(3) #194 Messengers-B (3-17)

308.64 (88)

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# Opponent Result Effect % of Ranking Status Date Event
147 Louisiana Second Line Loss 6-13 -12 6.41% Counts (Why) Jun 29th Texas 2 Finger Mens and Womens
66 Gaucho** Loss 5-13 0 0% Ignored (Why) Jun 29th Texas 2 Finger Mens and Womens
85 Texas United Loss 6-13 14.72 6.41% Counts (Why) Jun 29th Texas 2 Finger Mens and Womens
26 Nitro** Loss 0-13 0 0% Ignored (Why) Jun 29th Texas 2 Finger Mens and Womens
184 Alamode Loss 11-13 -8.67 6.41% Counts Jun 30th Texas 2 Finger Mens and Womens
144 Glycerine Loss 5-13 -10.31 6.41% Counts (Why) Jun 30th Texas 2 Finger Mens and Womens
202 Texas Toast Loss 11-13 -23.8 6.41% Counts Jun 30th Texas 2 Finger Mens and Womens
66 Gaucho** Loss 4-15 0 0% Ignored (Why) Jul 13th Riverside Classic 2019
168 E.V.I.L. Win 13-12 28.96 7.13% Counts Jul 13th Riverside Classic 2019
85 Texas United** Loss 6-15 0 0% Ignored (Why) Jul 13th Riverside Classic 2019
129 Papa Bear Loss 4-12 -0.73 6.85% Counts (Why) Jul 14th Riverside Classic 2019
144 Glycerine Loss 13-14 24.94 7.13% Counts Jul 14th Riverside Classic 2019
200 Surrilic Audovice Loss 12-15 -28.9 7.13% Counts Jul 14th Riverside Classic 2019
184 Alamode Loss 8-13 -33.95 7.94% Counts Jul 27th PBJ 2019
129 Papa Bear Loss 2-13 -0.85 7.94% Counts (Why) Jul 27th PBJ 2019
113 Gamble** Loss 5-13 0 0% Ignored (Why) Jul 27th PBJ 2019
168 E.V.I.L. Loss 5-13 -30.01 7.94% Counts (Why) Jul 27th PBJ 2019
184 Alamode Win 15-12 34.73 7.94% Counts Jul 28th PBJ 2019
66 Gaucho** Loss 4-13 0 0% Ignored (Why) Jul 28th PBJ 2019
199 Texas Heatwave Win 15-7 46.33 7.94% Counts (Why) Jul 28th PBJ 2019
**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.