() #48 Penn State (11-8)

1275.52 (55)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
139 Auburn Win 11-5 -1.38 54 3.77% Counts (Why) Feb 26th Commonwealth Cup Weekend 2
26 Brown Loss 9-11 -0.99 63 4.11% Counts Feb 26th Commonwealth Cup Weekend 2
50 Carnegie Mellon Loss 5-15 -25.92 56 4.11% Counts (Why) Feb 26th Commonwealth Cup Weekend 2
139 Auburn Win 7-4 -4.49 54 3.13% Counts (Why) Feb 27th Commonwealth Cup Weekend 2
100 Case Western Reserve Win 9-7 -3.54 50 3.77% Counts Feb 27th Commonwealth Cup Weekend 2
149 North Carolina-Wilmington Win 14-9 -9.13 57 4.11% Counts Feb 27th Commonwealth Cup Weekend 2
84 West Chester Win 11-9 -1.56 67 6.15% Counts Apr 16th Pennsylvania D I College Womens CC 2022
13 Pittsburgh Loss 8-15 -7.93 58 6.15% Counts Apr 16th Pennsylvania D I College Womens CC 2022
235 Temple -B** Win 15-2 0 75 0% Ignored (Why) Apr 16th Pennsylvania D I College Womens CC 2022
58 Temple Win 15-10 24.01 52 6.15% Counts Apr 16th Pennsylvania D I College Womens CC 2022
50 Carnegie Mellon Win 10-5 32.89 56 5.47% Counts (Why) Apr 17th Pennsylvania D I College Womens CC 2022
32 Pennsylvania Loss 10-11 2.45 51 6.15% Counts Apr 17th Pennsylvania D I College Womens CC 2022
32 Pennsylvania Loss 7-10 -14.05 51 5.82% Counts Apr 17th Pennsylvania D I College Womens CC 2022
50 Carnegie Mellon Win 7-5 19.94 56 5.82% Counts May 7th Ohio Valley D I College Womens Regionals 2022
24 Ohio Loss 8-9 7.81 57 6.92% Counts May 7th Ohio Valley D I College Womens Regionals 2022
32 Pennsylvania Loss 3-11 -31.51 51 6.72% Counts (Why) May 7th Ohio Valley D I College Womens Regionals 2022
24 Ohio Loss 5-12 -27.95 57 7.02% Counts (Why) May 8th Ohio Valley D I College Womens Regionals 2022
61 Ohio State Win 14-5 39.32 48 7.32% Counts (Why) May 8th Ohio Valley D I College Womens Regionals 2022
58 Temple Win 11-10 2.97 52 7.32% Counts May 8th Ohio Valley 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.