(9) #70 Case Western Reserve (14-7)

1366.71 (4)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
96 Connecticut Win 10-9 0.3 26 3.72% Counts Jan 27th Mid Atlantic Warm Up
68 James Madison Win 12-10 9.59 20 3.72% Counts Jan 27th Mid Atlantic Warm Up
208 Virginia Commonwealth Win 12-5 0.69 52 3.57% Counts (Why) Jan 27th Mid Atlantic Warm Up
111 SUNY-Binghamton Loss 6-10 -23.72 14 3.41% Counts Jan 27th Mid Atlantic Warm Up
123 Pennsylvania Win 9-8 -3.44 59 3.52% Counts Jan 27th Mid Atlantic Warm Up
142 Boston University Win 13-5 11.67 17 3.72% Counts (Why) Jan 28th Mid Atlantic Warm Up
73 Richmond Win 14-12 8.44 20 3.72% Counts Jan 28th Mid Atlantic Warm Up
154 Harvard Loss 10-14 -32.34 116 4.18% Counts Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Loss 10-13 -3.34 20 4.18% Counts Feb 10th Queen City Tune Up 2024
61 William & Mary Loss 10-12 -7.53 50 4.18% Counts Feb 10th Queen City Tune Up 2024
16 Penn State Loss 9-15 1.7 59 4.18% Counts Feb 10th Queen City Tune Up 2024
106 Notre Dame Win 15-12 6.28 25 4.18% Counts Feb 11th Queen City Tune Up 2024
72 Georgetown Win 11-6 22.44 30 3.95% Counts (Why) Feb 11th Queen City Tune Up 2024
184 George Mason Win 12-5 6.98 70 6% Counts (Why) Mar 30th East Coast Invite 2024
107 Princeton Loss 9-12 -33.6 61 6.26% Counts Mar 30th East Coast Invite 2024
169 Rutgers Win 13-6 12.34 29 6.26% Counts (Why) Mar 30th East Coast Invite 2024
98 Dartmouth Win 13-12 0.26 16 6.26% Counts Mar 30th East Coast Invite 2024
113 Syracuse Win 12-8 17.57 30 6.26% Counts Mar 31st East Coast Invite 2024
146 Yale Win 11-9 -3.83 8 6.26% Counts Mar 31st East Coast Invite 2024
123 Pennsylvania Win 13-7 22.58 59 6.26% Counts (Why) Mar 31st East Coast Invite 2024
25 McGill Loss 6-13 -12.84 130 6.26% Counts (Why) 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.