(1) #50 Stanford (8-16)

1632.74 (25)

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# Opponent Result Effect % of Ranking Status Date Event
56 California-San Diego Win 12-9 10.57 3.34% Jan 26th Santa Barbara Invite 2019
30 Victoria Loss 8-13 -12.56 3.34% Jan 26th Santa Barbara Invite 2019
45 California-Santa Barbara Loss 11-13 -6.86 3.34% Jan 26th Santa Barbara Invite 2019
16 Southern California Loss 8-13 -5.28 3.34% Jan 27th Santa Barbara Invite 2019
29 Texas-Dallas Win 13-11 12.73 3.34% Jan 27th Santa Barbara Invite 2019
10 Washington Loss 11-12 9.92 3.34% Jan 27th Santa Barbara Invite 2019
111 Washington University Win 10-9 -7.58 3.75% Feb 9th Stanford Open 2019
254 Cal Poly-Pomona Win 13-11 -21.98 3.75% Feb 9th Stanford Open 2019
125 Colorado School of Mines Win 11-10 -8.95 3.75% Feb 9th Stanford Open 2019
62 Duke Win 8-6 7.28 3.22% Feb 10th Stanford Open 2019
116 Nevada-Reno Loss 5-6 -13.64 2.86% Feb 10th Stanford Open 2019
6 Brigham Young Loss 10-13 8.12 4.46% Mar 1st Stanford Invite 2019
1 North Carolina Loss 2-13 -0.04 4.46% Mar 2nd Stanford Invite 2019
30 Victoria Loss 11-13 -4.47 4.46% Mar 2nd Stanford Invite 2019
13 Wisconsin Loss 10-11 11.36 4.46% Mar 2nd Stanford Invite 2019
49 Northwestern Loss 11-12 -5.61 4.46% Mar 3rd Stanford Invite 2019
10 Washington Loss 6-9 -0.28 3.97% Mar 3rd Stanford Invite 2019
17 Minnesota Loss 6-13 -13.16 4.46% Mar 3rd Stanford Invite 2019
59 Oregon State Loss 12-14 -16.34 5.31% Mar 23rd 2019 NW Challenge Mens Tier 1
6 Brigham Young Loss 8-13 0.33 5.31% Mar 23rd 2019 NW Challenge Mens Tier 1
42 British Columbia Win 13-7 33.55 5.31% Mar 23rd 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Loss 10-12 15.34 5.31% Mar 24th 2019 NW Challenge Mens Tier 1
51 Western Washington Loss 12-13 -7.17 5.31% Mar 24th 2019 NW Challenge Mens Tier 1
58 Whitman Win 13-10 15.42 5.31% 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.