(2) #39 Florida (14-8)

1741.42 (16)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
11 Brown Loss 11-13 4.94 50 4.52% Counts Feb 3rd Florida Warm Up 2023
72 Auburn Loss 10-13 -27.05 54 4.52% Counts Feb 3rd Florida Warm Up 2023
14 Carleton College Loss 6-13 -13.79 20 4.52% Counts (Why) Feb 3rd Florida Warm Up 2023
3 Massachusetts Loss 8-13 3.49 65 4.52% Counts Feb 4th Florida Warm Up 2023
8 Pittsburgh Loss 6-11 -5.93 17 4.27% Counts Feb 4th Florida Warm Up 2023
67 Virginia Tech Win 13-9 10.9 99 4.52% Counts Feb 4th Florida Warm Up 2023
79 Texas A&M Win 13-9 7.13 7 4.52% Counts Feb 5th Florida Warm Up 2023
147 Connecticut Win 13-9 -7.59 8 4.52% Counts Feb 5th Florida Warm Up 2023
87 Tennessee-Chattanooga Win 13-7 14.34 20 5.37% Counts (Why) Feb 25th Mardi Gras XXXV
331 Texas Tech** Win 13-1 0 11 0% Ignored (Why) Feb 25th Mardi Gras XXXV
336 Trinity** Win 13-5 0 10 0% Ignored (Why) Feb 25th Mardi Gras XXXV
259 Jacksonville State** Win 13-4 0 7 0% Ignored (Why) Feb 25th Mardi Gras XXXV
43 Alabama-Huntsville Loss 8-13 -30.34 7 5.37% Counts Feb 26th Mardi Gras XXXV
88 Central Florida Win 7-3 11.87 18 3.9% Counts (Why) Feb 26th Mardi Gras XXXV
89 Mississippi State Win 11-5 15.17 7 4.93% Counts (Why) Feb 26th Mardi Gras XXXV
60 Middlebury Win 13-10 11.24 27 6.39% Counts Mar 18th Centex 2023
47 Colorado State Win 12-10 9.82 10 6.39% Counts Mar 18th Centex 2023
86 Dartmouth Win 12-9 2.79 64 6.39% Counts Mar 18th Centex 2023
13 Tufts Loss 10-13 -0.09 37 6.39% Counts Mar 18th Centex 2023
60 Middlebury Win 15-14 -2.62 27 6.39% Counts Mar 19th Centex 2023
79 Texas A&M Loss 14-15 -26.8 7 6.39% Counts Mar 19th Centex 2023
54 Northwestern Win 11-7 22.65 16 6.22% Counts Mar 19th Centex 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.