(32) #83 Northwestern (10-9)

1335.48 (140)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
17 Brigham Young Loss 2-15 -2.47 39 3.95% Counts (Why) Jan 27th Santa Barbara Invite 2024
43 California-San Diego Loss 9-15 -11.87 47 3.95% Counts Jan 27th Santa Barbara Invite 2024
22 Washington Loss 5-15 -4.79 44 3.95% Counts (Why) Jan 27th Santa Barbara Invite 2024
23 UCLA Loss 5-15 -5.22 49 3.95% Counts (Why) Jan 27th Santa Barbara Invite 2024
54 California-Santa Barbara Loss 11-14 -7.37 55 3.95% Counts Jan 28th Santa Barbara Invite 2024
115 Southern California Win 13-8 14.2 59 3.95% Counts Jan 28th Santa Barbara Invite 2024
176 Saint Louis Win 10-9 -15.82 51 5.27% Counts Mar 2nd Midwest Throwdown 2024
259 Wisconsin-B Win 10-7 -20.22 34 4.99% Counts Mar 2nd Midwest Throwdown 2024
124 Macalester Win 11-8 9.83 112 5.27% Counts Mar 2nd Midwest Throwdown 2024
121 Iowa State Win 10-7 10.98 41 4.99% Counts Mar 3rd Midwest Throwdown 2024
48 Missouri Loss 8-9 2.85 13 4.99% Counts Mar 3rd Midwest Throwdown 2024
95 Wisconsin-Eau Claire Loss 6-9 -24.76 30 4.68% Counts Mar 3rd Midwest Throwdown 2024
128 Colorado College Win 13-6 28.49 232 6.64% Counts (Why) Mar 30th Huck Finn 2024
118 Michigan Tech Win 12-9 13.05 3 6.64% Counts Mar 30th Huck Finn 2024
108 Wisconsin-Milwaukee Win 12-7 27.37 78 6.64% Counts (Why) Mar 30th Huck Finn 2024
117 Vanderbilt Win 9-8 -2.05 62 6.28% Counts Mar 30th Huck Finn 2024
50 Alabama Win 12-10 28.75 3 6.64% Counts Mar 31st Huck Finn 2024
49 St Olaf Loss 10-12 -5.01 64 6.64% Counts Mar 31st Huck Finn 2024
65 Stanford Loss 7-13 -34.72 80 6.64% Counts Mar 31st Huck Finn 2024
**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.