(1) #68 James Madison (11-10)

1376.89 (20)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
224 American Win 13-7 -3.89 64 4.25% Counts (Why) Jan 27th Mid Atlantic Warm Up
142 Boston University Win 11-7 6.85 17 4.14% Counts Jan 27th Mid Atlantic Warm Up
85 Carnegie Mellon Win 12-6 22.49 20 4.14% Counts (Why) Jan 27th Mid Atlantic Warm Up
70 Case Western Reserve Loss 10-12 -11.03 4 4.25% Counts Jan 27th Mid Atlantic Warm Up
73 Richmond Win 12-10 10.02 20 4.25% Counts Jan 27th Mid Atlantic Warm Up
85 Carnegie Mellon Loss 7-8 -7.21 20 3.78% Counts Jan 28th Mid Atlantic Warm Up
98 Dartmouth Win 13-11 4.34 16 4.25% Counts Jan 28th Mid Atlantic Warm Up
84 Appalachian State Win 13-10 13.94 40 4.77% Counts Feb 10th Queen City Tune Up 2024
106 Notre Dame Win 14-13 -2.08 25 4.77% Counts Feb 10th Queen City Tune Up 2024
34 Ohio State Loss 7-12 -12.81 140 4.77% Counts Feb 10th Queen City Tune Up 2024
13 North Carolina State Loss 9-15 2.72 42 4.77% Counts Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Loss 13-15 1.36 20 4.77% Counts Feb 11th Queen City Tune Up 2024
29 South Carolina Loss 10-12 3.45 52 4.77% Counts Feb 11th Queen City Tune Up 2024
57 Auburn Win 12-9 23.54 7 5.36% Counts Feb 24th Easterns Qualifier 2024
56 Emory Loss 9-10 -3.08 31 5.36% Counts Feb 24th Easterns Qualifier 2024
28 North Carolina-Wilmington Loss 8-12 -4.73 109 5.36% Counts Feb 24th Easterns Qualifier 2024
169 Rutgers Win 9-6 -0.33 29 4.76% Counts Feb 24th Easterns Qualifier 2024
74 Cincinnati Loss 9-12 -20.45 6 5.36% Counts Feb 25th Easterns Qualifier 2024
154 Harvard Win 12-7 9.45 116 5.36% Counts (Why) Feb 25th Easterns Qualifier 2024
76 Purdue Loss 10-15 -26.8 56 5.36% Counts Feb 25th Easterns Qualifier 2024
126 Lehigh Win 10-9 -6.03 13 5.36% Counts Feb 25th Easterns Qualifier 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.