() #45 Virginia (15-10)

1545.7 (9)

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# Opponent Result Effect % of Ranking Status Date Event
59 Duke Win 7-5 7.1 3.01% Jan 26th Winta Binta Vinta Fest 2019
71 William & Mary Win 9-2 12.34 3.14% Jan 26th Winta Binta Vinta Fest 2019
105 Liberty Win 11-2 5.1 3.48% Jan 26th Winta Binta Vinta Fest 2019
182 George Mason** Win 11-2 0 0% Ignored Jan 26th Winta Binta Vinta Fest 2019
61 James Madison Win 8-6 6.39 3.26% Jan 27th Winta Binta Vinta Fest 2019
40 Michigan Win 9-8 5.53 3.59% Jan 27th Winta Binta Vinta Fest 2019
69 Notre Dame Win 10-7 7.25 4.03% Feb 9th Queen City Tune Up 2019 Women
3 Ohio State** Loss 4-12 0 0% Ignored Feb 9th Queen City Tune Up 2019 Women
57 Cornell Win 11-7 16.51 4.14% Feb 9th Queen City Tune Up 2019 Women
22 Tufts Loss 3-10 -8.15 3.72% Feb 9th Queen City Tune Up 2019 Women
25 Clemson Loss 7-12 -8.62 4.26% Feb 10th Queen City Tune Up 2019 Women
91 Case Western Reserve Win 15-13 -5.73 4.26% Feb 10th Queen City Tune Up 2019 Women
43 Georgia Tech Win 11-10 6 4.26% Feb 10th Queen City Tune Up 2019 Women
8 Dartmouth** Loss 5-13 0 0% Ignored Feb 23rd Commonwealth Cup 2019
31 West Chester Loss 1-13 -21.66 4.78% Feb 23rd Commonwealth Cup 2019
56 Pennsylvania Loss 3-7 -23.64 3.47% Feb 23rd Commonwealth Cup 2019
52 Columbia Win 13-9 18.88 4.78% Feb 24th Commonwealth Cup 2019
28 North Carolina State Loss 11-12 5.17 4.78% Feb 24th Commonwealth Cup 2019
29 Northwestern Loss 4-10 -20.99 5.26% Mar 23rd Womens College Centex 2019
89 Iowa State Win 13-4 17.77 6.02% Mar 23rd Womens College Centex 2019
34 Colorado College Loss 8-9 2 5.7% Mar 23rd Womens College Centex 2019
89 Iowa State Win 10-9 -12.66 6.02% Mar 24th Womens College Centex 2019
34 Colorado College Loss 5-14 -28.31 6.02% Mar 24th Womens College Centex 2019
80 St Olaf Win 12-8 10.67 6.02% Mar 24th Womens College Centex 2019
43 Georgia Tech Win 10-9 8.64 6.02% Mar 24th Womens College Centex 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.