(1) #178 Eat Lightning (8-13)

649.39 (23)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
221 Replay Win 8-7 -5.83 7 3.4% Counts Jul 15th Boston Invite 2023
78 Deadweight Loss 1-13 -3.49 47 3.83% Counts (Why) Jul 15th Boston Invite 2023
132 Harfang ultimate Loss 5-13 -14.55 2 3.83% Counts (Why) Jul 15th Boston Invite 2023
154 Moontower Loss 6-11 -15.09 6 3.62% Counts Jul 15th Boston Invite 2023
84 Buffalo Lake Effect Loss 7-11 0.64 30 4.37% Counts Aug 5th Philly Open 2023
146 Heavy Flow Loss 8-11 -8.99 11 4.49% Counts Aug 5th Philly Open 2023
242 Ultra Instinct Win 11-5 2.65 11 4.12% Counts (Why) Aug 5th Philly Open 2023
175 Philly Twist Loss 7-8 -4.94 17 3.99% Counts Aug 5th Philly Open 2023
216 Brooklyn Hive Win 8-5 8.08 13 3.72% Counts (Why) Aug 6th Philly Open 2023
195 Swampbenders Win 10-7 12.82 10 4.25% Counts Aug 6th Philly Open 2023
149 ColorBomb Win 10-9 15.63 13 5.27% Counts Aug 26th The Incident 2023
111 Lampshade Loss 4-13 -13.58 1 5.27% Counts (Why) Aug 26th The Incident 2023
216 Brooklyn Hive Win 11-8 6.75 13 5.27% Counts Aug 26th The Incident 2023
62 Funk Loss 7-13 3.06 48 5.27% Counts Aug 26th The Incident 2023
147 FLI Win 10-9 16.36 12 5.27% Counts Aug 27th The Incident 2023
62 Funk Loss 5-10 1.9 48 4.68% Counts Aug 27th The Incident 2023
78 Deadweight Loss 5-13 -5.47 47 5.87% Counts (Why) Sep 9th 2023 Mixed Metro New York Sectional Championship
155 NY Swipes Loss 9-10 1 22 5.87% Counts Sep 9th 2023 Mixed Metro New York Sectional Championship
162 Room Temperature Loss 9-10 0.03 33 5.87% Counts Sep 9th 2023 Mixed Metro New York Sectional Championship
71 Grand Army Loss 4-13 -2.81 70 5.87% Counts (Why) Sep 9th 2023 Mixed Metro New York Sectional Championship
216 Brooklyn Hive Win 12-9 6.3 13 5.87% Counts Sep 10th 2023 Mixed Metro New York Sectional Championship
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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.