(5) #97 Climax (11-11)

905 (69)

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# Opponent Result Effect % of Ranking Status Date Event
138 Midnight Meat Train Loss 10-13 -25.55 3.67% Jul 22nd Heavyweights 2017
117 Satellite Win 13-7 15.39 3.67% Jul 22nd Heavyweights 2017
124 DeMo Win 13-11 1.98 3.67% Jul 22nd Heavyweights 2017
54 Beachfront Property Loss 9-15 -8 3.67% Jul 22nd Heavyweights 2017
81 South Shore Line Win 15-10 20.85 3.67% Jul 23rd Heavyweights 2017
98 Dallas United: Desperados Win 15-10 17.13 3.67% Jul 23rd Heavyweights 2017
76 Enigma Loss 8-11 -9.49 3.67% Jul 23rd Heavyweights 2017
63 Haymaker Loss 6-13 -18 4.54% Aug 19th Cooler Classic 29
89 Scythe Loss 11-13 -8.89 4.54% Aug 19th Cooler Classic 29
124 DeMo Loss 11-13 -19.28 4.54% Aug 19th Cooler Classic 29
133 Auxiliary Loss 11-13 -24.11 4.54% Aug 19th Cooler Classic 29
- Mississippi Valley Tundra Swans Win 13-3 5.09 4.54% Aug 20th Cooler Classic 29
155 Cream City Crooks Loss 13-15 -35.14 4.54% Aug 20th Cooler Classic 29
130 DingWop Win 13-10 3.46 4.54% Aug 20th Cooler Classic 29
30 Mad Men Loss 11-13 19.3 5.32% Sep 9th 2017 Northwest Plains Mens Sectionals
109 houSE Win 13-7 27.9 5.32% Sep 9th 2017 Northwest Plains Mens Sectionals
- Snip Snip Win 13-5 4.85 5.32% Sep 9th 2017 Northwest Plains Mens Sectionals
- Green Bay Quackers Win 13-9 7.38 5.32% Sep 9th 2017 Northwest Plains Mens Sectionals
88 Wisconsin Hops Win 15-13 14.6 5.32% Sep 10th 2017 Northwest Plains Mens Sectionals
49 MKE Loss 11-15 -1.95 5.32% Sep 10th 2017 Northwest Plains Mens Sectionals
105 THE BODY Win 15-6 32 5.32% Sep 10th 2017 Northwest Plains Mens Sectionals
68 Imperial Loss 9-15 -18.84 5.32% Sep 10th 2017 Northwest Plains Mens Sectionals
**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.