(23) #141 Arizona State (13-8)

682.36 (120)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
169 Houston Win 10-2 15.64 35 3.89% Counts (Why) Feb 12th Antifreeze 2022
133 Trinity Loss 6-7 -3.73 32 3.68% Counts Feb 12th Antifreeze 2022
208 Texas-B Win 11-1 3.83 40 4.08% Counts (Why) Feb 12th Antifreeze 2022
219 Texas-San Antonio Win 8-2 0.16 35 3.46% Counts (Why) Feb 12th Antifreeze 2022
165 North Texas Win 10-8 4 35 4.33% Counts Feb 13th Antifreeze 2022
133 Trinity Win 10-4 25.39 32 3.89% Counts (Why) Feb 13th Antifreeze 2022
128 Sam Houston State Loss 4-12 -23.66 30 4.27% Counts (Why) Feb 13th Antifreeze 2022
137 Arizona Win 11-8 23.94 172 5.94% Counts Mar 19th Uomo Donna
239 Arizona-B** Win 13-1 0 0% Ignored (Why) Mar 19th Uomo Donna
211 Northern Arizona Win 11-2 3.31 153 5.45% Counts (Why) Mar 19th Uomo Donna
137 Arizona Loss 6-7 -5.77 172 4.92% Counts Mar 20th Uomo Donna
239 Arizona-B** Win 13-0 0 0% Ignored (Why) Mar 20th Uomo Donna
137 Arizona Loss 8-12 -36.86 172 7.93% Counts Apr 23rd Desert D I College Womens CC 2022
- New Mexico** Win 14-5 0 153 0% Ignored (Why) Apr 23rd Desert D I College Womens CC 2022
211 Northern Arizona Win 6-3 0.23 153 5.46% Counts (Why) Apr 23rd Desert D I College Womens CC 2022
10 California-San Diego** Loss 4-13 0 10 0% Ignored (Why) May 7th Southwest D I College Womens Regionals 2022
56 Santa Clara Loss 3-13 -3.82 91 8.9% Counts (Why) May 7th Southwest D I College Womens Regionals 2022
115 California-San Diego-B Win 10-9 25.98 82 8.9% Counts May 7th Southwest D I College Womens Regionals 2022
95 UCSC Loss 8-13 -19.27 142 8.9% Counts May 7th Southwest D I College Womens Regionals 2022
137 Arizona Loss 6-13 -57.34 172 8.9% Counts (Why) May 8th Southwest D I College Womens Regionals 2022
132 Nevada-Reno Win 8-4 45.54 151 7.08% Counts (Why) May 8th Southwest D I 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.