(20) #102 Georgetown (9-15)

1351.18 (98)

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# Opponent Result Effect % of Ranking Status Date Event
25 South Carolina Loss 9-12 2.91 3.13% Jan 25th Carolina Kickoff 2019
61 Tennessee Win 11-7 21.05 3.05% Jan 26th Carolina Kickoff 2019
81 Georgia Tech Loss 10-11 -0.93 3.13% Jan 26th Carolina Kickoff 2019
26 North Carolina-Wilmington Loss 5-10 -4.12 2.78% Jan 26th Carolina Kickoff 2019
25 South Carolina Loss 13-14 10.03 3.13% Jan 27th Carolina Kickoff 2019
73 Temple Win 14-11 14.32 3.13% Jan 27th Carolina Kickoff 2019
155 Elon Win 13-8 11.39 3.72% Feb 16th Easterns Qualifier 2019
87 Case Western Reserve Win 8-7 6.72 3.31% Feb 16th Easterns Qualifier 2019
61 Tennessee Loss 8-13 -11.34 3.72% Feb 16th Easterns Qualifier 2019
53 Indiana Win 15-11 25.39 3.72% Feb 17th Easterns Qualifier 2019
39 Vermont Loss 7-15 -9.49 3.72% Feb 17th Easterns Qualifier 2019
61 Tennessee Win 15-13 16.13 3.72% Feb 17th Easterns Qualifier 2019
188 East Carolina Loss 12-13 -21.94 4.69% Mar 16th Oak Creek Invite 2019
101 Connecticut Loss 10-13 -15.9 4.69% Mar 16th Oak Creek Invite 2019
110 Williams Win 14-12 9.13 4.69% Mar 16th Oak Creek Invite 2019
32 William & Mary Loss 6-13 -10.06 4.69% Mar 16th Oak Creek Invite 2019
150 Cornell Win 15-10 13.8 4.69% Mar 17th Oak Creek Invite 2019
54 Virginia Tech Loss 11-15 -5.56 4.69% Mar 17th Oak Creek Invite 2019
66 Penn State Loss 8-13 -17.34 5.26% Mar 30th Atlantic Coast Open 2019
35 Middlebury Loss 6-13 -12.49 5.26% Mar 30th Atlantic Coast Open 2019
62 Duke Loss 9-13 -12.16 5.26% Mar 30th Atlantic Coast Open 2019
137 North Carolina-B Loss 8-11 -26.88 5.26% Mar 30th Atlantic Coast Open 2019
120 James Madison Loss 11-12 -10.75 5.26% Mar 31st Atlantic Coast Open 2019
101 Connecticut Win 14-9 26.61 5.26% Mar 31st Atlantic Coast Open 2019
**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.