(10) #73 Richmond (12-8)

1364.26 (20)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
224 American Win 11-5 -1.24 64 3.68% Counts (Why) Jan 27th Mid Atlantic Warm Up
142 Boston University Loss 8-9 -16.56 17 3.79% Counts Jan 27th Mid Atlantic Warm Up
68 James Madison Loss 10-12 -9.41 20 4.01% Counts Jan 27th Mid Atlantic Warm Up
116 Liberty Win 12-7 14.16 8 4.01% Counts (Why) Jan 27th Mid Atlantic Warm Up
70 Case Western Reserve Loss 12-14 -9.12 4 4.01% Counts Jan 28th Mid Atlantic Warm Up
98 Dartmouth Win 12-11 0.27 16 4.01% Counts Jan 28th Mid Atlantic Warm Up
61 William & Mary Win 14-13 8.04 50 4.01% Counts Jan 28th Mid Atlantic Warm Up
119 Berry Win 13-11 2.08 6 5.35% Counts Mar 2nd FCS D III Tune Up 2024
51 Franciscan Loss 11-12 0.4 7 5.35% Counts Mar 2nd FCS D III Tune Up 2024
217 Kenyon** Win 13-3 0 20 0% Ignored (Why) Mar 2nd FCS D III Tune Up 2024
122 Oberlin Win 12-11 -4.9 43 5.35% Counts Mar 2nd FCS D III Tune Up 2024
59 Whitman Loss 11-13 -8.83 10 5.35% Counts Mar 3rd FCS D III Tune Up 2024
80 Lewis & Clark Win 13-12 5.67 16 5.35% Counts Mar 3rd FCS D III Tune Up 2024
137 Union (Tennessee) Win 13-6 18.43 16 5.35% Counts (Why) Mar 3rd FCS D III Tune Up 2024
84 Appalachian State Loss 12-14 -18.67 40 6.74% Counts Mar 30th Atlantic Coast Open 2024
38 Duke Loss 11-12 7.33 16 6.74% Counts Mar 30th Atlantic Coast Open 2024
97 Florida State Win 13-11 8.12 0 6.74% Counts Mar 30th Atlantic Coast Open 2024
87 Tennessee-Chattanooga Win 13-12 5.11 164 6.74% Counts Mar 30th Atlantic Coast Open 2024
85 Carnegie Mellon Loss 13-14 -12.35 20 6.74% Counts Mar 31st Atlantic Coast Open 2024
126 Lehigh Win 15-11 11.72 13 6.74% Counts Mar 31st Atlantic Coast Open 2024
**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.