(9) #62 Northwestern (10-10)

1387.69 (66)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
7 Oregon Loss 3-15 -0.91 67 4.43% Counts (Why) Jan 28th Santa Barbara Invitational 2023
26 California Win 12-10 25.17 67 4.43% Counts Jan 28th Santa Barbara Invitational 2023
66 Stanford Win 13-9 18.68 66 4.43% Counts Jan 28th Santa Barbara Invitational 2023
45 Western Washington Loss 10-15 -16.3 68 4.43% Counts Jan 28th Santa Barbara Invitational 2023
16 British Columbia Loss 5-11 -8.92 66 4.06% Counts (Why) Jan 29th Santa Barbara Invitational 2023
47 Case Western Reserve Loss 8-12 -17.29 46 4.43% Counts Jan 29th Santa Barbara Invitational 2023
30 Utah State Loss 12-13 6.39 66 4.43% Counts Jan 29th Santa Barbara Invitational 2023
102 Missouri S&T Win 11-9 2.35 68 5.91% Counts Mar 4th Midwest Throwdown 2023
198 Illinois State** Win 13-1 0 69 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
291 Washington University-B** Win 13-2 0 39 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
121 Saint Louis Win 11-8 4.07 105 5.91% Counts Mar 5th Midwest Throwdown 2023
35 Washington University Loss 4-11 -21.69 68 5.42% Counts (Why) Mar 5th Midwest Throwdown 2023
88 St. Olaf Loss 8-12 -36.06 37 5.91% Counts Mar 5th Midwest Throwdown 2023
69 Middlebury Win 13-9 27.77 14 6.63% Counts Mar 18th Centex 2023
80 Texas A&M Win 13-8 27.92 85 6.63% Counts Mar 18th Centex 2023
80 Texas A&M Loss 10-13 -30.62 85 6.63% Counts Mar 18th Centex 2023
6 Colorado Loss 6-13 0.33 70 6.63% Counts (Why) Mar 18th Centex 2023
44 Colorado College Win 15-13 22.46 61 6.63% Counts Mar 19th Centex 2023
52 Colorado State Win 15-13 18.68 69 6.63% Counts Mar 19th Centex 2023
37 Florida Loss 7-11 -21.6 79 6.45% 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.