(6) #170 Kansas State (13-9)

1059.64 (41)

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# Opponent Result Effect % of Ranking Status Date Event
123 Nebraska Loss 9-15 -20.19 5.85% Feb 3rd Big D in Little d Open 2018
336 Texas-Dallas-B** Win 13-3 0 0% Ignored Feb 3rd Big D in Little d Open 2018
287 Central Arkansas Win 13-1 11.29 5.85% Feb 3rd Big D in Little d Open 2018
27 Texas State** Loss 4-12 0 0% Ignored Feb 3rd Big D in Little d Open 2018
305 Oklahoma-B Win 12-3 6.05 5.62% Feb 3rd Big D in Little d Open 2018
82 Oklahoma State Loss 5-13 -15.69 5.85% Feb 3rd Big D in Little d Open 2018
130 North Texas Loss 6-13 -29.06 5.85% Feb 4th Big D in Little d Open 2018
217 Texas Christian Win 11-10 -2.88 5.85% Feb 4th Big D in Little d Open 2018
152 Denver Win 7-5 22.53 5.53% Feb 24th Dust Bowl 2018
99 Missouri S&T Loss 12-13 11.46 6.96% Feb 24th Dust Bowl 2018
391 Kansas B-B** Win 11-3 0 0% Ignored Feb 24th Dust Bowl 2018
187 Texas A&M-B Loss 6-9 -32.75 6.18% Feb 24th Dust Bowl 2018
287 Central Arkansas Win 5-0 8.01 4.22% Feb 24th Dust Bowl 2018
387 North Texas-B** Win 15-0 0 0% Ignored Feb 24th Dust Bowl 2018
284 Tulsa Win 15-3 13.99 6.96% Feb 25th Dust Bowl 2018
287 Central Arkansas Win 9-2 11.1 5.76% Feb 25th Dust Bowl 2018
348 Iowa State-B** Win 15-4 0 0% Ignored Mar 3rd Midwest Throwdown 2018
99 Missouri S&T Win 15-14 32.1 7.37% Mar 3rd Midwest Throwdown 2018
363 Wisconsin-Oshkosh** Win 15-3 0 0% Ignored Mar 3rd Midwest Throwdown 2018
51 Ohio State Loss 5-15 -9.71 7.37% Mar 4th Midwest Throwdown 2018
105 Wisconsin-Milwaukee Loss 10-11 10.57 7.37% Mar 4th Midwest Throwdown 2018
190 Northern Iowa Loss 14-15 -16.63 7.37% Mar 4th Midwest Throwdown 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.