(2) #66 Virginia (9-14)

1771.7 (431)

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# Opponent Result Effect % of Ranking Status Date Event
135 William & Mary Win 11-5 3.94 3.71% Jan 27th Winta Binta Vinta Fest 2018
145 Liberty Win 11-3 1.63 3.71% Jan 27th Winta Binta Vinta Fest 2018
150 Virginia Commonwealth Win 10-3 0.53 3.53% Jan 27th Winta Binta Vinta Fest 2018
49 Duke Loss 6-9 -8.9 3.6% Jan 27th Winta Binta Vinta Fest 2018
80 James Madison Win 11-9 5.25 4.05% Jan 28th Winta Binta Vinta Fest 2018
88 Georgetown Win 10-4 14.91 3.53% Jan 28th Winta Binta Vinta Fest 2018
46 North Carolina-Wilmington Loss 6-14 -16.59 4.05% Jan 28th Winta Binta Vinta Fest 2018
25 Notre Dame Win 10-8 27.46 4.17% Feb 3rd Queen City Tune Up 2018 College Women
48 Georgia Loss 9-10 2.64 4.29% Feb 3rd Queen City Tune Up 2018 College Women
93 Cornell Loss 7-8 -14.1 3.81% Feb 3rd Queen City Tune Up 2018 College Women
1 Dartmouth Loss 6-13 23.54 4.29% Feb 3rd Queen City Tune Up 2018 College Women
7 Tufts** Loss 2-15 0 0% Ignored Feb 24th Commonwealth Cup 2018
48 Georgia Loss 9-11 -3.51 5.1% Feb 24th Commonwealth Cup 2018
46 North Carolina-Wilmington Loss 10-14 -10.32 5.1% Feb 24th Commonwealth Cup 2018
99 Princeton Win 13-5 18.72 5.1% Feb 25th Commonwealth Cup 2018
48 Georgia Loss 7-9 -4.68 4.68% Feb 25th Commonwealth Cup 2018
15 North Carolina State Loss 8-15 1.07 6.06% Mar 16th Atlantic Coast Showcase ACS NCSU vs Virginia
42 Wisconsin Loss 8-10 -2.05 6.25% Mar 24th Womens Centex 2018
7 Tufts** Loss 2-13 0 0% Ignored Mar 24th Womens Centex 2018
109 Texas Christian Win 14-12 -7.63 6.42% Mar 24th Womens Centex 2018
44 Colorado State Loss 5-10 -22.03 5.71% Mar 24th Womens Centex 2018
78 Boston University Loss 10-11 -15.25 6.42% Mar 25th Womens Centex 2018
96 St Olaf Win 14-11 4.65 6.42% Mar 25th Womens Centex 2018
**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.