(5) #112 Illinois (7-14)

1315.98 (10)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
19 Georgia** Loss 5-13 0 81 0% Ignored (Why) Feb 3rd Florida Warm Up 2023
23 Wisconsin Loss 6-13 -0.93 11 4.15% Counts (Why) Feb 3rd Florida Warm Up 2023
2 Brigham Young** Loss 2-13 0 3 0% Ignored (Why) Feb 4th Florida Warm Up 2023
77 Temple Loss 8-9 1.61 62 3.93% Counts Feb 4th Florida Warm Up 2023
147 Connecticut Win 13-6 19.34 8 4.15% Counts (Why) Feb 4th Florida Warm Up 2023
104 Florida State Loss 7-8 -3.68 17 3.69% Counts Feb 4th Florida Warm Up 2023
79 Texas A&M Loss 12-13 1.42 7 4.15% Counts Feb 5th Florida Warm Up 2023
201 South Florida Win 9-2 7.88 24 3.43% Counts (Why) Feb 5th Florida Warm Up 2023
47 Colorado State Loss 6-13 -16.76 10 5.87% Counts (Why) Mar 18th Centex 2023
86 Dartmouth Loss 8-12 -19.97 64 5.87% Counts Mar 18th Centex 2023
91 Tulane Loss 8-9 -0.63 16 5.55% Counts Mar 18th Centex 2023
60 Middlebury Loss 8-15 -18.88 27 5.87% Counts Mar 19th Centex 2023
86 Dartmouth Win 12-11 15.34 64 5.87% Counts Mar 19th Centex 2023
91 Tulane Win 15-10 35.41 16 5.87% Counts Mar 19th Centex 2023
115 Michigan State Win 9-8 7.95 180 6.23% Counts Apr 1st Huck Finn1
92 Missouri S&T Win 8-7 14.85 51 5.85% Counts Apr 1st Huck Finn1
98 Kentucky Loss 6-7 -1.39 42 5.45% Counts Apr 1st Huck Finn1
64 St. Olaf Loss 6-11 -19.59 28 6.23% Counts Apr 1st Huck Finn1
163 Boston University Win 10-3 23.52 84 5.76% Counts (Why) Apr 2nd Huck Finn1
90 Chicago Loss 8-10 -9.93 6 6.41% Counts Apr 2nd Huck Finn1
131 Georgia State Loss 5-9 -36.12 14 5.66% Counts Apr 2nd Huck Finn1
**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.