 12-9 0.04 29 3.9% Counts Feb 25th Easterns Qualifier 2023
104 Florida State Win 13-7 0.74 17 3.9% Counts (Why) Feb 25th Easterns Qualifier 2023
33 Duke Loss 11-12 -8.87 34 3.9% Counts Feb 25th Easterns Qualifier 2023
49 Notre Dame Loss 11-12 -14.85 27 3.9% Counts Feb 25th Easterns Qualifier 2023
71 Cornell Win 11-5 8.14 79 3.58% Counts (Why) Feb 26th Easterns Qualifier 2023
56 James Madison Win 12-10 -1.89 21 3.9% Counts Feb 26th Easterns Qualifier 2023
24 North Carolina-Charlotte Loss 8-12 -17.49 14 3.9% Counts Feb 26th Easterns Qualifier 2023
134 Carnegie Mellon Win 9-7 -14.53 18 3.79% Counts Mar 4th Fish Bowl
36 Penn State Win 11-6 17.9 9 3.91% Counts (Why) Mar 4th Fish Bowl
36 Penn State Win 11-6 17.9 9 3.91% Counts (Why) Mar 5th Fish Bowl
97 Delaware Win 15-6 5.81 34 4.13% Counts (Why) Mar 5th Fish Bowl
12 Minnesota Loss 11-13 -2.32 32 5.21% Counts Apr 1st Easterns 2023
1 North Carolina Loss 8-11 7.84 30 5.21% Counts Apr 1st Easterns 2023
30 Ohio State Win 11-9 11.03 6 5.21% Counts Apr 1st Easterns 2023
21 Northeastern Loss 10-11 -5.61 6 5.21% Counts Apr 1st Easterns 2023
18 California Loss 6-14 -28.7 37 5.21% Counts (Why) Apr 2nd Easterns 2023
23 Wisconsin Win 14-13 7.43 11 5.21% Counts Apr 2nd Easterns 2023
21 Northeastern Win 12-11 8.12 6 5.21% Counts Apr 2nd Easterns 2023
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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.