(28) #87 Temple (8-12)

1310.92 (179)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
54 Carleton College-CHOP Loss 9-13 -7.97 41 4% Counts Feb 1st Carolina Kickoff mens 2025
49 North Carolina State Loss 9-13 -6.85 21 4% Counts Feb 1st Carolina Kickoff mens 2025
81 North Carolina-Charlotte Loss 8-13 -20.18 26 4% Counts Feb 1st Carolina Kickoff mens 2025
54 Carleton College-CHOP Loss 10-15 -9.43 41 4% Counts Feb 2nd Carolina Kickoff mens 2025
97 Duke Loss 11-12 -6.76 42 4% Counts Feb 2nd Carolina Kickoff mens 2025
84 Ohio State Win 15-12 12.87 51 4% Counts Feb 2nd Carolina Kickoff mens 2025
44 Emory Loss 7-12 -11.14 42 4.75% Counts Feb 22nd Easterns Qualifier 2025
88 Georgetown Win 13-10 16.26 78 4.75% Counts Feb 22nd Easterns Qualifier 2025
37 North Carolina-Wilmington Loss 6-13 -13.76 84 4.75% Counts (Why) Feb 22nd Easterns Qualifier 2025
102 Syracuse Loss 9-10 -8.76 140 4.75% Counts Feb 22nd Easterns Qualifier 2025
69 Auburn Loss 12-14 -5.16 76 4.75% Counts Feb 23rd Easterns Qualifier 2025
97 Duke Loss 10-15 -24.5 42 4.75% Counts Feb 23rd Easterns Qualifier 2025
63 Notre Dame Win 15-13 18.1 277 4.75% Counts Feb 23rd Easterns Qualifier 2025
98 Boston College Win 12-10 13.47 296 6.34% Counts Mar 29th East Coast Invite 2025
75 Carnegie Mellon Loss 8-9 -4.13 26 6% Counts Mar 29th East Coast Invite 2025
120 Connecticut Win 11-9 7.95 57 6.34% Counts Mar 29th East Coast Invite 2025
174 Delaware Win 13-5 15.38 78 6.34% Counts (Why) Mar 29th East Coast Invite 2025
75 Carnegie Mellon Win 7-5 20.62 26 5.04% Counts Mar 30th East Coast Invite 2025
71 Case Western Reserve Win 13-9 34.73 41 6.34% Counts Mar 30th East Coast Invite 2025
102 Syracuse Loss 10-12 -19.55 140 6.34% 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.