(7) #168 Kennesaw State (7-12)

890.93 (31)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
239 Radford Loss 6-12 -42.36 34 4.85% Counts Feb 26th Cutlass Classic 2022
79 Charleston Loss 9-11 5.9 28 4.98% Counts Feb 26th Cutlass Classic 2022
198 George Washington Win 11-5 22.61 37 4.57% Counts (Why) Feb 26th Cutlass Classic 2022
304 Embry-Riddle Win 13-3 0.37 30 4.98% Counts (Why) Feb 26th Cutlass Classic 2022
239 Radford Loss 9-15 -40.24 34 4.98% Counts Feb 27th Cutlass Classic 2022
233 Navy-B Win 15-5 19.19 32 4.98% Counts (Why) Feb 27th Cutlass Classic 2022
35 Middlebury** Loss 5-13 0 60 0% Ignored (Why) Mar 26th Rodeo
157 Liberty Win 9-7 19.42 41 5.76% Counts Mar 26th Rodeo
243 Elon Win 12-7 17.05 29 6.28% Counts (Why) Mar 26th Rodeo
149 SUNY-Binghamton Win 8-7 11.3 38 5.58% Counts Mar 26th Rodeo
94 Princeton Loss 10-11 11.6 37 6.28% Counts Mar 27th Rodeo
157 Liberty Loss 7-10 -22.18 41 5.94% Counts Mar 27th Rodeo
111 Georgia State Loss 11-12 7.19 25 7.05% Counts Apr 9th Southern Appalachian D I College Mens CC 2022
102 Emory Loss 3-10 -22.36 25 6.16% Counts (Why) Apr 9th Southern Appalachian D I College Mens CC 2022
134 Tennessee Loss 7-9 -10.54 28 6.47% Counts Apr 9th Southern Appalachian D I College Mens CC 2022
8 Georgia** Loss 4-15 0 30 0% Ignored (Why) Apr 9th Southern Appalachian D I College Mens CC 2022
134 Tennessee Win 13-11 26.97 28 7.05% Counts Apr 10th Southern Appalachian D I College Mens CC 2022
111 Georgia State Loss 12-13 7.19 25 7.05% Counts Apr 10th Southern Appalachian D I College Mens CC 2022
121 Tennessee-Chattanooga Loss 10-13 -10.53 29 7.05% Counts Apr 10th Southern Appalachian D I College Mens CC 2022
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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.