(21) #147 George Washington (13-7)

881.09 (106)

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# Opponent Result Effect % of Ranking Status Date Event
155 Appalachian State Win 12-7 26.79 5.19% Feb 2nd Hucking Shucking 2019
209 North Carolina-B Win 13-6 11.24 5.19% Feb 2nd Hucking Shucking 2019
192 William & Mary-B Win 13-7 13.87 5.19% Feb 2nd Hucking Shucking 2019
199 Miami Win 10-7 3.21 4.91% Feb 3rd Hucking Shucking 2019
288 University of North Carolina - Asheville** Win 13-1 0 0% Ignored Feb 3rd Hucking Shucking 2019
117 Catholic Win 13-6 41.74 5.19% Feb 3rd Hucking Shucking 2019
117 Catholic Loss 10-12 -4.13 5.19% Feb 3rd Hucking Shucking 2019
258 Maryland-Baltimore County** Win 13-2 0 0% Ignored Feb 16th Cherry Blossom Classic 2019
242 West Virginia** Win 13-3 0 0% Ignored Feb 16th Cherry Blossom Classic 2019
166 Richmond Win 8-6 9.99 5% Feb 16th Cherry Blossom Classic 2019
94 Carnegie Mellon Loss 8-10 2.46 5.67% Feb 16th Cherry Blossom Classic 2019
145 American Loss 6-11 -31.25 5.51% Feb 17th Cherry Blossom Classic 2019
258 Maryland-Baltimore County** Win 10-4 0 0% Ignored Feb 17th Cherry Blossom Classic 2019
94 Carnegie Mellon Loss 5-13 -18.33 5.82% Feb 17th Cherry Blossom Classic 2019
71 William & Mary Loss 4-13 -13.87 8.24% Mar 30th Atlantic Coast Open 2019
157 Virginia Commonwealth Loss 6-10 -43.77 7.56% Mar 30th Atlantic Coast Open 2019
182 George Mason Win 13-4 31.17 8.24% Mar 30th Atlantic Coast Open 2019
197 Christopher Newport Win 13-4 25.5 8.24% Mar 30th Atlantic Coast Open 2019
182 George Mason Win 9-7 2.17 7.56% Mar 31st Atlantic Coast Open 2019
197 Christopher Newport Loss 6-9 -58 7.32% Mar 31st Atlantic Coast Open 2019
**Blowout Eligible

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.