(15) #160 Oklahoma (7-15)

1092.6 (23)

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# Opponent Result Effect % of Ranking Status Date Event
68 Baylor Loss 8-15 -9.85 4.64% Feb 3rd Big D in Little d Open 2018
344 Dallas** Win 13-5 0 0% Ignored Feb 3rd Big D in Little d Open 2018
199 Stephen F Austin Win 9-8 -1.52 4.39% Feb 3rd Big D in Little d Open 2018
217 Texas Christian Win 9-4 15.78 3.84% Feb 3rd Big D in Little d Open 2018
112 Texas Tech Loss 10-13 -6.6 4.64% Feb 3rd Big D in Little d Open 2018
123 Nebraska Loss 11-15 -10.86 4.64% Feb 4th Big D in Little d Open 2018
200 Rice Win 11-10 -1.7 4.64% Feb 4th Big D in Little d Open 2018
41 Northeastern Loss 6-13 -4.9 5.2% Feb 16th Warm Up A Florida Affair 2018
10 Virginia Tech** Loss 5-13 0 0% Ignored Feb 16th Warm Up A Florida Affair 2018
81 Florida State Win 12-10 30.43 5.2% Feb 16th Warm Up A Florida Affair 2018
36 Michigan Loss 5-13 -2.98 5.2% Feb 16th Warm Up A Florida Affair 2018
31 LSU Loss 8-12 9.1 5.2% Feb 17th Warm Up A Florida Affair 2018
14 Florida** Loss 5-13 0 0% Ignored Feb 17th Warm Up A Florida Affair 2018
168 South Florida Loss 13-15 -13.33 5.2% Feb 18th Warm Up A Florida Affair 2018
42 Connecticut Loss 6-13 -5.33 5.2% Feb 18th Warm Up A Florida Affair 2018
111 Arizona State Loss 6-14 -22.15 5.2% Feb 18th Warm Up A Florida Affair 2018
56 Temple Loss 7-10 1.7 5.85% Mar 10th Mens Centex 2018
199 Stephen F Austin Loss 9-11 -26.87 6.19% Mar 10th Mens Centex 2018
184 Texas-San Antonio Win 13-6 32.43 6.19% Mar 10th Mens Centex 2018
200 Rice Win 13-8 22.18 6.19% Mar 10th Mens Centex 2018
58 Kansas Loss 11-15 1.79 6.19% Mar 11th Mens Centex 2018
112 Texas Tech Loss 12-15 -7.13 6.19% Mar 11th Mens 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.