(2) #45 Georgetown (10-10)

1696.69 (15)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
52 Appalachian State Win 15-14 2.52 6 3.88% Counts Jan 28th Carolina Kickoff
36 Penn State Loss 12-14 -5.66 9 3.88% Counts Jan 28th Carolina Kickoff
1 North Carolina Loss 8-15 5.3 30 3.88% Counts Jan 28th Carolina Kickoff
33 Duke Loss 13-14 -1.25 34 3.88% Counts Jan 28th Carolina Kickoff
52 Appalachian State Loss 7-11 -20.79 6 3.78% Counts Jan 29th Carolina Kickoff
20 North Carolina State Loss 12-15 -2.09 3 3.88% Counts Jan 29th Carolina Kickoff
36 Penn State Loss 11-15 -12.13 9 3.88% Counts Jan 29th Carolina Kickoff
85 Alabama Loss 12-13 -19.27 10 4.89% Counts Feb 25th Easterns Qualifier 2023
59 Cincinnati Win 13-11 5.7 70 4.89% Counts Feb 25th Easterns Qualifier 2023
41 William & Mary Win 13-12 7.57 18 4.89% Counts Feb 25th Easterns Qualifier 2023
24 North Carolina-Charlotte Loss 9-13 -11.35 14 4.89% Counts Feb 25th Easterns Qualifier 2023
69 Maryland Win 15-13 2.95 29 4.89% Counts Feb 26th Easterns Qualifier 2023
26 Georgia Tech Loss 8-15 -20.22 1 4.89% Counts Feb 26th Easterns Qualifier 2023
49 Notre Dame Win 11-7 20.67 27 4.76% Counts Feb 26th Easterns Qualifier 2023
106 Liberty Win 14-4 17.2 80 6.53% Counts (Why) Apr 1st Atlantic Coast Open 2023
71 Cornell Win 14-7 27.23 79 6.53% Counts (Why) Apr 1st Atlantic Coast Open 2023
172 East Carolina Win 15-7 -2.32 64 6.53% Counts (Why) Apr 1st Atlantic Coast Open 2023
70 Lehigh Win 11-6 24.8 77 6.18% Counts (Why) Apr 2nd Atlantic Coast Open 2023
63 Rutgers Win 14-10 18.92 39 6.53% Counts Apr 2nd Atlantic Coast Open 2023
67 Virginia Tech Loss 11-15 -36.64 99 6.53% Counts Apr 2nd Atlantic Coast Open 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.