(4) #202 Spring Break '93 (9-9)

660.13 (103)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
189 Dirty Laundry Win 13-9 34.18 105 6.32% Counts Aug 19th Ow My Knee 2023
238 Mohawk Valley Wild Win 13-6 18.03 107 6.32% Counts (Why) Aug 19th Ow My Knee 2023
209 Long Island Riff Raff Win 13-11 10.68 104 6.32% Counts Aug 19th Ow My Knee 2023
189 Dirty Laundry Win 13-8 39.42 105 6.32% Counts Aug 20th Ow My Knee 2023
226 Buffalo Frostbite Loss 9-13 -39.95 108 6.32% Counts Aug 20th Ow My Knee 2023
238 Mohawk Valley Wild Win 13-6 18.03 107 6.32% Counts (Why) Aug 20th Ow My Knee 2023
102 Harvey Cats Loss 5-13 -3.78 83 6.67% Counts (Why) Aug 26th The Incident 2023
70 OAT Loss 7-13 12.99 108 6.67% Counts Aug 26th The Incident 2023
154 Odyssey Loss 2-13 -26.13 114 6.67% Counts (Why) Aug 26th The Incident 2023
209 Long Island Riff Raff Loss 9-13 -34.94 104 6.67% Counts Aug 26th The Incident 2023
235 Adelphos Loss 10-11 -29.33 102 6.67% Counts Aug 27th The Incident 2023
252 Deepfake Win 12-6 -2.16 102 6.49% Counts (Why) Aug 27th The Incident 2023
24 Blueprint** Loss 2-15 0 41 0% Ignored (Why) Sep 9th 2023 Mens Metro New York Sectional Championship
252 Deepfake Win 15-10 -12.57 102 7.42% Counts Sep 9th 2023 Mens Metro New York Sectional Championship
94 Magma Bears** Loss 5-15 0 65 0% Ignored (Why) Sep 9th 2023 Mens Metro New York Sectional Championship
230 Bartle Boys Win 11-8 10.93 108 7.42% Counts Sep 10th 2023 Mens Metro New York Sectional Championship
209 Long Island Riff Raff Win 13-12 4.36 104 7.42% Counts Sep 10th 2023 Mens Metro New York Sectional Championship
94 Magma Bears** Loss 4-15 0 65 0% Ignored (Why) Sep 10th 2023 Mens Metro New York Sectional Championship
**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.