(11) #163 LSU (5-13)

513.21 (55)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
93 Rice Loss 7-11 2.55 29 5.47% Counts Mar 19th Womens Centex
162 Oklahoma Loss 8-11 -21.72 18 5.62% Counts Mar 19th Womens Centex
89 Texas-Dallas Loss 4-11 -3.49 27 5.16% Counts (Why) Mar 19th Womens Centex
79 MIT Loss 7-11 6.2 30 5.47% Counts Mar 19th Womens Centex
165 North Texas Win 7-2 25.28 35 4.08% Counts (Why) Mar 20th Womens Centex
77 Texas State Loss 3-11 -0.8 24 5.16% Counts (Why) Mar 20th Womens Centex
87 Alabama Loss 8-12 6.36 48 5.95% Counts Mar 26th T Town Throwdown 2022
51 Central Florida** Loss 0-13 0 40 0% Ignored (Why) Mar 26th T Town Throwdown 2022
220 Mississippi State Win 13-2 10.24 45 5.95% Counts (Why) Mar 26th T Town Throwdown 2022
87 Alabama Loss 6-12 -2.32 48 5.8% Counts Mar 27th T Town Throwdown 2022
148 Alabama-Huntsville Win 11-6 40.29 38 5.63% Counts (Why) Mar 27th T Town Throwdown 2022
140 Auburn Win 11-10 24.8 49 7.5% Counts Apr 23rd Gulf Coast D I College Womens CC 2022
127 Jacksonville State Loss 6-12 -26.17 49 7.3% Counts Apr 23rd Gulf Coast D I College Womens CC 2022
124 Tulane Loss 7-14 -25.35 53 7.5% Counts Apr 23rd Gulf Coast D I College Womens CC 2022
217 Vanderbilt Win 15-1 14.91 53 7.5% Counts (Why) Apr 24th Gulf Coast D I College Womens CC 2022
140 Auburn Loss 7-13 -32.53 49 7.95% Counts Apr 30th Southeast D I College Womens Regionals 2022
181 Georgia Tech-B Loss 10-11 -20.07 217 7.95% Counts Apr 30th Southeast D I College Womens Regionals 2022
42 Tennessee** Loss 0-13 0 41 0% Ignored (Why) Apr 30th Southeast D I College Womens Regionals 2022
**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.