(6) #280 Idaho (6-15)

754.23 (134)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
191 Montana State Win 9-8 17.51 60 4.24% Counts Mar 2nd Big Sky Brawl 2019
289 Brigham Young-B Loss 9-11 -13.71 144 4.48% Counts Mar 2nd Big Sky Brawl 2019
202 Northern Arizona Loss 11-12 4.4 64 4.48% Counts Mar 2nd Big Sky Brawl 2019
238 Denver Loss 7-11 -14.74 56 4.36% Counts Mar 2nd Big Sky Brawl 2019
200 Montana Loss 8-15 -15.7 85 4.48% Counts Mar 3rd Big Sky Brawl 2019
76 Utah Loss 7-15 5.6 28 4.48% Counts (Why) Mar 3rd Big Sky Brawl 2019
202 Northern Arizona Win 14-11 24.95 64 4.48% Counts Mar 3rd Big Sky Brawl 2019
59 Oregon State** Loss 4-15 0 13 0% Ignored (Why) Mar 9th Palouse Open 2019
311 Central Washington Win 15-4 23.45 81 4.75% Counts (Why) Mar 9th Palouse Open 2019
162 Washington State Loss 6-15 -12.19 35 4.75% Counts (Why) Mar 9th Palouse Open 2019
312 Portland State Loss 12-15 -21.96 1 4.75% Counts Mar 10th Palouse Open 2019
311 Central Washington Loss 8-15 -34.58 81 4.75% Counts Mar 10th Palouse Open 2019
104 Portland Loss 5-11 -0.82 75 5.18% Counts (Why) Mar 30th 2019 NW Challenge Tier 2 3
200 Montana Loss 9-11 -1.15 85 5.64% Counts Mar 30th 2019 NW Challenge Tier 2 3
289 Brigham Young-B Loss 8-11 -24.45 144 5.64% Counts Mar 30th 2019 NW Challenge Tier 2 3
241 Washington-B Win 11-7 34.94 212 5.49% Counts Mar 30th 2019 NW Challenge Tier 2 3
162 Washington State Loss 7-11 -6.49 35 5.49% Counts Mar 30th 2019 NW Challenge Tier 2 3
99 Lewis & Clark Loss 7-13 2.81 25 5.64% Counts Mar 31st 2019 NW Challenge Tier 2 3
326 Western Washington University-B Win 13-10 9.31 92 5.64% Counts Mar 31st 2019 NW Challenge Tier 2 3
162 Washington State Loss 6-13 -14.64 35 5.64% Counts (Why) Mar 31st 2019 NW Challenge Tier 2 3
241 Washington-B Win 13-8 37.7 212 5.64% Counts Mar 31st 2019 NW Challenge Tier 2 3
**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.