() #40 Wisconsin (11-11)

1628.01 (19)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
19 Georgia Loss 9-12 -2.98 91 3.56% Counts Jan 31st Florida Warm Up 2025
51 Purdue Win 13-6 19.47 185 3.56% Counts (Why) Jan 31st Florida Warm Up 2025
103 Texas A&M Win 13-8 4.01 133 3.56% Counts Jan 31st Florida Warm Up 2025
119 Central Florida Win 13-8 1.83 42 3.56% Counts Feb 1st Florida Warm Up 2025
51 Purdue Win 13-8 15.64 185 3.56% Counts Feb 1st Florida Warm Up 2025
20 Vermont Loss 8-13 -9.82 36 3.56% Counts Feb 1st Florida Warm Up 2025
16 Brown Loss 9-13 -4.68 74 3.56% Counts Feb 2nd Florida Warm Up 2025
31 Minnesota Win 11-9 12.35 33 3.56% Counts Feb 2nd Florida Warm Up 2025
35 Chicago Loss 8-12 -21.98 97 5.03% Counts Mar 15th Mens Centex 2025
66 Dartmouth Win 8-7 -2.38 18 4.47% Counts Mar 15th Mens Centex 2025
46 Middlebury Win 13-9 20.58 7 5.03% Counts Mar 15th Mens Centex 2025
17 Tufts Loss 8-11 -5.12 80 5.03% Counts Mar 15th Mens Centex 2025
35 Chicago Win 15-10 25.43 97 5.03% Counts Mar 16th Mens Centex 2025
13 Texas Loss 10-15 -5.87 15 5.03% Counts Mar 16th Mens Centex 2025
29 Utah Valley Win 15-11 26.78 30 5.03% Counts Mar 16th Mens Centex 2025
8 Brigham Young Loss 9-15 -4.11 13 5.33% Counts Mar 22nd Northwest Challenge 2025 mens
33 California-Santa Barbara Loss 10-14 -20.6 5 5.33% Counts Mar 22nd Northwest Challenge 2025 mens
109 Gonzaga Win 15-11 -1.64 34 5.33% Counts Mar 22nd Northwest Challenge 2025 mens
38 Utah State Loss 14-15 -6.78 44 5.33% Counts Mar 22nd Northwest Challenge 2025 mens
41 California-San Diego Loss 11-15 -21.83 4 5.33% Counts Mar 23rd Northwest Challenge 2025 mens
109 Gonzaga Win 11-3 9.75 34 4.89% Counts (Why) Mar 23rd Northwest Challenge 2025 mens
32 Virginia Loss 8-15 -28.88 9 5.33% Counts Mar 23rd Northwest Challenge 2025 mens
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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.