(3) #102 LSU (16-8)

1119.43 (23)

Click on column to sort  • 
# Opponent Result Effect % of Ranking Status Date Event
144 Tennessee Loss 6-8 -18.5 3.41% Jan 26th Clutch Classic 2019
139 Tennessee-Chattanooga Win 9-4 13.99 3.29% Jan 26th Clutch Classic 2019
261 Emory-B** Win 8-3 0 0% Ignored Jan 26th Clutch Classic 2019
18 South Carolina** Loss 0-13 0 0% Ignored Jan 26th Clutch Classic 2019
139 Tennessee-Chattanooga Loss 7-9 -17.7 3.65% Jan 27th Clutch Classic 2019
189 Tulane Loss 5-7 -27.83 3.16% Jan 27th Clutch Classic 2019
113 Oklahoma Win 9-7 9.63 4.34% Feb 16th Big D in lil d Women
195 Texas A&M Win 11-1 2.22 4.34% Feb 16th Big D in lil d Women
120 Arizona State Win 8-7 1.43 4.2% Feb 16th Big D in lil d Women
113 Oklahoma Win 8-5 15.73 3.91% Feb 17th Big D in lil d Women
110 Dallas Win 6-5 2.51 3.59% Feb 17th Big D in lil d Women
72 Texas-Dallas Loss 8-12 -11.61 4.72% Feb 17th Big D in lil d Women
201 Indiana Win 14-4 1.08 5.3% Mar 2nd Mardi Gras XXXII
214 Mississippi Win 11-7 -11.26 5.16% Mar 2nd Mardi Gras XXXII
112 Central Florida Win 12-11 3.35 5.3% Mar 2nd Mardi Gras XXXII
143 Alabama Win 13-6 21.17 5.3% Mar 3rd Mardi Gras XXXII
195 Texas A&M Win 6-5 -21.47 4.8% Mar 23rd Womens College Centex 2019
217 Minnesota-B** Win 12-4 0 0% Ignored Mar 23rd Womens College Centex 2019
99 MIT Loss 6-13 -39.84 6.31% Mar 23rd Womens College Centex 2019
168 Rice Win 10-4 14.41 5.51% Mar 23rd Womens College Centex 2019
90 Colorado State Loss 12-13 -1.84 6.31% Mar 24th Womens College Centex 2019
101 Trinity Win 15-4 40.81 6.31% Mar 24th Womens College Centex 2019
80 St Olaf Loss 9-10 1.79 6.31% Mar 24th Womens College Centex 2019
108 Southern California Win 7-4 22.64 4.8% Mar 24th Womens College Centex 2019
**Blowout Eligible

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.