(4) #11 Brown (14-8) NE 3

2074.72 (50)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
39 Florida Win 13-11 -4.04 16 3.72% Counts Feb 3rd Florida Warm Up 2023
77 Temple Win 13-7 -1.43 62 3.72% Counts (Why) Feb 3rd Florida Warm Up 2023
23 Wisconsin Win 13-11 1.88 11 3.72% Counts Feb 3rd Florida Warm Up 2023
2 Brigham Young Loss 6-13 -13.79 3 3.72% Counts (Why) Feb 4th Florida Warm Up 2023
19 Georgia Loss 7-9 -14.27 81 3.42% Counts Feb 4th Florida Warm Up 2023
21 Northeastern Loss 12-15 -18.11 6 3.72% Counts Feb 4th Florida Warm Up 2023
104 Florida State** Win 15-6 0 17 0% Ignored (Why) Feb 5th Florida Warm Up 2023
88 Central Florida Win 13-10 -12.08 18 3.72% Counts Feb 5th Florida Warm Up 2023
14 Carleton College Loss 10-13 -17.38 20 4.69% Counts Mar 4th Smoky Mountain Invite
72 Auburn Win 13-11 -17.14 54 4.69% Counts Mar 4th Smoky Mountain Invite
1 North Carolina Loss 9-13 -4.95 30 4.69% Counts Mar 4th Smoky Mountain Invite
6 Colorado Loss 12-13 -0.11 13 4.69% Counts Mar 4th Smoky Mountain Invite
107 Tennessee Win 15-7 -6.55 9 4.69% Counts (Why) Mar 5th Smoky Mountain Invite
12 Minnesota Win 15-12 14.61 32 4.69% Counts Mar 5th Smoky Mountain Invite
21 Northeastern Win 15-9 17.13 6 4.69% Counts Mar 5th Smoky Mountain Invite
34 Michigan Win 13-10 2.65 44 5.91% Counts Apr 1st Easterns 2023
9 Oregon Win 13-10 24.54 10 5.91% Counts Apr 1st Easterns 2023
14 Carleton College Loss 12-13 -9.42 20 5.91% Counts Apr 1st Easterns 2023
27 South Carolina Win 13-9 12.07 73 5.91% Counts Apr 1st Easterns 2023
5 Vermont Loss 13-14 0.65 7 5.91% Counts Apr 2nd Easterns 2023
12 Minnesota Win 14-12 13.65 32 5.91% Counts Apr 2nd Easterns 2023
3 Massachusetts Win 15-12 33.76 65 5.91% Counts Apr 2nd Easterns 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.