(10) #26 North Carolina-Wilmington (15-11)

1780.98 (203)

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# Opponent Result Effect % of Ranking Status Date Event
102 Georgetown Win 10-5 3.93 2.66% Jan 26th Carolina Kickoff 2019
81 Georgia Tech Loss 8-9 -13.35 2.83% Jan 26th Carolina Kickoff 2019
61 Tennessee Win 10-4 10.01 2.61% Jan 26th Carolina Kickoff 2019
69 Emory Win 15-9 7.49 2.99% Jan 27th Carolina Kickoff 2019
52 Notre Dame Win 15-10 9.22 2.99% Jan 27th Carolina Kickoff 2019
1 North Carolina Loss 7-15 -4.59 2.99% Jan 27th Carolina Kickoff 2019
131 Chicago Win 12-6 2.19 3.27% Feb 9th Queen City Tune Up 2019 Men
24 Auburn Win 12-9 12.54 3.36% Feb 9th Queen City Tune Up 2019 Men
64 Ohio Win 10-6 8.09 3.08% Feb 9th Queen City Tune Up 2019 Men
44 Virginia Win 9-7 5.39 3.08% Feb 9th Queen City Tune Up 2019 Men
47 Maryland Win 15-14 0.01 3.36% Feb 10th Queen City Tune Up 2019 Men
1 North Carolina Loss 10-15 -0.09 3.36% Feb 10th Queen City Tune Up 2019 Men
11 North Carolina State Loss 11-15 -4.67 3.36% Feb 10th Queen City Tune Up 2019 Men
57 Carnegie Mellon Win 13-4 17.94 4.23% Mar 9th Classic City Invite 2019
22 Georgia Win 13-10 16.85 4.23% Mar 9th Classic City Invite 2019
81 Georgia Tech Win 13-8 7.17 4.23% Mar 9th Classic City Invite 2019
28 Northeastern Win 13-10 14.26 4.23% Mar 9th Classic City Invite 2019
9 Massachusetts Loss 7-10 -4.38 4% Mar 10th Classic City Invite 2019
11 North Carolina State Win 9-8 15.48 4% Mar 10th Classic City Invite 2019
4 Pittsburgh Loss 4-13 -10.38 5.03% Mar 30th Easterns 2019 Men
49 Northwestern Loss 11-13 -19.7 5.03% Mar 30th Easterns 2019 Men
32 William & Mary Loss 8-11 -21.17 5.03% Mar 30th Easterns 2019 Men
20 Tufts Loss 11-13 -7.71 5.03% Mar 30th Easterns 2019 Men
43 Harvard Loss 9-12 -24.03 5.03% Mar 31st Easterns 2019 Men
28 Northeastern Loss 8-14 -28.64 5.03% Mar 31st Easterns 2019 Men
44 Virginia Win 12-10 6.8 5.03% Mar 31st Easterns 2019 Men
**Blowout Eligible

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.