(6) #112 Texas Tech (12-11)

1285.08 (1)

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# Opponent Result Effect % of Ranking Status Date Event
344 Dallas** Win 13-4 0 0% Ignored Feb 3rd Big D in Little d Open 2018
160 Oklahoma Win 13-10 5.88 4.15% Feb 3rd Big D in Little d Open 2018
82 Oklahoma State Win 15-11 21.8 4.15% Feb 3rd Big D in Little d Open 2018
199 Stephen F Austin Win 12-8 3.92 4.15% Feb 3rd Big D in Little d Open 2018
217 Texas Christian Win 10-6 3.94 3.81% Feb 3rd Big D in Little d Open 2018
68 Baylor Loss 8-9 1.83 3.93% Feb 4th Big D in Little d Open 2018
287 Central Arkansas** Win 15-4 0 0% Ignored Feb 4th Big D in Little d Open 2018
27 Texas State Loss 9-15 -3.44 4.15% Feb 4th Big D in Little d Open 2018
89 John Brown Loss 7-8 -1.27 4.39% Feb 24th Dust Bowl 2018
123 Nebraska Win 9-5 21.88 4.24% Feb 24th Dust Bowl 2018
200 Rice Win 8-6 -2.3 4.24% Feb 24th Dust Bowl 2018
162 Saint Louis Win 9-6 9.78 4.39% Feb 24th Dust Bowl 2018
70 Arkansas Loss 9-14 -16.59 4.94% Feb 25th Dust Bowl 2018
139 Luther Win 15-11 13.72 4.94% Feb 25th Dust Bowl 2018
82 Oklahoma State Loss 10-11 -0.15 4.94% Feb 25th Dust Bowl 2018
40 Iowa Loss 7-12 -10.61 5.54% Mar 10th Mens Centex 2018
114 Minnesota-Duluth Win 11-10 7.1 5.54% Mar 10th Mens Centex 2018
31 LSU Loss 6-12 -9.4 5.39% Mar 10th Mens Centex 2018
39 Northwestern Loss 6-13 -15.04 5.54% Mar 10th Mens Centex 2018
27 Texas State Loss 7-11 -1.76 5.39% Mar 10th Mens Centex 2018
160 Oklahoma Win 15-12 6.34 5.54% Mar 11th Mens Centex 2018
130 North Texas Loss 8-11 -26.91 5.54% Mar 11th Mens Centex 2018
41 Northeastern Loss 6-10 -9.53 5.09% 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.