(8) #137 BATL Cows (9-12)

934.52 (5)

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# Opponent Result Effect % of Ranking Status Date Event
232 Orbit Win 12-3 0.52 3.85% Counts (Why) Jun 15th ATL Classic 2019
47 Huntsville Outlaws Loss 3-13 -2.52 4.01% Counts (Why) Jun 15th ATL Classic 2019
226 Bold City Win 13-6 1.65 4.01% Counts (Why) Jun 15th ATL Classic 2019
205 Mississippi Blues Win 13-5 9.18 4.01% Counts (Why) Jun 15th ATL Classic 2019
227 Baywatch Win 13-4 1.37 4.01% Counts (Why) Jun 16th ATL Classic 2019
71 Trash Pandas Loss 6-12 -8.87 3.91% Counts Jun 16th ATL Classic 2019
92 m'kay Ultimate Loss 3-7 -10.97 2.91% Counts (Why) Jun 16th ATL Classic 2019
88 Mutiny Win 13-8 41.66 5.24% Counts Jul 20th 2019 Club Terminus
99 Shakedown Loss 5-13 -23.83 5.24% Counts (Why) Jul 20th 2019 Club Terminus
163 Magic City Mayhem Win 11-9 4.62 5.24% Counts Jul 20th 2019 Club Terminus
62 Memphis STAX Loss 10-12 9.21 5.24% Counts Jul 20th 2019 Club Terminus
92 m'kay Ultimate Loss 9-13 -10.19 5.24% Counts Jul 21st 2019 Club Terminus
88 Mutiny Loss 6-13 -18.95 5.24% Counts (Why) Jul 21st 2019 Club Terminus
163 Magic City Mayhem Win 9-8 -2.12 4.96% Counts Jul 21st 2019 Club Terminus
110 sKNO cone Loss 10-12 -7.98 6.15% Counts Aug 10th HoDown ShowDown 23 GOAT
35 Superlame** Loss 4-13 0 0% Ignored (Why) Aug 10th HoDown ShowDown 23 GOAT
71 Trash Pandas Loss 7-13 -12.87 6.15% Counts Aug 10th HoDown ShowDown 23 GOAT
88 Mutiny Loss 10-12 1.26 6.15% Counts Aug 10th HoDown ShowDown 23 GOAT
84 FlyTrap Win 11-9 34.88 6.15% Counts Aug 11th HoDown ShowDown 23 GOAT
148 Rowdy Win 14-10 21.23 6.15% Counts Aug 11th HoDown ShowDown 23 GOAT
169 APEX Loss 13-15 -26.91 6.15% Counts Aug 11th HoDown ShowDown 23 GOAT
**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.