(3) #9 Brown (14-8) NE 3

2025.07 (49)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
41 Florida Win 13-8 1.56 4 3.58% Counts Feb 2nd Florida Warm Up 2024
42 Michigan Win 13-9 -1.49 170 3.58% Counts Feb 2nd Florida Warm Up 2024
10 Carleton College Loss 11-12 -5.19 87 3.58% Counts Feb 2nd Florida Warm Up 2024
27 Georgia Tech Win 13-8 7.84 17 3.58% Counts Feb 3rd Florida Warm Up 2024
37 Texas A&M Win 13-8 2.3 2 3.58% Counts Feb 3rd Florida Warm Up 2024
20 Northeastern Win 15-14 -2.59 65 3.58% Counts Feb 3rd Florida Warm Up 2024
11 Minnesota Win 15-14 3.79 102 3.58% Counts Feb 4th Florida Warm Up 2024
2 Georgia Loss 10-15 -7.64 80 3.58% Counts Feb 4th Florida Warm Up 2024
92 Tennessee Win 13-7 -9.47 16 4.51% Counts (Why) Mar 2nd Smoky Mountain Invite 2024
15 California Win 13-7 21.57 35 4.51% Counts (Why) Mar 2nd Smoky Mountain Invite 2024
14 Texas Win 13-10 11.32 30 4.51% Counts Mar 2nd Smoky Mountain Invite 2024
26 Utah State Win 15-14 -6.17 88 4.51% Counts Mar 2nd Smoky Mountain Invite 2024
7 Pittsburgh Loss 12-15 -10.98 86 4.51% Counts Mar 3rd Smoky Mountain Invite 2024
8 Vermont Loss 14-15 -5.27 36 4.51% Counts Mar 3rd Smoky Mountain Invite 2024
10 Carleton College Win 15-14 5.21 87 4.51% Counts Mar 3rd Smoky Mountain Invite 2024
12 Alabama-Huntsville Win 13-11 11.9 3 5.68% Counts Mar 30th Easterns 2024
16 Penn State Win 13-11 7.53 59 5.68% Counts Mar 30th Easterns 2024
36 North Carolina-Charlotte Win 13-6 11.64 20 5.68% Counts (Why) Mar 30th Easterns 2024
5 Cal Poly-SLO Loss 9-10 1.48 12 5.68% Counts Mar 30th Easterns 2024
4 Massachusetts Loss 8-15 -21.39 47 5.68% Counts Mar 31st Easterns 2024
1 North Carolina Loss 12-15 -2.23 18 5.68% Counts Mar 31st Easterns 2024
10 Carleton College Loss 13-15 -13.79 87 5.68% Counts Mar 31st Easterns 2024
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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.