() #51 Georgetown (12-7)

1421.83 (120)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
103 Virginia Commonwealth Loss 7-8 -32.51 117 5.36% Counts Jan 28th Winta Binta Vinta
14 Virginia Loss 5-9 -1.1 139 5.18% Counts Jan 28th Winta Binta Vinta
41 James Madison Loss 7-10 -17.39 120 5.7% Counts Jan 28th Winta Binta Vinta
57 Virginia Tech Win 9-7 11.4 118 5.53% Counts Jan 28th Winta Binta Vinta
80 American Loss 7-10 -38.58 154 5.7% Counts Jan 29th Winta Binta Vinta
130 Liberty** Win 9-3 0 124 0% Ignored (Why) Jan 29th Winta Binta Vinta
103 Virginia Commonwealth Win 7-3 6.9 117 4.38% Counts (Why) Jan 29th Winta Binta Vinta
93 South Carolina-B Win 8-3 12.54 128 5.27% Counts (Why) Feb 11th Cutlass Classic
110 Charleston Win 10-1 6.1 131 5.91% Counts (Why) Feb 11th Cutlass Classic
37 East Carolina Win 7-5 27.43 125 5.38% Counts Feb 11th Cutlass Classic
184 Georgetown-B** Win 13-0 0 138 0% Ignored (Why) Feb 11th Cutlass Classic
184 Georgetown-B** Win 13-1 0 138 0% Ignored (Why) Feb 12th Cutlass Classic
37 East Carolina Loss 5-9 -23.12 125 5.81% Counts Feb 12th Cutlass Classic
85 Catholic Win 11-6 22.87 84 9.06% Counts (Why) Mar 25th Bonanza 2023
41 James Madison Loss 7-8 -2.12 120 8.51% Counts Mar 25th Bonanza 2023
103 Virginia Commonwealth Win 13-3 15.97 117 9.57% Counts (Why) Mar 25th Bonanza 2023
135 Mary Washington** Win 11-2 0 119 0% Ignored (Why) Mar 25th Bonanza 2023
57 Virginia Tech Win 12-6 50.83 118 9.32% Counts (Why) Mar 26th Bonanza 2023
41 James Madison Loss 7-11 -37.48 120 9.32% Counts Mar 26th Bonanza 2023
**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.