(2) #21 Northeastern (8-14)

1907.17 (6)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
4 Texas Loss 8-13 -7.02 1 3.59% Counts Feb 3rd Florida Warm Up 2023
104 Florida State Win 13-3 1.41 17 3.59% Counts (Why) Feb 3rd Florida Warm Up 2023
11 Brown Win 15-12 17.41 50 3.59% Counts Feb 4th Florida Warm Up 2023
14 Carleton College Loss 11-13 -3.2 20 3.59% Counts Feb 4th Florida Warm Up 2023
19 Georgia Loss 8-9 -2.86 81 3.39% Counts Feb 4th Florida Warm Up 2023
8 Pittsburgh Loss 6-13 -13.1 17 3.59% Counts (Why) Feb 4th Florida Warm Up 2023
72 Auburn Win 13-6 7.09 54 3.59% Counts (Why) Feb 5th Florida Warm Up 2023
5 Vermont Loss 8-13 -7.19 7 3.59% Counts Feb 5th Florida Warm Up 2023
12 Minnesota Loss 10-12 -3.52 32 4.52% Counts Mar 4th Smoky Mountain Invite
8 Pittsburgh Loss 9-11 -0.06 17 4.52% Counts Mar 4th Smoky Mountain Invite
15 UCLA Loss 9-12 -10.61 3 4.52% Counts Mar 4th Smoky Mountain Invite
11 Brown Loss 9-15 -16.47 50 4.52% Counts Mar 5th Smoky Mountain Invite
3 Massachusetts Loss 9-13 -0.68 65 4.52% Counts Mar 5th Smoky Mountain Invite
20 North Carolina State Win 15-12 16.03 3 4.52% Counts Mar 5th Smoky Mountain Invite
30 Ohio State Win 15-13 6.77 6 4.52% Counts Mar 5th Smoky Mountain Invite
12 Minnesota Loss 9-13 -15.39 32 5.69% Counts Apr 1st Easterns 2023
1 North Carolina Loss 6-13 -6.91 30 5.69% Counts (Why) Apr 1st Easterns 2023
25 North Carolina-Wilmington Win 11-10 6.16 24 5.69% Counts Apr 1st Easterns 2023
30 Ohio State Win 12-7 27.13 6 5.69% Counts (Why) Apr 1st Easterns 2023
20 North Carolina State Loss 11-12 -5.24 3 5.69% Counts Apr 2nd Easterns 2023
25 North Carolina-Wilmington Loss 11-12 -8.93 24 5.69% Counts Apr 2nd Easterns 2023
27 South Carolina Win 15-11 19.45 73 5.69% Counts Apr 2nd Easterns 2023
**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.