(1) #61 Notorious C.L.E. (7-13)

596.45 (19)

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# Opponent Result Effect % of Ranking Status Date Event
42 Pine Baroness Loss 5-15 -7.99 4.39% Jul 14th Old Line Classic 2018
64 Suffrage Win 9-8 1.54 4.16% Jul 14th Old Line Classic 2018
53 Backhanded Loss 4-15 -21.4 4.39% Jul 14th Old Line Classic 2018
79 DINO** Win 14-4 0 0% Ignored Jul 14th Old Line Classic 2018
67 Broad City Win 13-11 3.18 4.39% Jul 15th Old Line Classic 2018
- DC Rogue Win 10-8 -5.41 4.28% Jul 15th Old Line Classic 2018
29 Virginia Rebellion** Loss 4-13 0 0% Ignored Aug 11th Chesapeake Open 2018
45 Outbreak Loss 9-13 -6.4 5.44% Aug 11th Chesapeake Open 2018
56 Brooklyn Book Club Loss 6-11 -24.38 5.14% Aug 11th Chesapeake Open 2018
35 Hot Metal Loss 5-13 -3.36 5.44% Aug 11th Chesapeake Open 2018
53 Backhanded Loss 8-10 -7.17 5.29% Aug 12th Chesapeake Open 2018
56 Brooklyn Book Club Loss 8-13 -22.94 5.44% Aug 12th Chesapeake Open 2018
55 Sureshot Loss 9-12 -18.89 7.1% Sep 15th East Plains Womens Sectional Championship 2018
75 Autonomous Win 13-1 10.66 7.1% Sep 15th East Plains Womens Sectional Championship 2018
57 Helix Win 15-11 37 7.49% Sep 22nd Great Lakes Womens Regional Championship 2018
55 Sureshot Loss 9-10 -2.17 7.49% Sep 22nd Great Lakes Womens Regional Championship 2018
31 Indy Rogue Loss 6-13 0.61 7.49% Sep 22nd Great Lakes Womens Regional Championship 2018
10 Nemesis** Loss 2-13 0 0% Ignored Sep 22nd Great Lakes Womens Regional Championship 2018
55 Sureshot Win 14-8 51.33 7.49% Sep 23rd Great Lakes Womens Regional Championship 2018
31 Indy Rogue Loss 11-15 18.32 7.49% Sep 23rd Great Lakes Womens Regional 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.