(6) #43 Georgia Tech (8-14)

1555.59 (30)

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# Opponent Result Effect % of Ranking Status Date Event
3 Ohio State Loss 10-12 24.17 4.01% Jan 19th Florida Winter Classic 2019
38 Florida Loss 7-8 -2.56 3.56% Jan 19th Florida Winter Classic 2019
51 Florida State Loss 5-6 -5.41 3.05% Jan 19th Florida Winter Classic 2019
112 Central Florida Win 9-2 3.38 3.31% Jan 19th Florida Winter Classic 2019
3 Ohio State Loss 6-12 9.66 3.9% Jan 20th Florida Winter Classic 2019
20 North Carolina-Wilmington Loss 8-9 11.01 3.79% Jan 20th Florida Winter Classic 2019
49 Emory Win 8-5 14.27 3.31% Jan 20th Florida Winter Classic 2019
112 Central Florida Win 13-2 4.12 4.01% Jan 20th Florida Winter Classic 2019
1 North Carolina** Loss 3-13 0 0% Ignored Feb 9th Queen City Tune Up 2019 Women
20 North Carolina-Wilmington Loss 5-13 -9.77 4.76% Feb 9th Queen City Tune Up 2019 Women
32 Brigham Young Loss 8-10 -5.24 4.64% Feb 9th Queen City Tune Up 2019 Women
91 Case Western Reserve Win 11-6 9.14 4.51% Feb 9th Queen City Tune Up 2019 Women
69 Notre Dame Win 15-7 18.67 4.76% Feb 10th Queen City Tune Up 2019 Women
45 Virginia Loss 10-11 -6.75 4.76% Feb 10th Queen City Tune Up 2019 Women
41 Harvard Loss 7-9 -12.22 4.37% Feb 10th Queen City Tune Up 2019 Women
90 Colorado State Win 8-5 6.79 5.57% Mar 23rd Womens College Centex 2019
59 Duke Loss 7-8 -14.94 5.99% Mar 23rd Womens College Centex 2019
30 Utah Loss 4-10 -24.8 5.88% Mar 23rd Womens College Centex 2019
113 Oklahoma Win 15-4 7.01 6.74% Mar 24th Womens College Centex 2019
51 Florida State Loss 7-8 -10.96 5.99% Mar 24th Womens College Centex 2019
45 Virginia Loss 9-10 -9.74 6.74% Mar 24th Womens College Centex 2019
72 Texas-Dallas Win 9-8 -7.09 6.37% Mar 24th Womens College Centex 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.