(17) #383 Florida State-B (4-14)

278.94 (356)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
73 Ave Maria** Loss 0-13 0 255 0% Ignored (Why) Feb 24th Florida Warm Up 2024 Weekend 2
412 Central Florida-B Win 13-5 1.68 726 5.41% Counts (Why) Feb 24th Florida Warm Up 2024 Weekend 2
206 Embry-Riddle** Loss 5-13 0 369 0% Ignored (Why) Feb 24th Florida Warm Up 2024 Weekend 2
309 Florida Gulf Coast Loss 5-13 -11.3 392 5.41% Counts (Why) Feb 24th Florida Warm Up 2024 Weekend 2
248 Florida-B Loss 6-13 4.86 463 5.41% Counts (Why) Feb 25th Florida Warm Up 2024 Weekend 2
287 Florida Tech Loss 6-13 -5.21 398 5.41% Counts (Why) Feb 25th Florida Warm Up 2024 Weekend 2
287 Florida Tech Loss 2-13 -6.27 398 6.44% Counts (Why) Mar 16th Tally Classic XVIII
384 Notre Dame-B Loss 9-10 -10 244 6.44% Counts Mar 16th Tally Classic XVIII
406 South Florida-B Win 10-4 16.1 430 5.62% Counts (Why) Mar 16th Tally Classic XVIII
309 Florida Gulf Coast Loss 8-11 2.53 392 6.44% Counts Mar 17th Tally Classic XVIII
309 Florida Gulf Coast Loss 10-12 11.3 392 6.44% Counts Mar 17th Tally Classic XVIII
406 South Florida-B Win 13-4 18.59 430 6.44% Counts (Why) Mar 17th Tally Classic XVIII
303 Alabama-B Loss 7-15 -14.81 190 8.11% Counts (Why) Apr 13th Southeast Dev Mens Conferences 2024
388 Ave Maria-B Loss 13-14 -15.68 788 8.11% Counts Apr 13th Southeast Dev Mens Conferences 2024
261 Georgia Tech-B Loss 7-15 2.24 276 8.11% Counts (Why) Apr 13th Southeast Dev Mens Conferences 2024
303 Alabama-B Loss 10-14 2.95 190 8.11% Counts Apr 14th Southeast Dev Mens Conferences 2024
412 Central Florida-B Win 15-6 2.59 726 8.11% Counts (Why) Apr 14th Southeast Dev Mens Conferences 2024
224 Georgia-B** Loss 4-15 0 183 0% Ignored (Why) Apr 14th Southeast Dev Mens Conferences 2024
**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.