() #43 Virginia Tech (14-8)

1612.2 (19)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
4 Carleton College Loss 2-13 -0.39 64 4.21% Counts (Why) Jan 31st Florida Warm Up 2025
47 McGill Win 10-9 4.59 43 4.21% Counts Jan 31st Florida Warm Up 2025
13 Texas Loss 6-13 -10.62 15 4.21% Counts (Why) Jan 31st Florida Warm Up 2025
79 Florida Win 11-10 -6.13 41 4.21% Counts Feb 1st Florida Warm Up 2025
134 South Florida Win 13-7 2.54 68 4.21% Counts (Why) Feb 1st Florida Warm Up 2025
1 Massachusetts** Loss 4-13 0 108 0% Ignored (Why) Feb 1st Florida Warm Up 2025
119 Central Florida Win 13-7 5.58 42 4.21% Counts (Why) Feb 2nd Florida Warm Up 2025
56 Cornell Win 13-7 20.65 82 4.21% Counts (Why) Feb 2nd Florida Warm Up 2025
9 California-Santa Cruz Loss 7-13 -8.86 0 5.63% Counts Mar 8th Stanford Invite 2025 Mens
85 Southern California Win 12-11 -10.14 140 5.63% Counts Mar 8th Stanford Invite 2025 Mens
55 UCLA Loss 9-10 -12.04 5 5.63% Counts Mar 8th Stanford Invite 2025 Mens
53 Whitman Win 13-9 21.14 11 5.63% Counts Mar 8th Stanford Invite 2025 Mens
12 British Columbia Loss 8-13 -8.13 11 5.63% Counts Mar 9th Stanford Invite 2025 Mens
14 California Loss 9-10 13.83 5 5.63% Counts Mar 9th Stanford Invite 2025 Mens
42 Stanford Loss 7-9 -14.93 27 5.16% Counts Mar 9th Stanford Invite 2025 Mens
247 George Washington** Win 15-4 0 38 0% Ignored (Why) Mar 22nd Atlantic Coast Open 2025
113 Lehigh Win 14-8 8.63 4 6.32% Counts (Why) Mar 22nd Atlantic Coast Open 2025
138 RIT Win 13-9 -6.79 21 6.32% Counts Mar 22nd Atlantic Coast Open 2025
255 Wake Forest** Win 15-1 0 9 0% Ignored (Why) Mar 22nd Atlantic Coast Open 2025
96 Appalachian State Win 14-9 9.17 68 6.32% Counts Mar 23rd Atlantic Coast Open 2025
97 Duke Win 15-11 2.86 42 6.32% Counts Mar 23rd Atlantic Coast Open 2025
81 North Carolina-Charlotte Win 13-12 -11.12 26 6.32% Counts Mar 23rd Atlantic Coast Open 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.