(1) #70 Northwestern (9-11)

1238.77 (145)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
50 California-Santa Cruz Loss 6-11 -19.84 142 5.4% Counts Jan 28th Santa Barbara Invitational 2023
7 Carleton College** Loss 4-13 0 141 0% Ignored (Why) Jan 28th Santa Barbara Invitational 2023
31 California Loss 5-7 4.32 142 4.53% Counts Jan 28th Santa Barbara Invitational 2023
50 California-Santa Cruz Loss 10-11 4.47 142 5.7% Counts Jan 29th Santa Barbara Invitational 2023
53 Cal Poly-SLO Loss 6-7 0.8 142 4.72% Counts Jan 29th Santa Barbara Invitational 2023
78 Lewis & Clark Loss 5-7 -18.45 142 4.53% Counts Jan 29th Santa Barbara Invitational 2023
42 Wisconsin Win 7-6 19.45 142 4.72% Counts Jan 29th Santa Barbara Invitational 2023
213 St. Olaf-B** Win 11-0 0 177 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
171 Illinois** Win 11-1 0 213 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
209 Purdue-B** Win 11-0 0 183 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
189 Wisconsin-Milwaukee** Win 11-0 0 184 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
124 Saint Louis Win 11-3 13.57 150 6.99% Counts (Why) Mar 5th Midwest Throwdown 2023
122 Purdue Win 11-4 14.24 146 6.99% Counts (Why) Mar 5th Midwest Throwdown 2023
45 Washington University Loss 6-7 7.76 140 6.3% Counts Mar 5th Midwest Throwdown 2023
82 Central Florida Loss 7-9 -33.02 148 7.84% Counts Mar 18th Womens Centex1
54 Georgia Tech Loss 9-10 -1.02 140 8.55% Counts Mar 18th Womens Centex1
45 Washington University Loss 10-13 -8.19 140 8.55% Counts Mar 18th Womens Centex1
104 Iowa Win 13-11 -4.03 160 8.55% Counts Mar 19th Womens Centex1
23 Texas-Dallas Loss 7-10 9.77 139 8.09% Counts Mar 19th Womens Centex1
121 Texas A&M Win 15-9 10.01 154 8.55% Counts Mar 19th Womens Centex1
**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.