#98 Nevada-Reno (10-10)

1104.63

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
103 Humboldt State Win 12-11 5.29 5.3% Counts Jan 20th Flat Tail Open Tournament 2018
125 Gonzaga Win 15-14 -10.2 5.3% Counts Jan 20th Flat Tail Open Tournament 2018
155 Portland State** Win 15-3 0 0% Ignored (Why) Jan 20th Flat Tail Open Tournament 2018
88 Puget Sound Loss 5-7 -11.96 4.21% Counts Jan 21st Flat Tail Open Tournament 2018
131 Central Washington Win 13-6 13.19 5.3% Counts (Why) Jan 21st Flat Tail Open Tournament 2018
109 Lewis & Clark Win 7-6 -0.18 4.38% Counts Jan 21st Flat Tail Open Tournament 2018
56 Oregon State Loss 6-10 -9.46 4.86% Counts Jan 21st Flat Tail Open Tournament 2018
20 Brigham Young** Loss 4-13 0 0% Ignored (Why) Jan 27th Santa Barbara Invitational 2018
84 California-San Diego Loss 7-13 -25.44 5.62% Counts Jan 27th Santa Barbara Invitational 2018
21 Western Washington Loss 7-13 3.87 5.62% Counts Jan 27th Santa Barbara Invitational 2018
52 California-Davis Loss 10-13 -0.28 5.62% Counts Jan 27th Santa Barbara Invitational 2018
75 Arizona Win 13-8 41.48 5.62% Counts Jan 28th Santa Barbara Invitational 2018
86 Washington University Loss 12-13 -0.99 5.62% Counts Jan 28th Santa Barbara Invitational 2018
141 Cal Poly-SLO-B Win 8-6 -15.39 5.41% Counts Feb 10th Stanford Open 2018
64 Las Positas Loss 7-10 -8.17 5.96% Counts Feb 10th Stanford Open 2018
35 Santa Clara Loss 6-11 0.08 5.96% Counts Feb 10th Stanford Open 2018
85 Boston College Loss 9-11 -8.2 6.3% Counts Feb 11th Stanford Open 2018
115 Occidental Win 10-9 -5.12 6.3% Counts Feb 11th Stanford Open 2018
96 California-Santa Cruz Win 12-8 30.17 6.3% Counts Feb 11th Stanford Open 2018
142 Sacramento State Win 13-3 1.67 6.3% Counts (Why) Feb 11th Stanford Open 2018
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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.