(5) #16 North Carolina-Wilmington (19-2) AC 4

1884.51 (39)

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# Opponent Result Effect % of Ranking Status Date Event
69 Carleton College-GoP Win 13-7 5.33 4.17% Jan 20th Carolina Kickoff 2018 NC Ultimate
37 Central Florida Win 12-7 11.78 4.17% Jan 20th Carolina Kickoff 2018 NC Ultimate
12 North Carolina State Win 11-8 17.4 4.17% Jan 21st Carolina Kickoff 2018 NC Ultimate
14 Florida Win 11-10 5.54 4.17% Jan 21st Carolina Kickoff 2018 NC Ultimate
91 Penn State Win 13-7 2.04 4.17% Jan 21st Carolina Kickoff 2018 NC Ultimate
30 Auburn Win 10-9 -2.47 4.68% Feb 3rd Queen City Tune Up 2018 College Open
64 North Carolina-Charlotte Win 11-7 2.14 4.55% Feb 3rd Queen City Tune Up 2018 College Open
91 Penn State Win 10-8 -11.83 4.55% Feb 3rd Queen City Tune Up 2018 College Open
133 Case Western Reserve** Win 11-4 0 0% Ignored Feb 3rd Queen City Tune Up 2018 College Open
151 George Mason Win 11-6 -10.23 4.43% Feb 3rd Queen City Tune Up 2018 College Open
124 Indiana Win 13-8 -10.8 6.25% Mar 10th Tally Classic XIII
168 South Florida** Win 13-5 0 0% Ignored Mar 10th Tally Classic XIII
28 Carnegie Mellon Win 14-12 3.67 6.25% Mar 10th Tally Classic XIII
88 Alabama-Huntsville Win 13-6 6.92 6.25% Mar 10th Tally Classic XIII
224 Georgia Southern** Win 13-3 0 0% Ignored Mar 10th Tally Classic XIII
52 Harvard Win 15-12 -3.2 6.25% Mar 11th Tally Classic XIII
9 Georgia Win 15-14 12.64 6.25% Mar 11th Tally Classic XIII
7 Pittsburgh Loss 12-13 -1.77 7.43% Mar 31st Easterns 2018
37 Central Florida Win 15-10 16.36 7.43% Mar 31st Easterns 2018
65 California-Santa Barbara Win 12-11 -23.84 7.43% Mar 31st Easterns 2018
2 Carleton College Loss 5-15 -20.57 7.43% Mar 31st Easterns 2018
**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.