(7) #101 Cornell (10-11)

1224.57 (51)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
12 Alabama-Huntsville Loss 8-13 10.54 3 3.72% Counts Feb 2nd Florida Warm Up 2024
10 Carleton College Loss 8-13 11.18 87 3.72% Counts Feb 2nd Florida Warm Up 2024
185 South Florida Win 13-5 9.33 12 3.72% Counts (Why) Feb 2nd Florida Warm Up 2024
74 Cincinnati Win 13-11 14.11 6 3.72% Counts Feb 3rd Florida Warm Up 2024
20 Northeastern Loss 6-13 0.22 65 3.72% Counts (Why) Feb 3rd Florida Warm Up 2024
41 Florida Loss 12-13 8.55 4 3.72% Counts Feb 4th Florida Warm Up 2024
19 Washington University Loss 11-12 19.91 112 3.72% Counts Feb 4th Florida Warm Up 2024
90 SUNY-Buffalo Loss 1-13 -27.01 72 4.68% Counts (Why) Mar 2nd Oak Creek Challenge 2024
280 Drexel Win 13-6 -7.42 32 4.68% Counts (Why) Mar 2nd Oak Creek Challenge 2024
156 Johns Hopkins Win 13-4 19.38 53 4.68% Counts (Why) Mar 2nd Oak Creek Challenge 2024
84 Appalachian State Loss 9-13 -15.55 40 4.68% Counts Mar 3rd Oak Creek Challenge 2024
85 Carnegie Mellon Win 13-12 10.75 20 4.68% Counts Mar 3rd Oak Creek Challenge 2024
130 Towson Loss 11-13 -16.54 45 4.68% Counts Mar 3rd Oak Creek Challenge 2024
167 Columbia Win 10-9 -8.86 38 5.9% Counts Mar 30th East Coast Invite 2024
146 Yale Loss 7-10 -32.76 8 5.58% Counts Mar 30th East Coast Invite 2024
123 Pennsylvania Loss 10-11 -12.67 59 5.9% Counts Mar 30th East Coast Invite 2024
154 Harvard Win 11-10 -4.79 116 5.9% Counts Mar 30th East Coast Invite 2024
150 Navy Win 13-9 14.32 45 5.9% Counts Mar 31st East Coast Invite 2024
107 Princeton Win 11-10 6.84 61 5.9% Counts Mar 31st East Coast Invite 2024
25 McGill Loss 4-13 -3.16 130 5.9% Counts (Why) Mar 31st East Coast Invite 2024
123 Pennsylvania Win 7-6 2.46 59 4.88% Counts Mar 31st East Coast Invite 2024
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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.