(1) #51 Western Washington (9-12)

1629.76 (26)

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# Opponent Result Effect % of Ranking Status Date Event
5 Cal Poly-SLO Loss 3-13 -3.53 3.97% Jan 26th Santa Barbara Invite 2019
100 California-Santa Cruz Win 13-11 -1.74 3.97% Jan 26th Santa Barbara Invite 2019
40 Dartmouth Win 12-8 20.6 3.97% Jan 26th Santa Barbara Invite 2019
29 Texas-Dallas Loss 10-13 -7.7 3.97% Jan 26th Santa Barbara Invite 2019
45 California-Santa Barbara Loss 13-14 -3.79 3.97% Jan 27th Santa Barbara Invite 2019
29 Texas-Dallas Loss 10-11 0.71 3.97% Jan 27th Santa Barbara Invite 2019
34 UCLA Loss 5-13 -20.74 3.97% Jan 27th Santa Barbara Invite 2019
100 California-Santa Cruz Win 8-6 1.25 4.06% Feb 16th Presidents Day Invite 2019
34 UCLA Loss 8-10 -7.89 4.6% Feb 16th Presidents Day Invite 2019
45 California-Santa Barbara Loss 4-10 -24.4 4.13% Feb 17th Presidents Day Invite 2019
3 Oregon Loss 4-13 -2.02 4.73% Feb 17th Presidents Day Invite 2019
271 San Diego State** Win 11-4 0 0% Ignored Feb 18th Presidents Day Invite 2019
90 Santa Clara Win 9-6 7.7 4.2% Feb 18th Presidents Day Invite 2019
42 British Columbia Win 13-9 31.14 6.31% Mar 23rd 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Loss 5-13 -5.74 6.31% Mar 23rd 2019 NW Challenge Mens Tier 1
30 Victoria Loss 6-13 -31.23 6.31% Mar 23rd 2019 NW Challenge Mens Tier 1
50 Stanford Win 13-12 8.62 6.31% Mar 24th 2019 NW Challenge Mens Tier 1
59 Oregon State Win 12-9 18.71 6.31% Mar 24th 2019 NW Challenge Mens Tier 1
58 Whitman Win 13-6 37.03 6.31% Mar 24th 2019 NW Challenge Mens Tier 1
6 Brigham Young Loss 6-13 -6.4 6.31% Mar 25th 2019 NW Challenge Mens Tier 1
10 Washington Loss 7-13 -9.61 6.31% Mar 25th 2019 NW Challenge Mens Tier 1
**Blowout Eligible

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.