(7) #30 Wisconsin (8-10)

2022.97 (99)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
140 North Carolina-Wilmington** Win 13-1 0 117 0% Ignored (Why) Feb 15th Queen City Tune Up 2025
6 Vermont** Loss 2-13 0 33 0% Ignored (Why) Feb 15th Queen City Tune Up 2025
49 William & Mary Loss 9-13 -39.14 207 5.87% Counts Feb 15th Queen City Tune Up 2025
62 North Carolina State Win 8-4 9.46 81 4.66% Counts (Why) Feb 16th Queen City Tune Up 2025
27 Northeastern Loss 4-9 -27.06 184 4.86% Counts (Why) Feb 16th Queen City Tune Up 2025
58 Brown Win 8-6 -3.3 157 5.66% Counts Mar 1st Stanford Invite 2025 Womens
14 Cal Poly-SLO Loss 9-12 5.27 3 6.59% Counts Mar 1st Stanford Invite 2025 Womens
12 California-Santa Cruz Loss 6-13 -10.29 25 6.59% Counts (Why) Mar 1st Stanford Invite 2025 Womens
13 Stanford Loss 5-13 -10.43 1 6.59% Counts (Why) Mar 1st Stanford Invite 2025 Womens
39 California Win 8-7 -1.92 18 5.85% Counts Mar 2nd Stanford Invite 2025 Womens
16 California-Davis Loss 7-8 9.87 10 5.85% Counts Mar 2nd Stanford Invite 2025 Womens
37 American Win 12-6 39.83 20 8.08% Counts (Why) Mar 29th East Coast Invite 2025
33 Cornell Win 13-4 49.38 14 8.3% Counts (Why) Mar 29th East Coast Invite 2025
31 Pittsburgh Loss 8-11 -33.86 65 8.3% Counts Mar 29th East Coast Invite 2025
119 Yale** Win 15-3 0 813 0% Ignored (Why) Mar 29th East Coast Invite 2025
36 MIT Win 8-6 13.56 295 7.13% Counts Mar 30th East Coast Invite 2025
21 Ohio State Loss 10-13 -7.65 145 8.3% Counts Mar 30th East Coast Invite 2025
23 Pennsylvania Loss 7-8 7.79 99 7.38% Counts Mar 30th East Coast Invite 2025
**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.