(6) #115 Florida State (8-14)

1085.76 (24)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
10 Minnesota** Loss 4-13 0 1 0% Ignored (Why) Feb 3rd Florida Warm Up 2023
96 Texas A&M Win 10-8 17.38 75 4.41% Counts Feb 3rd Florida Warm Up 2023
26 Northeastern Loss 3-13 -3.54 1 4.53% Counts (Why) Feb 3rd Florida Warm Up 2023
3 Brigham Young** Loss 4-13 0 42 0% Ignored (Why) Feb 4th Florida Warm Up 2023
67 Auburn Loss 8-13 -12.04 2 4.53% Counts Feb 4th Florida Warm Up 2023
124 Illinois Win 8-7 3.99 86 4.02% Counts Feb 4th Florida Warm Up 2023
20 Brown Loss 6-15 -0.63 6 4.53% Counts (Why) Feb 5th Florida Warm Up 2023
109 Temple Win 10-9 7.23 28 4.53% Counts Feb 5th Florida Warm Up 2023
70 Maryland Loss 11-12 6.33 26 5.38% Counts Feb 25th Easterns Qualifier 2023
41 Duke Loss 9-12 1.57 28 5.38% Counts Feb 25th Easterns Qualifier 2023
79 Notre Dame Loss 10-13 -7.1 40 5.38% Counts Feb 25th Easterns Qualifier 2023
25 North Carolina-Wilmington Loss 7-13 -1.02 26 5.38% Counts Feb 25th Easterns Qualifier 2023
77 Cincinnati Loss 8-15 -20.05 31 5.38% Counts Feb 26th Easterns Qualifier 2023
109 Temple Loss 7-13 -30.16 28 5.38% Counts Feb 26th Easterns Qualifier 2023
164 George Washington Win 14-13 -4.3 44 5.38% Counts Feb 26th Easterns Qualifier 2023
102 Central Florida Win 13-8 36.34 14 6.04% Counts Mar 11th Tally Classic XVII
141 LSU Win 13-8 25.09 28 6.04% Counts Mar 11th Tally Classic XVII
79 Notre Dame Loss 6-10 -17.19 40 5.55% Counts Mar 11th Tally Classic XVII
247 Georgia Southern** Win 13-5 0 18 0% Ignored (Why) Mar 11th Tally Classic XVII
167 Minnesota-Duluth Win 14-8 20.84 22 6.04% Counts (Why) Mar 12th Tally Classic XVII
141 LSU Loss 11-13 -21.54 28 6.04% Counts Mar 12th Tally Classic XVII
97 Harvard Loss 12-13 -0.97 37 6.04% Counts Mar 12th Tally Classic XVII
**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.