(1) #11 Texas (14-8) SC 2

2471.75 (429)

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# Opponent Result Effect % of Ranking Status Date Event
16 Western Washington Win 11-4 19.29 3.92% Feb 17th Presidents Day Invitational Tournament 2018
17 California-Santa Barbara Loss 9-10 -12.34 4.27% Feb 17th Presidents Day Invitational Tournament 2018
61 California-Davis** Win 12-5 0 0% Ignored Feb 17th Presidents Day Invitational Tournament 2018
22 Minnesota Loss 10-11 -16.42 4.27% Feb 17th Presidents Day Invitational Tournament 2018
5 Oregon Win 12-11 11.78 4.27% Feb 18th Presidents Day Invitational Tournament 2018
20 Washington Win 11-7 10.22 4.16% Feb 18th Presidents Day Invitational Tournament 2018
4 Stanford Loss 6-13 -16.8 4.27% Feb 18th Presidents Day Invitational Tournament 2018
2 California-San Diego Loss 6-8 -1.5 3.67% Feb 19th Presidents Day Invitational Tournament 2018
9 Colorado Loss 9-10 -4.33 4.27% Feb 19th Presidents Day Invitational Tournament 2018
33 UCLA Win 11-7 2.83 4.67% Mar 3rd Stanford Invite 2018
26 California Win 13-7 11.04 4.8% Mar 3rd Stanford Invite 2018
6 British Columbia Loss 10-12 -7.54 4.8% Mar 3rd Stanford Invite 2018
16 Western Washington Win 12-11 -0.12 4.8% Mar 4th Stanford Invite 2018
2 California-San Diego Loss 9-13 -7.94 4.8% Mar 4th Stanford Invite 2018
12 Carleton College Loss 8-9 -8.31 4.54% Mar 4th Stanford Invite 2018
10 Pittsburgh Win 9-6 19.08 4.26% Mar 4th Stanford Invite 2018
34 Northeastern Win 13-8 4.8 5.7% Mar 24th Womens Centex 2018
65 Utah Win 13-8 -11.72 5.7% Mar 24th Womens Centex 2018
32 Florida Win 13-10 -3.83 5.7% Mar 24th Womens Centex 2018
37 Northwestern Win 13-10 -6.98 5.7% Mar 25th Womens Centex 2018
13 Ohio State Win 15-13 8.91 5.7% Mar 25th Womens Centex 2018
7 Tufts Win 13-12 9.8 5.7% 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.