(6) #136 Rochester (9-10)

645.42 (85)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
119 Catholic Win 10-6 31.85 80 4.95% Counts (Why) Mar 19th Jersey Devil
37 Wellesley** Loss 3-13 0 58 0% Ignored (Why) Mar 19th Jersey Devil
147 Swarthmore Loss 7-10 -23.68 73 5.1% Counts Mar 19th Jersey Devil
65 Haverford Loss 8-13 -0.16 55 5.4% Counts Mar 20th Jersey Devil
28 Williams** Loss 4-15 0 74 0% Ignored (Why) Mar 20th Jersey Devil
147 Swarthmore Win 10-9 4.23 73 5.4% Counts Mar 20th Jersey Devil
140 Ithaca Loss 6-7 -7.32 76 5.31% Counts Apr 9th Western NY D III College Womens CC 2022
190 Hamilton Win 7-0 12.37 85 4.65% Counts (Why) Apr 9th Western NY D III College Womens CC 2022
206 SUNY-Oneonta Win 7-2 5.3 79 4.65% Counts (Why) Apr 9th Western NY D III College Womens CC 2022
93 SUNY-Geneseo Loss 2-6 -12.79 128 4.29% Counts (Why) Apr 9th Western NY D III College Womens CC 2022
190 Hamilton Loss 6-7 -26.43 85 5.31% Counts Apr 10th Western NY D III College Womens CC 2022
206 SUNY-Oneonta Win 8-7 -22.16 79 5.7% Counts Apr 10th Western NY D III College Womens CC 2022
91 Skidmore Loss 5-7 -0.45 44 6.06% Counts Apr 30th Metro East D III College Womens Regionals 2022
190 Hamilton Win 9-5 12.79 85 6.55% Counts (Why) Apr 30th Metro East D III College Womens Regionals 2022
216 Wesleyan Win 13-6 1.61 80 7.63% Counts (Why) Apr 30th Metro East D III College Womens Regionals 2022
93 SUNY-Geneseo Loss 5-8 -9.35 128 6.31% Counts Apr 30th Metro East D III College Womens Regionals 2022
190 Hamilton Win 14-8 15.65 85 7.63% Counts (Why) May 1st Metro East D III College Womens Regionals 2022
199 Connecticut College Win 15-6 15.16 79 7.63% Counts (Why) May 1st Metro East D III College Womens Regionals 2022
93 SUNY-Geneseo Loss 8-10 4.19 128 7.43% Counts May 1st Metro East D III College Womens 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.