(1) #20 McGill (11-8)

1686.18 (31)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
106 LSU Win 12-7 -1.07 23 4.26% Counts (Why) Mar 19th Mens College Centex
17 Texas Loss 9-13 -14.91 33 4.26% Counts Mar 19th Mens College Centex
70 Chicago Win 11-6 7.9 19 4.03% Counts (Why) Mar 19th Mens College Centex
63 California-Santa Barbara Win 12-8 5.82 11 4.26% Counts Mar 19th Mens College Centex
31 Oklahoma Christian Win 15-10 15.99 26 4.26% Counts Mar 20th Mens College Centex
56 Dartmouth Win 11-7 8.67 35 4.15% Counts Mar 20th Mens College Centex
17 Texas Loss 10-12 -6.88 33 4.26% Counts Mar 20th Mens College Centex
156 Rhode Island Win 12-8 -17.59 44 5.37% Counts Apr 16th Greater New England D I College Mens CC 2022
86 Maine Win 15-5 8.17 49 5.37% Counts (Why) Apr 16th Greater New England D I College Mens CC 2022
7 Massachusetts Loss 9-10 7.09 34 5.37% Counts Apr 16th Greater New England D I College Mens CC 2022
56 Dartmouth Win 12-7 14.4 35 5.37% Counts (Why) Apr 16th Greater New England D I College Mens CC 2022
7 Massachusetts Loss 7-15 -19.85 34 5.37% Counts (Why) Apr 17th Greater New England D I College Mens CC 2022
1 Brown Loss 9-15 0.46 23 5.37% Counts Apr 17th Greater New England D I College Mens CC 2022
86 Maine Win 13-1 9.82 49 6.38% Counts (Why) May 7th New England D I College Mens Regionals 2022
93 Harvard Win 13-6 7.21 37 6.38% Counts (Why) May 7th New England D I College Mens Regionals 2022
1 Brown Loss 6-13 -5.21 23 6.38% Counts (Why) May 7th New England D I College Mens Regionals 2022
12 Northeastern Loss 6-13 -30.45 36 6.38% Counts (Why) May 7th New England D I College Mens Regionals 2022
7 Massachusetts Win 12-9 40.6 34 6.38% Counts May 8th New England D I College Mens Regionals 2022
12 Northeastern Loss 3-15 -30.45 36 6.38% Counts (Why) May 8th New England D I College Mens Regionals 2022
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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.