() #1 North Carolina (21-0) AC 1

2345.34 (38)

Click on column to sort  • 
# Opponent Result Effect % of Ranking Status Date Event
14 Florida Win 13-8 1.65 4.19% Jan 20th Carolina Kickoff 2018 NC Ultimate
69 Carleton College-GoP Win 13-7 -14.81 4.19% Jan 20th Carolina Kickoff 2018 NC Ultimate
12 North Carolina State Win 13-11 -8.65 4.19% Jan 21st Carolina Kickoff 2018 NC Ultimate
37 Central Florida Win 13-7 -6.7 4.19% Jan 21st Carolina Kickoff 2018 NC Ultimate
91 Penn State Win 13-6 -16.26 4.19% Jan 21st Carolina Kickoff 2018 NC Ultimate
12 North Carolina State Win 10-5 6.43 4.18% Feb 3rd Queen City Tune Up 2018 College Open
9 Georgia Win 11-7 3.4 4.58% Feb 3rd Queen City Tune Up 2018 College Open
22 Tufts Win 11-5 0.22 4.32% Feb 3rd Queen City Tune Up 2018 College Open
28 Carnegie Mellon Win 11-5 -1.2 4.32% Feb 3rd Queen City Tune Up 2018 College Open
62 Vermont Win 11-6 -15.51 4.45% Feb 3rd Queen City Tune Up 2018 College Open
7 Pittsburgh Win 13-8 8.72 5.93% Mar 3rd Stanford Invite 2018
32 California Win 13-7 -5.8 5.93% Mar 3rd Stanford Invite 2018
18 Brigham Young Win 13-10 -10.33 5.93% Mar 3rd Stanford Invite 2018
6 Brown Win 13-7 16.32 5.93% Mar 4th Stanford Invite 2018
3 Oregon Win 15-14 -1.99 5.93% Mar 4th Stanford Invite 2018
2 Carleton College Win 13-8 23.89 5.93% Mar 4th Stanford Invite 2018
12 North Carolina State Win 13-9 -0.56 6.66% Mar 20th Atlantic Coast Showcase ACS NCSU vs UNC
33 Maryland** Win 15-6 0 0% Ignored Mar 31st Easterns 2018
14 Florida Win 15-8 8.58 7.47% Mar 31st Easterns 2018
36 Michigan** Win 15-5 0 0% Ignored Mar 31st Easterns 2018
13 Wisconsin Win 15-7 13.87 7.47% 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.