(81) #241 Washington-B (10-14)

888.48 (212)

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# Opponent Result Effect % of Ranking Status Date Event
402 Oregon State-B Win 13-8 -6.69 3.41% Jan 26th Flat Tail Open 2019 Mens
116 Nevada-Reno Loss 3-13 -6.88 3.41% Jan 26th Flat Tail Open 2019 Mens
446 Lewis & Clark-B** Win 15-0 0 0% Ignored Jan 26th Flat Tail Open 2019 Mens
58 Whitman Loss 7-13 4.72 3.41% Jan 26th Flat Tail Open 2019 Mens
402 Oregon State-B Win 15-7 -3.02 3.41% Jan 27th Flat Tail Open 2019 Mens
180 Humboldt State Win 15-13 13.57 3.41% Jan 27th Flat Tail Open 2019 Mens
326 Western Washington University-B Win 15-8 9.12 3.41% Jan 27th Flat Tail Open 2019 Mens
74 Arizona Win 10-8 33.04 3.73% Feb 9th Stanford Open 2019
41 Las Positas** Loss 3-13 0 0% Ignored Feb 9th Stanford Open 2019
99 Lewis & Clark Loss 5-11 -4.72 3.51% Feb 9th Stanford Open 2019
100 California-Santa Cruz Loss 5-11 -4.73 3.51% Feb 9th Stanford Open 2019
192 Gonzaga Loss 8-13 -17.28 4.55% Mar 2nd 19th Annual PLU BBQ Open
168 Whitworth Win 13-5 38.12 4.55% Mar 2nd 19th Annual PLU BBQ Open
99 Lewis & Clark Loss 9-11 10.55 4.55% Mar 3rd 19th Annual PLU BBQ Open
192 Gonzaga Win 13-7 33 4.55% Mar 3rd 19th Annual PLU BBQ Open
104 Portland Loss 5-11 -8.3 5.27% Mar 30th 2019 NW Challenge Tier 2 3
200 Montana Loss 5-11 -28.03 5.27% Mar 30th 2019 NW Challenge Tier 2 3
289 Brigham Young-B Win 11-8 11.45 5.74% Mar 30th 2019 NW Challenge Tier 2 3
280 Idaho Loss 7-11 -35.56 5.59% Mar 30th 2019 NW Challenge Tier 2 3
162 Washington State Loss 10-12 -1.04 5.74% Mar 30th 2019 NW Challenge Tier 2 3
291 Pacific Lutheran Win 13-1 25.26 5.74% Mar 31st 2019 NW Challenge Tier 2 3
104 Portland Loss 3-13 -9.09 5.74% Mar 31st 2019 NW Challenge Tier 2 3
200 Montana Loss 8-11 -16.43 5.74% Mar 31st 2019 NW Challenge Tier 2 3
280 Idaho Loss 8-13 -38.38 5.74% Mar 31st 2019 NW Challenge Tier 2 3
**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.