() #91 Sprawl (8-14)

897.37 (26)

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# Opponent Result Effect % of Ranking Status Date Event
78 Rip City Ultimate Win 11-10 7.94 3.76% Jul 7th 2018 San Diego Slammer
- Carbon Loss 8-11 -18.52 3.76% Jul 7th 2018 San Diego Slammer
74 DOGGPOUND Win 11-10 8.78 3.76% Jul 7th 2018 San Diego Slammer
40 Streetgang Loss 6-11 -6.22 3.55% Jul 7th 2018 San Diego Slammer
88 PowderHogs Loss 8-11 -13.83 3.76% Jul 7th 2018 San Diego Slammer
81 Sundowners Win 14-13 6.84 3.76% Jul 8th 2018 San Diego Slammer
86 Green River Swordfish Win 13-6 24.38 3.76% Jul 8th 2018 San Diego Slammer
78 Rip City Ultimate Loss 5-13 -30.1 5.46% Aug 25th CBR Memorial 2018
86 Green River Swordfish Loss 10-11 -5.78 5.46% Aug 25th CBR Memorial 2018
6 Furious George** Loss 4-13 0 0% Ignored Aug 25th CBR Memorial 2018
40 Streetgang Loss 10-12 8.06 5.46% Aug 25th CBR Memorial 2018
63 Sawtooth Loss 6-12 -23.56 5.31% Aug 26th CBR Memorial 2018
38 Dark Star Loss 7-13 -10.1 5.46% Aug 26th CBR Memorial 2018
46 Ghost Train Loss 8-13 -9.68 5.46% Aug 26th CBR Memorial 2018
- Arizona Society of Underachievers** Win 11-4 0 0% Ignored Sep 8th So Cal Mens Sectional Championship 2018
81 Sundowners Win 11-9 19.36 6.07% Sep 8th So Cal Mens Sectional Championship 2018
144 Gridlock Win 11-5 10.08 5.57% Sep 8th So Cal Mens Sectional Championship 2018
40 Streetgang Loss 9-11 8.3 6.07% Sep 8th So Cal Mens Sectional Championship 2018
74 DOGGPOUND Loss 12-13 -1.62 6.07% Sep 9th So Cal Mens Sectional Championship 2018
74 DOGGPOUND Win 11-4 41.28 5.57% Sep 9th So Cal Mens Sectional Championship 2018
17 SoCal Condors Loss 7-11 19.89 5.91% Sep 9th So Cal Mens Sectional Championship 2018
81 Sundowners Loss 4-13 -35.52 6.07% Sep 9th So Cal Mens Sectional Championship 2018
**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.