() #4 Texas (17-5) SC 1

2214.75 (1)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
5 Vermont Win 12-7 23.13 7 4.29% Counts (Why) Feb 3rd Florida Warm Up 2023
21 Northeastern Win 13-8 8.45 6 4.29% Counts Feb 3rd Florida Warm Up 2023
88 Central Florida Win 15-8 -9.67 18 4.29% Counts (Why) Feb 4th Florida Warm Up 2023
34 Michigan Loss 9-11 -30.27 44 4.29% Counts Feb 4th Florida Warm Up 2023
201 South Florida** Win 13-5 0 24 0% Ignored (Why) Feb 4th Florida Warm Up 2023
23 Wisconsin Loss 10-11 -19.96 11 4.29% Counts Feb 4th Florida Warm Up 2023
72 Auburn** Win 13-5 0 54 0% Ignored (Why) Feb 5th Florida Warm Up 2023
5 Vermont Win 13-12 5.39 7 4.29% Counts Feb 5th Florida Warm Up 2023
3 Massachusetts Loss 10-13 -13.23 65 5.41% Counts Mar 4th Smoky Mountain Invite
12 Minnesota Win 13-11 4.86 32 5.41% Counts Mar 4th Smoky Mountain Invite
107 Tennessee Win 13-9 -25.97 9 5.41% Counts Mar 4th Smoky Mountain Invite
20 North Carolina State Win 11-8 5.5 3 5.41% Counts Mar 4th Smoky Mountain Invite
1 North Carolina Loss 8-15 -22.11 30 5.41% Counts Mar 5th Smoky Mountain Invite
8 Pittsburgh Win 15-8 28.88 17 5.41% Counts (Why) Mar 5th Smoky Mountain Invite
15 UCLA Win 15-10 15.27 3 5.41% Counts Mar 5th Smoky Mountain Invite
2 Brigham Young Win 13-12 14.77 3 6.07% Counts Mar 17th Centex 2023
86 Dartmouth** Win 13-4 0 64 0% Ignored (Why) Mar 18th Centex 2023
28 Oklahoma Christian Win 13-2 14.78 13 6.07% Counts (Why) Mar 18th Centex 2023
13 Tufts Win 11-10 -1.39 37 6.07% Counts Mar 18th Centex 2023
14 Carleton College Win 15-9 22.65 20 6.07% Counts Mar 19th Centex 2023
79 Texas A&M Win 15-8 -11.39 7 6.07% Counts (Why) Mar 19th Centex 2023
6 Colorado Loss 13-14 -9.19 13 6.07% Counts Mar 19th Centex 2023
**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.