(7) #143 Michigan Tech (7-13)

1062.91 (73)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
70 Franciscan Loss 8-12 -4.5 180 4.59% Counts Mar 1st D III River City Showdown 2025
145 Kenyon Loss 7-9 -12.46 4 4.22% Counts Mar 1st D III River City Showdown 2025
89 North Carolina-Asheville Loss 10-13 -4.28 44 4.59% Counts Mar 1st D III River City Showdown 2025
213 Air Force Win 13-9 4.94 61 4.59% Counts Mar 2nd D III River City Showdown 2025
166 Brandeis Win 11-7 17.61 68 4.47% Counts Mar 2nd D III River City Showdown 2025
149 Davidson Loss 10-11 -6.41 54 4.59% Counts Mar 2nd D III River City Showdown 2025
11 Davenport Loss 6-13 17.29 377 5.16% Counts (Why) Mar 15th Grand Rapids Invite 2025
212 Eastern Michigan Win 10-6 9.12 246 4.73% Counts (Why) Mar 15th Grand Rapids Invite 2025
190 Toronto Win 10-7 9.56 142 4.88% Counts Mar 15th Grand Rapids Invite 2025
63 Notre Dame Loss 6-14 -11.06 277 5.16% Counts (Why) Mar 15th Grand Rapids Invite 2025
249 Cedarville Win 15-6 8.15 13 5.16% Counts (Why) Mar 16th Grand Rapids Invite 2025
123 Wisconsin-Milwaukee Loss 11-14 -12.47 70 5.16% Counts Mar 16th Grand Rapids Invite 2025
131 Pittsburgh-B Loss 9-10 -3.62 64 5.16% Counts Mar 16th Grand Rapids Invite 2025
59 Asbury Loss 8-13 -3.98 43 5.79% Counts Mar 29th Corny Classic College 2025
303 Ball State Win 12-6 -7.09 149 5.63% Counts (Why) Mar 29th Corny Classic College 2025
133 Lipscomb Loss 9-10 -4.49 49 5.79% Counts Mar 29th Corny Classic College 2025
164 Ohio Loss 8-9 -12.23 115 5.48% Counts Mar 29th Corny Classic College 2025
59 Asbury Loss 6-8 6.84 43 4.97% Counts Mar 30th Corny Classic College 2025
164 Ohio Win 10-7 17.59 115 5.48% Counts Mar 30th Corny Classic College 2025
158 Vanderbilt Loss 5-6 -8.53 203 4.4% Counts Mar 30th Corny Classic College 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.