(8) #88 Michigan Tech (12-7)

1135.42 (201)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
97 Arkansas Win 9-8 4.86 145 5.55% Counts Mar 1st Midwest Throwdown 2025
67 Illinois Win 7-5 24.55 129 4.66% Counts Mar 1st Midwest Throwdown 2025
146 Knox Win 12-2 11.39 209 5.63% Counts (Why) Mar 1st Midwest Throwdown 2025
210 Vanderbilt** Win 13-1 0 374 0% Ignored (Why) Mar 1st Midwest Throwdown 2025
92 Iowa State Loss 8-11 -24.23 217 5.86% Counts Mar 2nd Midwest Throwdown 2025
28 Missouri Loss 5-11 -0.08 94 5.38% Counts (Why) Mar 2nd Midwest Throwdown 2025
118 Northwestern Loss 4-10 -42.97 203 5.12% Counts (Why) Mar 2nd Midwest Throwdown 2025
135 Grand Valley Win 9-0 16.11 250 5.44% Counts (Why) Mar 15th Davenport Spring Skirmish
110 Michigan State Win 7-6 -0.26 402 5.44% Counts Mar 15th Davenport Spring Skirmish
199 Oberlin** Win 6-2 0 396 0% Ignored (Why) Mar 15th Davenport Spring Skirmish
58 Davenport Loss 6-8 -1.94 256 5.65% Counts Mar 16th Davenport Spring Skirmish
58 Davenport Loss 4-7 -12.03 256 5.01% Counts Mar 16th Davenport Spring Skirmish
110 Michigan State Win 6-5 -0.24 402 5.01% Counts Mar 16th Davenport Spring Skirmish
46 Carleton College-Eclipse Loss 3-15 -17.22 407 8.29% Counts (Why) Apr 12th North Central D III Womens Conferences 2025
173 Grinnell Win 15-7 5.25 22 8.29% Counts (Why) Apr 12th North Central D III Womens Conferences 2025
194 St Olaf-B** Win 15-0 0 196 0% Ignored (Why) Apr 12th North Central D III Womens Conferences 2025
46 Carleton College-Eclipse Loss 13-15 17.67 407 8.29% Counts Apr 13th North Central D III Womens Conferences 2025
173 Grinnell Win 11-7 -6.59 22 8.07% Counts Apr 13th North Central D III Womens Conferences 2025
129 Winona State Win 14-7 26.51 253 8.29% 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.