(6) #56 Cornell (14-7)

1523.94 (82)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
44 Emory Win 10-9 8.77 42 4.02% Counts Jan 31st Florida Warm Up 2025
134 South Florida Win 9-5 4.2 68 3.45% Counts (Why) Jan 31st Florida Warm Up 2025
36 Michigan Loss 8-11 -9.92 13 4.02% Counts Jan 31st Florida Warm Up 2025
15 Washington University Loss 7-13 -5.44 70 4.02% Counts Feb 1st Florida Warm Up 2025
38 Utah State Loss 9-13 -12.99 44 4.02% Counts Feb 1st Florida Warm Up 2025
139 Florida State Win 13-9 -0.91 59 4.02% Counts Feb 1st Florida Warm Up 2025
79 Florida Win 10-9 -2.14 41 4.02% Counts Feb 2nd Florida Warm Up 2025
43 Virginia Tech Loss 7-13 -19.67 19 4.02% Counts Feb 2nd Florida Warm Up 2025
116 West Chester Win 11-7 7.01 101 4.93% Counts Mar 1st Oak Creek Challenge 2025
226 SUNY-Albany** Win 10-1 0 74 0% Ignored (Why) Mar 1st Oak Creek Challenge 2025
105 Liberty Win 13-2 16.49 86 5.07% Counts (Why) Mar 1st Oak Creek Challenge 2025
138 RIT Win 13-7 6.75 21 5.07% Counts (Why) Mar 2nd Oak Creek Challenge 2025
132 Rutgers Loss 8-9 -26.8 135 4.79% Counts Mar 2nd Oak Creek Challenge 2025
157 Johns Hopkins Win 13-5 4.37 47 5.07% Counts (Why) Mar 2nd Oak Creek Challenge 2025
98 Boston College Win 11-10 -8.68 296 6.38% Counts Mar 29th East Coast Invite 2025
71 Case Western Reserve Win 9-8 0.39 41 6.04% Counts Mar 29th East Coast Invite 2025
108 Columbia Win 15-8 17.74 52 6.38% Counts (Why) Mar 29th East Coast Invite 2025
48 Maryland Loss 10-11 -5.68 58 6.38% Counts Mar 29th East Coast Invite 2025
52 William & Mary Win 14-11 23.25 101 6.38% Counts Mar 30th East Coast Invite 2025
48 Maryland Loss 8-10 -14.64 58 6.21% Counts Mar 30th East Coast Invite 2025
102 Syracuse Win 10-5 18.66 140 5.67% Counts (Why) 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.