(7) #156 Johns Hopkins (11-8)

1019.03 (53)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
142 Boston University Loss 8-9 -2.99 17 3.82% Counts Jan 27th Mid Atlantic Warm Up
85 Carnegie Mellon Loss 8-13 -8.28 20 4.04% Counts Jan 27th Mid Atlantic Warm Up
165 RIT Win 11-9 8.22 24 4.04% Counts Jan 27th Mid Atlantic Warm Up
116 Liberty Loss 8-9 1.54 8 3.82% Counts Jan 27th Mid Atlantic Warm Up
224 American Win 12-8 6.47 64 4.04% Counts Jan 28th Mid Atlantic Warm Up
208 Virginia Commonwealth Loss 10-11 -15.08 52 4.04% Counts Jan 28th Mid Atlantic Warm Up
165 RIT Win 15-10 16.81 24 4.04% Counts Jan 28th Mid Atlantic Warm Up
101 Cornell Loss 4-13 -22.46 51 5.39% Counts (Why) Mar 2nd Oak Creek Challenge 2024
280 Drexel Win 13-2 3.11 32 5.39% Counts (Why) Mar 2nd Oak Creek Challenge 2024
90 SUNY-Buffalo Loss 7-13 -17.17 72 5.39% Counts Mar 2nd Oak Creek Challenge 2024
280 Drexel Win 13-3 3.11 32 5.39% Counts (Why) Mar 3rd Oak Creek Challenge 2024
206 George Washington Win 11-6 17.77 125 5.1% Counts (Why) Mar 3rd Oak Creek Challenge 2024
152 West Chester Loss 8-11 -20.39 34 5.39% Counts Mar 3rd Oak Creek Challenge 2024
256 Virginia Tech-B Win 15-8 8.91 97 6.79% Counts (Why) Mar 30th Atlantic Coast Open 2024
165 RIT Loss 13-14 -13.01 24 6.79% Counts Mar 30th Atlantic Coast Open 2024
298 Mary Washington Win 14-8 -8.68 210 6.79% Counts (Why) Mar 30th Atlantic Coast Open 2024
208 Virginia Commonwealth Win 9-7 3.03 52 6.23% Counts Mar 30th Atlantic Coast Open 2024
252 Dickinson Win 15-4 13.93 80 6.79% Counts (Why) Mar 31st Atlantic Coast Open 2024
206 George Washington Win 15-8 25.42 125 6.79% Counts (Why) Mar 31st Atlantic Coast Open 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.