(23) #191 Midnight Meat Train (6-13)

526.4 (35)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
30 Black Market I** Loss 2-13 0 0 0% Ignored (Why) Jul 6th Motown Throwdown 2019
204 Red Imp.ala Win 13-6 26.75 13 5.03% Counts (Why) Jul 6th Motown Throwdown 2019
125 Dynasty Loss 7-13 -9.31 78 5.03% Counts Jul 6th Motown Throwdown 2019
137 Babe Loss 9-13 -5.46 44 5.03% Counts Jul 7th Motown Throwdown 2019
233 Buffalo Open Win 13-5 7.64 122 5.03% Counts (Why) Jul 7th Motown Throwdown 2019
200 NEO Win 13-7 26.21 168 5.03% Counts (Why) Jul 7th Motown Throwdown 2019
205 BlackER Market X Win 11-8 14.27 76 5.03% Counts Jul 7th Motown Throwdown 2019
54 Battery** Loss 1-13 0 93 0% Ignored (Why) Aug 3rd Heavyweights 2019
101 Imperial Loss 5-13 -5.75 33 6.23% Counts (Why) Aug 3rd Heavyweights 2019
237 Kettering Win 13-5 2.12 52 6.23% Counts (Why) Aug 3rd Heavyweights 2019
175 Milwaukee Revival Loss 9-13 -20.4 72 6.23% Counts Aug 4th Heavyweights 2019
102 THE BODY Loss 6-13 -6.46 13 6.23% Counts (Why) Aug 4th Heavyweights 2019
149 Ditto A Loss 9-13 -11.3 146 6.23% Counts Aug 4th Heavyweights 2019
23 CLE Smokestack** Loss 2-11 0 177 0% Ignored (Why) Sep 7th East Plains Mens Club Sectional Championship 2019
200 NEO Loss 9-11 -27.63 168 8.13% Counts Sep 7th East Plains Mens Club Sectional Championship 2019
225 Flying Dutchmen Win 11-5 25.86 87 7.46% Counts (Why) Sep 7th East Plains Mens Club Sectional Championship 2019
129 Kentucky Flying Circus Loss 8-11 0.57 99 8.13% Counts Sep 7th East Plains Mens Club Sectional Championship 2019
82 Black Lung Loss 2-11 -0.01 120 7.46% Counts (Why) Sep 8th East Plains Mens Club Sectional Championship 2019
128 Enigma Loss 4-11 -18.33 31 7.46% Counts (Why) Sep 8th East Plains Mens Club Sectional Championship 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.