(3) #28 Pittsburgh (8-14)

1764.73 (11)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
8 Brigham Young Loss 7-10 -2.94 13 3.39% Counts Jan 31st Florida Warm Up 2025
16 Brown Loss 11-13 -2.75 74 3.59% Counts Jan 31st Florida Warm Up 2025
134 South Florida Win 12-9 -11.42 68 3.59% Counts Jan 31st Florida Warm Up 2025
4 Carleton College Loss 9-13 0.74 64 3.59% Counts Feb 1st Florida Warm Up 2025
31 Minnesota Win 13-9 13.67 33 3.59% Counts Feb 1st Florida Warm Up 2025
51 Purdue Win 13-6 14.55 185 3.59% Counts (Why) Feb 1st Florida Warm Up 2025
19 Georgia Loss 11-13 -3.76 91 3.59% Counts Feb 2nd Florida Warm Up 2025
20 Vermont Win 13-12 8.12 36 3.59% Counts Feb 2nd Florida Warm Up 2025
3 North Carolina Loss 6-13 -7.51 5 4.52% Counts (Why) Mar 1st Smoky Mountain Invite 2025
18 Northeastern Loss 9-10 0.28 27 4.52% Counts Mar 1st Smoky Mountain Invite 2025
5 Oregon Loss 10-13 4.77 4 4.52% Counts Mar 1st Smoky Mountain Invite 2025
1 Massachusetts Loss 8-15 -3.33 108 4.52% Counts Mar 1st Smoky Mountain Invite 2025
16 Brown Loss 12-15 -6.89 74 4.52% Counts Mar 2nd Smoky Mountain Invite 2025
19 Georgia Win 15-10 27.52 91 4.52% Counts Mar 2nd Smoky Mountain Invite 2025
25 Penn State Win 15-14 8.88 56 4.52% Counts Mar 2nd Smoky Mountain Invite 2025
6 Cal Poly-SLO Loss 9-13 -3.36 18 5.69% Counts Mar 29th Easterns 2025
4 Carleton College Loss 2-13 -9.75 64 5.69% Counts (Why) Mar 29th Easterns 2025
21 Georgia Tech Loss 11-13 -8.38 27 5.69% Counts Mar 29th Easterns 2025
37 North Carolina-Wilmington Loss 8-13 -37.79 84 5.69% Counts Mar 29th Easterns 2025
64 James Madison Win 13-7 15.11 70 5.69% Counts (Why) Mar 30th Easterns 2025
36 Michigan Loss 11-13 -20.58 13 5.69% Counts Mar 30th Easterns 2025
49 North Carolina State Win 15-6 24.16 21 5.69% Counts (Why) Mar 30th Easterns 2025
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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.