(24) #135 Kansas (11-9)

1107.24 (110)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
211 Arizona Win 13-10 -0.27 127 4.04% Counts Jan 27th New Year Fest 40
178 Brigham Young-B Win 10-6 11.62 179 3.71% Counts (Why) Jan 27th New Year Fest 40
93 Colorado-B Loss 4-13 -18.57 387 4.04% Counts (Why) Jan 27th New Year Fest 40
109 Denver Loss 7-12 -18.14 256 4.04% Counts Jan 27th New Year Fest 40
45 Utah Valley Loss 5-13 -7.34 241 4.04% Counts (Why) Jan 28th New Year Fest 40
193 Northern Arizona Win 13-2 14.46 148 4.04% Counts (Why) Jan 28th New Year Fest 40
128 Colorado College Loss 4-10 -28.25 232 4.71% Counts (Why) Mar 2nd Snow Melt 2024
263 Colorado-C Win 13-4 2.07 95 5.39% Counts (Why) Mar 2nd Snow Melt 2024
327 Denver-B** Win 13-5 0 80 0% Ignored (Why) Mar 2nd Snow Melt 2024
45 Utah Valley Loss 1-13 -9.94 241 5.39% Counts (Why) Mar 2nd Snow Melt 2024
128 Colorado College Loss 11-12 -5.49 232 5.39% Counts Mar 3rd Snow Melt 2024
69 Colorado Mines Loss 10-14 -7.78 356 5.39% Counts Mar 3rd Snow Melt 2024
171 Montana State Win 11-7 17.02 255 5.25% Counts Mar 3rd Snow Melt 2024
188 John Brown Win 13-5 24.34 18 6.41% Counts (Why) Mar 23rd Free State Classic
176 Saint Louis Win 10-8 5.44 51 6.24% Counts Mar 23rd Free State Classic
161 Truman State Win 11-9 9.23 49 6.41% Counts Mar 23rd Free State Classic
49 St Olaf Loss 10-11 18.57 64 6.41% Counts Mar 23rd Free State Classic
188 John Brown Win 11-8 8.28 18 6.41% Counts Mar 24th Free State Classic
254 Oklahoma State Win 11-7 -2.72 14 6.24% Counts Mar 24th Free State Classic
49 St Olaf Loss 8-15 -11.57 64 6.41% Counts Mar 24th Free State Classic
**Blowout Eligible. Learn more about how this works here.

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.