(9) #44 Emory (12-10)

1608.19 (42)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
119 Central Florida Win 13-5 6.62 42 3.68% Counts (Why) Jan 31st Florida Warm Up 2025
56 Cornell Loss 9-10 -8 82 3.68% Counts Jan 31st Florida Warm Up 2025
103 Texas A&M Win 10-6 4.5 133 3.38% Counts (Why) Jan 31st Florida Warm Up 2025
8 Brigham Young Loss 8-13 -1.29 13 3.68% Counts Feb 1st Florida Warm Up 2025
16 Brown Win 13-11 20.66 74 3.68% Counts Feb 1st Florida Warm Up 2025
62 Tulane Win 12-11 -0.8 4 3.68% Counts Feb 1st Florida Warm Up 2025
16 Brown Loss 8-13 -7.06 74 3.68% Counts Feb 2nd Florida Warm Up 2025
31 Minnesota Loss 11-13 -4.72 33 3.68% Counts Feb 2nd Florida Warm Up 2025
102 Syracuse Win 12-8 4.27 140 4.38% Counts Feb 22nd Easterns Qualifier 2025
88 Georgetown Win 13-6 13.76 78 4.38% Counts (Why) Feb 22nd Easterns Qualifier 2025
87 Temple Win 12-7 10.22 179 4.38% Counts (Why) Feb 22nd Easterns Qualifier 2025
37 North Carolina-Wilmington Win 11-6 24.78 84 4.14% Counts (Why) Feb 22nd Easterns Qualifier 2025
32 Virginia Loss 13-15 -6.53 9 4.38% Counts Feb 23rd Easterns Qualifier 2025
67 Indiana Win 14-10 10.7 51 4.38% Counts Feb 23rd Easterns Qualifier 2025
27 South Carolina Loss 13-15 -1.96 50 4.38% Counts Feb 23rd Easterns Qualifier 2025
50 Colorado State Loss 10-12 -17.67 35 5.84% Counts Mar 29th Huck Finn 2025
74 Oklahoma Christian Loss 10-14 -39.03 26 5.84% Counts Mar 29th Huck Finn 2025
15 Washington University Loss 8-11 -1.38 70 5.84% Counts Mar 29th Huck Finn 2025
63 Notre Dame Loss 10-14 -33.98 277 5.84% Counts Mar 29th Huck Finn 2025
72 Southern Illinois-Edwardsville Win 11-8 9.56 146 5.84% Counts Mar 30th Huck Finn 2025
103 Texas A&M Win 13-11 -8.61 133 5.84% Counts Mar 30th Huck Finn 2025
67 Indiana Win 11-4 24.66 51 5.36% Counts (Why) Mar 30th Huck Finn 2025
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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.