(27) #53 Colorado State (9-11)

1470.56 (118)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
151 Cal Poly-SLO-B Win 15-7 6.34 45 3.73% Counts (Why) Jan 27th Santa Barbara Invite 2024
15 California Loss 6-15 -5.68 35 3.73% Counts (Why) Jan 27th Santa Barbara Invite 2024
54 California-Santa Barbara Win 15-7 23.24 55 3.73% Counts (Why) Jan 27th Santa Barbara Invite 2024
30 Utah Loss 7-14 -14.6 31 3.73% Counts Jan 27th Santa Barbara Invite 2024
24 British Columbia Loss 6-13 -10.47 42 3.73% Counts (Why) Jan 28th Santa Barbara Invite 2024
43 California-San Diego Loss 12-14 -5.01 47 3.73% Counts Jan 28th Santa Barbara Invite 2024
35 California-Santa Cruz Loss 11-13 -2.41 50 3.73% Counts Jan 28th Santa Barbara Invite 2024
17 Brigham Young Loss 10-13 4.55 39 5.59% Counts Mar 15th College Mens Centex Tier 1
47 Oklahoma Christian Loss 9-12 -17.52 30 5.59% Counts Mar 16th College Mens Centex Tier 1
48 Missouri Loss 10-12 -11.49 13 5.59% Counts Mar 16th College Mens Centex Tier 1
44 Tulane Win 8-7 10.26 21 4.97% Counts Mar 16th College Mens Centex Tier 1
20 Northeastern Loss 8-13 -8.08 65 5.59% Counts Mar 17th College Mens Centex Tier 1
121 Iowa State Loss 10-11 -26.1 41 5.59% Counts Mar 17th College Mens Centex Tier 1
132 Arkansas Win 9-5 9.71 34 5.39% Counts (Why) Mar 30th Huck Finn 2024
88 Kentucky Win 10-8 6.15 265 6.11% Counts Mar 30th Huck Finn 2024
65 Stanford Win 9-5 26.4 80 5.39% Counts (Why) Mar 30th Huck Finn 2024
91 Indiana Loss 9-10 -21.76 100 6.28% Counts Mar 30th Huck Finn 2024
76 Purdue Win 12-7 27.28 56 6.28% Counts (Why) Mar 31st Huck Finn 2024
118 Michigan Tech Win 13-7 17.46 3 6.28% Counts (Why) Mar 31st Huck Finn 2024
108 Wisconsin-Milwaukee Win 7-6 -7.99 78 5.19% Counts Mar 31st Huck Finn 2024
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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.