(2) #79 Nevada-Reno (11-7)

1175.72 (142)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
24 Carleton College-Eclipse Loss 2-10 -2.92 141 6.38% Counts (Why) Feb 4th Stanford Open
53 Cal Poly-SLO Loss 4-7 -17.16 142 5.55% Counts Feb 4th Stanford Open
108 California-San Diego-B Win 5-4 -5.32 141 5.02% Counts Feb 4th Stanford Open
- Humboldt State** Win 13-1 0 144 0% Ignored (Why) Feb 4th Stanford Open
87 Southern California Loss 6-7 -13.79 142 6.04% Counts Feb 5th Stanford Open
178 Chico State Win 8-4 -15.99 146 5.8% Counts (Why) Feb 5th Stanford Open
108 California-San Diego-B Loss 3-4 -16.25 141 4.43% Counts Feb 5th Stanford Open
50 California-Santa Cruz Loss 6-9 -12.3 142 7.28% Counts Feb 18th Santa Clara Rage Tournament
150 Arizona State Win 11-6 -1.86 143 7.75% Counts (Why) Feb 18th Santa Clara Rage Tournament
197 California-B** Win 10-1 0 153 0% Ignored (Why) Feb 18th Santa Clara Rage Tournament
196 California-Davis-B** Win 13-1 0 178 0% Ignored (Why) Feb 18th Santa Clara Rage Tournament
50 California-Santa Cruz Win 7-6 28.13 142 6.78% Counts Feb 19th Santa Clara Rage Tournament
178 Chico State Win 13-6 -20.04 146 8.19% Counts (Why) Feb 19th Santa Clara Rage Tournament
90 Claremont Win 12-4 41.68 140 7.86% Counts (Why) Feb 19th Santa Clara Rage Tournament
108 California-San Diego-B Win 7-4 17.99 141 6.23% Counts (Why) Feb 19th Santa Clara Rage Tournament
- Montana State Loss 4-5 11.92 142 6.33% Counts Mar 4th Big Sky Brawl1
43 Whitman Loss 6-9 -8.48 141 8.17% Counts Mar 4th Big Sky Brawl1
74 Utah Win 8-7 15.46 141 8.17% Counts Mar 4th Big Sky Brawl1
**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.