(1) #79 Texas A&M (12-12)

1473.68 (7)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
104 Florida State Loss 8-10 -15.83 17 3.89% Counts Feb 3rd Florida Warm Up 2023
8 Pittsburgh** Loss 5-13 0 17 0% Ignored (Why) Feb 3rd Florida Warm Up 2023
72 Auburn Loss 7-11 -17.91 54 3.89% Counts Feb 4th Florida Warm Up 2023
3 Massachusetts** Loss 4-13 0 65 0% Ignored (Why) Feb 4th Florida Warm Up 2023
147 Connecticut Win 11-9 -2.58 8 3.99% Counts Feb 4th Florida Warm Up 2023
201 South Florida Win 12-9 -7.93 24 3.99% Counts Feb 4th Florida Warm Up 2023
112 Illinois Win 13-12 -1.36 10 3.99% Counts Feb 5th Florida Warm Up 2023
39 Florida Loss 9-13 -6.27 16 3.99% Counts Feb 5th Florida Warm Up 2023
88 Central Florida Loss 7-12 -27.92 18 4.75% Counts Feb 25th Mardi Gras XXXV
344 Mississippi** Win 13-1 0 11 0% Ignored (Why) Feb 25th Mardi Gras XXXV
89 Mississippi State Win 11-6 23.85 7 4.49% Counts (Why) Feb 25th Mardi Gras XXXV
218 Tulane-B Win 11-6 -2.79 10 4.49% Counts (Why) Feb 25th Mardi Gras XXXV
43 Alabama-Huntsville Loss 6-8 -3.02 7 4.08% Counts Feb 26th Mardi Gras XXXV
89 Mississippi State Win 8-7 3.76 7 4.22% Counts Feb 26th Mardi Gras XXXV
174 Sam Houston Win 13-3 8.99 23 4.75% Counts (Why) Feb 26th Mardi Gras XXXV
54 Northwestern Loss 8-13 -21.17 16 5.65% Counts Mar 18th Centex 2023
91 Tulane Win 12-11 4.88 16 5.65% Counts Mar 18th Centex 2023
26 Georgia Tech Loss 3-13 -12.29 1 5.65% Counts (Why) Mar 18th Centex 2023
51 Virginia Win 11-8 31.57 1 5.65% Counts Mar 18th Centex 2023
54 Northwestern Win 13-10 28.17 16 5.65% Counts Mar 18th Centex 2023
4 Texas Loss 8-15 10.55 1 5.65% Counts Mar 19th Centex 2023
39 Florida Win 15-14 23.51 16 5.65% Counts Mar 19th Centex 2023
23 Wisconsin Loss 6-15 -10.72 11 5.65% Counts (Why) Mar 19th Centex 2023
26 Georgia Tech Loss 4-7 -4.56 1 4.3% Counts Mar 19th Centex 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.