(8) #68 Cincinnati (16-9)

1515.37 (33)

Click on column to sort  • 
# Opponent Result Effect % of Ranking Status Date Event
136 South Florida Win 13-10 1.87 3.63% Feb 8th Florida Warm Up 2019
55 Florida State Loss 8-13 -15.04 3.63% Feb 8th Florida Warm Up 2019
150 Cornell Loss 10-13 -25.04 3.63% Feb 8th Florida Warm Up 2019
98 Kansas Loss 7-8 -9.23 3.22% Feb 9th Florida Warm Up 2019
6 Brigham Young** Loss 5-13 0 0% Ignored Feb 9th Florida Warm Up 2019
72 Alabama-Huntsville Win 11-9 8.2 3.63% Feb 9th Florida Warm Up 2019
80 Oklahoma Loss 7-13 -23.36 3.63% Feb 9th Florida Warm Up 2019
65 Florida Loss 12-13 -3.94 3.63% Feb 10th Florida Warm Up 2019
127 Boston College Win 10-3 11.75 3.17% Feb 10th Florida Warm Up 2019
226 Miami (Ohio) Win 13-4 0.05 4.57% Mar 9th Boogienights 2019
204 SUNY-Buffalo Win 13-8 -2.27 4.57% Mar 9th Boogienights 2019
368 Cleveland State** Win 13-2 0 0% Ignored Mar 9th Boogienights 2019
148 Michigan-B Win 15-9 8.71 4.57% Mar 10th Boogienights 2019
204 SUNY-Buffalo Win 15-9 -1.34 4.57% Mar 10th Boogienights 2019
159 Mississippi State Win 13-3 10.7 4.84% Mar 16th Tally Classic XIV
36 Alabama Win 13-10 27.26 4.84% Mar 16th Tally Classic XIV
61 Tennessee Loss 6-13 -28.54 4.84% Mar 16th Tally Classic XIV
88 Tennessee-Chattanooga Win 15-14 1.47 4.84% Mar 16th Tally Classic XIV
165 Georgia Southern Win 15-8 7.19 4.84% Mar 17th Tally Classic XIV
52 Notre Dame Loss 12-15 -9.62 4.84% Mar 17th Tally Classic XIV
106 Illinois State Win 10-8 4.17 5.29% Mar 30th Huck Finn XXIII
46 Iowa State Win 11-7 34.1 5.29% Mar 31st Huck Finn XXIII
37 Illinois Loss 6-10 -15.28 4.99% Mar 31st Huck Finn XXIII
152 Arkansas Win 9-5 8.16 4.66% Mar 31st Huck Finn XXIII
86 Marquette Win 7-5 10.78 4.32% Mar 31st Huck Finn XXIII
**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.