(2) #41 Florida (10-9)

1571.02 (4)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
9 Brown Loss 8-13 -2.27 49 5.12% Counts Feb 2nd Florida Warm Up 2024
10 Carleton College Loss 11-13 11.35 87 5.12% Counts Feb 2nd Florida Warm Up 2024
37 Texas A&M Win 13-5 33.43 2 5.12% Counts (Why) Feb 2nd Florida Warm Up 2024
12 Alabama-Huntsville Loss 3-13 -9.56 3 5.12% Counts (Why) Feb 3rd Florida Warm Up 2024
17 Brigham Young Loss 9-13 -6.16 39 5.12% Counts Feb 3rd Florida Warm Up 2024
27 Georgia Tech Loss 11-15 -11.44 17 5.12% Counts Feb 3rd Florida Warm Up 2024
101 Cornell Win 13-12 -11.94 51 5.12% Counts Feb 4th Florida Warm Up 2024
139 LSU Win 9-6 -3.88 13 5.41% Counts Feb 24th Mardi Gras XXXVI college
261 Texas Tech** Win 13-5 0 14 0% Ignored (Why) Feb 24th Mardi Gras XXXVI college
230 Texas State** Win 13-1 0 3 0% Ignored (Why) Feb 24th Mardi Gras XXXVI college
91 Indiana Win 12-9 2.93 100 6.08% Counts Feb 24th Mardi Gras XXXVI college
110 Arizona State Win 12-7 9.21 94 6.08% Counts (Why) Feb 25th Mardi Gras XXXVI college
82 Central Florida Loss 9-10 -23.24 52 6.08% Counts Feb 25th Mardi Gras XXXVI college
37 Texas A&M Loss 6-11 -32.17 2 5.76% Counts Feb 25th Mardi Gras XXXVI college
17 Brigham Young Loss 9-13 -8.9 39 7.24% Counts Mar 16th College Mens Centex Tier 1
67 Chicago Win 8-6 7.72 40 6.21% Counts Mar 16th College Mens Centex Tier 1
40 Illinois Loss 11-12 -9.08 18 7.24% Counts Mar 16th College Mens Centex Tier 1
20 Northeastern Win 11-10 29.98 65 7.24% Counts Mar 16th College Mens Centex Tier 1
48 Missouri Win 10-7 24.5 13 6.84% Counts Mar 17th College Mens Centex Tier 1
**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.