(18) #102 Macalester (13-7)

1055.12 (378)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
97 Arkansas Win 12-5 34.56 145 5.14% Counts (Why) Mar 1st Midwest Throwdown 2025
67 Illinois Win 8-6 26.73 129 4.6% Counts Mar 1st Midwest Throwdown 2025
253 Northwestern-B** Win 12-0 0 302 0% Ignored (Why) Mar 1st Midwest Throwdown 2025
99 Wisconsin-Eau Claire Win 10-4 30.96 258 4.68% Counts (Why) Mar 1st Midwest Throwdown 2025
118 Northwestern Win 13-6 27.41 203 5.35% Counts (Why) Mar 2nd Midwest Throwdown 2025
28 Missouri Loss 7-12 8.96 94 5.35% Counts Mar 2nd Midwest Throwdown 2025
55 St Olaf Loss 6-10 -5.65 182 4.91% Counts Mar 2nd Midwest Throwdown 2025
173 Grinnell Win 13-3 10.01 22 6.75% Counts (Why) Mar 29th Old Capitol Open 2025
142 Saint Louis Win 9-8 -13.06 372 6.38% Counts Mar 29th Old Capitol Open 2025
215 Minnesota-Duluth** Win 11-4 0 97 0% Ignored (Why) Mar 29th Old Capitol Open 2025
74 Purdue Loss 6-8 -5.83 397 5.79% Counts Mar 29th Old Capitol Open 2025
62 Chicago Loss 2-7 -16.02 174 4.89% Counts (Why) Mar 30th Old Capitol Open 2025
142 Saint Louis Win 7-5 0.65 372 5.36% Counts Mar 30th Old Capitol Open 2025
99 Wisconsin-Eau Claire Loss 6-7 -5.55 258 5.58% Counts Mar 30th Old Capitol Open 2025
228 Carleton College-C Win 11-6 -25.26 7.16% Counts (Why) Apr 12th North Central D III Womens Conferences 2025
129 Winona State Win 7-6 -5.64 253 6.26% Counts Apr 12th North Central D III Womens Conferences 2025
259 St Thomas** Win 13-1 0 0% Ignored (Why) Apr 12th North Central D III Womens Conferences 2025
55 St Olaf Loss 3-12 -16.71 182 7.27% Counts (Why) Apr 12th North Central D III Womens Conferences 2025
173 Grinnell Loss 7-11 -73.87 22 7.37% Counts Apr 13th North Central D III Womens Conferences 2025
129 Winona State Win 11-6 26.02 253 7.16% Counts (Why) Apr 13th North Central D III Womens Conferences 2025
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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.