(1) #58 Whitman (13-9)

1579.65 (12)

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# Opponent Result Effect % of Ranking Status Date Event
402 Oregon State-B** Win 13-1 0 0% Ignored Jan 26th Flat Tail Open 2019 Mens
116 Nevada-Reno Win 13-7 12.2 4.3% Jan 26th Flat Tail Open 2019 Mens
99 Lewis & Clark Win 15-5 17.03 4.3% Jan 26th Flat Tail Open 2019 Mens
241 Washington-B Win 13-7 -6 4.3% Jan 26th Flat Tail Open 2019 Mens
3 Oregon Loss 8-15 2 4.3% Jan 27th Flat Tail Open 2019 Mens
192 Gonzaga Win 15-7 1.93 4.3% Jan 27th Flat Tail Open 2019 Mens
357 San Jose State** Win 13-0 0 0% Ignored Feb 9th Stanford Open 2019
90 Santa Clara Win 10-8 3.44 4.7% Feb 9th Stanford Open 2019
184 California-B Win 13-2 2.68 4.83% Feb 9th Stanford Open 2019
367 Texas-B** Win 13-5 0 0% Ignored Feb 9th Stanford Open 2019
116 Nevada-Reno Win 8-3 12.25 3.75% Feb 10th Stanford Open 2019
41 Las Positas Win 7-6 9.31 3.99% Feb 10th Stanford Open 2019
21 California Loss 5-6 5.29 3.67% Feb 10th Stanford Open 2019
180 Humboldt State Win 9-5 0.34 4.14% Feb 10th Stanford Open 2019
6 Brigham Young Loss 9-13 10 6.82% Mar 23rd 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Loss 7-13 0.53 6.82% Mar 23rd 2019 NW Challenge Mens Tier 1
30 Victoria Loss 11-13 -3.12 6.82% Mar 23rd 2019 NW Challenge Mens Tier 1
42 British Columbia Loss 9-13 -23.78 6.82% Mar 24th 2019 NW Challenge Mens Tier 1
51 Western Washington Loss 6-13 -40.28 6.82% Mar 24th 2019 NW Challenge Mens Tier 1
50 Stanford Loss 10-13 -20.15 6.82% Mar 25th 2019 NW Challenge Mens Tier 1
59 Oregon State Win 13-10 22.76 6.82% Mar 25th 2019 NW Challenge Mens Tier 1
10 Washington Loss 2-9 -8.09 5.65% 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.