(1) #233 Auburn (3-17)

124.76 (1)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
163 Alabama Loss 0-12 -4.64 0 5.24% Counts (Why) Feb 22nd Mardi Gras XXXVII
247 Alabama-Birmingham Win 4-3 0.59 1 3.32% Counts (Why) Feb 22nd Mardi Gras XXXVII
189 LSU Loss 8-10 5.78 1 5.32% Counts Feb 22nd Mardi Gras XXXVII
257 Mississippi State Win 10-3 10.77 1 4.77% Counts (Why) Feb 22nd Mardi Gras XXXVII
163 Alabama Loss 2-11 -5.66 0 6.32% Counts (Why) Mar 22nd Moxie Madness 2025
155 Berry Loss 4-10 -3.03 0 6.01% Counts (Why) Mar 22nd Moxie Madness 2025
32 Ohio** Loss 0-13 0 1 0% Ignored (Why) Mar 22nd Moxie Madness 2025
71 Union (Tennessee)** Loss 1-13 0 1 0% Ignored (Why) Mar 23rd Moxie Madness 2025
203 Tennessee-Chattanooga Loss 5-9 -16.25 0 5.91% Counts Mar 23rd Moxie Madness 2025
209 Vanderbilt Loss 7-8 2.02 1 6.12% Counts Mar 23rd Moxie Madness 2025
209 Vanderbilt Loss 7-8 2.44 1 7.27% Counts Apr 12th Gulf Coast D I Womens Conferences 2025
124 Jacksonville State** Loss 2-13 0 1 0% Ignored (Why) Apr 12th Gulf Coast D I Womens Conferences 2025
177 Tulane Loss 7-8 25.29 1 7.27% Counts Apr 12th Gulf Coast D I Womens Conferences 2025
189 LSU Loss 7-10 -2.03 1 7.74% Counts Apr 13th Gulf Coast D I Womens Conferences 2025
209 Vanderbilt Loss 4-10 -34.19 1 7.15% Counts (Why) Apr 13th Gulf Coast D I Womens Conferences 2025
81 Tennessee** Loss 1-15 0 0 0% Ignored (Why) Apr 26th Southeast D I College Womens Regionals 2025
65 Florida** Loss 0-15 0 1 0% Ignored (Why) Apr 26th Southeast D I College Womens Regionals 2025
169 Miami (Florida) Loss 5-15 -11.71 0 9.19% Counts (Why) Apr 26th Southeast D I College Womens Regionals 2025
212 Georgia-B Win 10-9 27.6 0 9.19% Counts Apr 27th Southeast D I College Womens Regionals 2025
172 Florida State Loss 8-12 3.42 1 9.19% Counts Apr 27th Southeast D I College Womens Regionals 2025
**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.