(47) #156 Denver (9-10)

1296.21 (99)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
219 Arizona Win 12-7 13.12 280 4.53% Counts (Why) Jan 27th New Year Fest 40
180 Brigham Young-B Win 13-6 23.65 282 4.53% Counts (Why) Jan 27th New Year Fest 40
100 Colorado-B Loss 10-12 -0.99 247 4.53% Counts Jan 27th New Year Fest 40
137 Kansas Win 12-7 27.96 258 4.53% Counts (Why) Jan 27th New Year Fest 40
101 Colorado Mines Loss 8-11 -7.1 143 4.53% Counts Jan 28th New Year Fest 40
100 Colorado-B Loss 6-13 -18.15 247 4.53% Counts (Why) Jan 28th New Year Fest 40
247 Northern Arizona Win 13-4 12.75 115 4.53% Counts (Why) Jan 28th New Year Fest 40
119 Colorado College Win 11-7 36.17 272 5.88% Counts Mar 2nd Snow Melt 2024
405 Colorado State-B** Win 13-1 0 131 0% Ignored (Why) Mar 2nd Snow Melt 2024
58 Utah Valley Loss 6-13 -10.2 205 6.04% Counts (Why) Mar 2nd Snow Melt 2024
100 Colorado-B Loss 8-15 -22.35 247 6.04% Counts Mar 3rd Snow Melt 2024
58 Utah Valley Loss 10-11 20.34 205 6.04% Counts Mar 3rd Snow Melt 2024
1 Colorado** Loss 5-15 0 257 0% Ignored (Why) Apr 13th Rocky Mountain D I Mens Conferences 2024
360 Colorado Mesa** Win 15-3 0 0% Ignored (Why) Apr 13th Rocky Mountain D I Mens Conferences 2024
290 Colorado-C Win 15-6 6.75 225 8.55% Counts (Why) Apr 13th Rocky Mountain D I Mens Conferences 2024
100 Colorado-B Loss 7-15 -35.77 247 8.55% Counts (Why) Apr 14th Rocky Mountain D I Mens Conferences 2024
290 Colorado-C Win 15-0 6.75 225 8.55% Counts (Why) Apr 14th Rocky Mountain D I Mens Conferences 2024
161 Saint Louis Loss 10-12 -26.62 357 9.59% Counts Apr 27th South Central D I College Mens Regionals 2024
128 Houston Loss 7-10 -29.69 281 9.07% Counts Apr 27th South Central D I College Mens Regionals 2024
**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.