(22) #101 Yale (10-9)

1262.48 (46)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
180 American Win 12-8 3.67 31 4.16% Counts Jan 25th Mid Atlantic Warm Up 2025
75 Carnegie Mellon Loss 9-12 -10.27 26 4.16% Counts Jan 25th Mid Atlantic Warm Up 2025
184 East Carolina Win 13-7 8.02 0 4.16% Counts (Why) Jan 25th Mid Atlantic Warm Up 2025
64 James Madison Win 10-8 19.32 70 4.05% Counts Jan 25th Mid Atlantic Warm Up 2025
159 George Mason Win 14-8 11.78 43 4.16% Counts (Why) Jan 26th Mid Atlantic Warm Up 2025
138 RIT Win 11-8 8.51 21 4.16% Counts Jan 26th Mid Atlantic Warm Up 2025
115 Vermont-B Loss 8-14 -26.22 35 4.16% Counts Jan 26th Mid Atlantic Warm Up 2025
69 Auburn Loss 7-13 -20.39 76 4.95% Counts Feb 15th Queen City Tune Up 2025
3 North Carolina Loss 6-13 17.89 5 4.95% Counts (Why) Feb 15th Queen City Tune Up 2025
81 North Carolina-Charlotte Win 13-7 32.14 26 4.95% Counts (Why) Feb 15th Queen City Tune Up 2025
104 Alabama Win 8-7 4.67 10 4.4% Counts Feb 16th Queen City Tune Up 2025
48 Maryland Loss 3-7 -11.05 58 3.59% Counts (Why) Feb 16th Queen City Tune Up 2025
75 Carnegie Mellon Loss 11-12 -1.22 26 7% Counts Mar 29th East Coast Invite 2025
192 Princeton Win 11-3 13.17 216 6.42% Counts (Why) Mar 29th East Coast Invite 2025
128 SUNY-Binghamton Win 13-8 27.23 178 7% Counts Mar 29th East Coast Invite 2025
52 William & Mary Loss 10-11 12.34 101 7% Counts Mar 29th East Coast Invite 2025
98 Boston College Loss 4-12 -42.54 296 6.72% Counts (Why) Mar 30th East Coast Invite 2025
179 Pennsylvania Win 12-9 -0.39 1 7% Counts Mar 30th East Coast Invite 2025
128 SUNY-Binghamton Loss 8-13 -47.46 178 7% Counts Mar 30th East Coast Invite 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.