(1) #35 Michigan (8-13)

1619.57 (145)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
40 Georgia Win 9-6 13.39 149 3.9% Counts Feb 11th Queen City Tune Up1
202 North Carolina-Wilmington** Win 13-4 0 164 0% Ignored (Why) Feb 11th Queen City Tune Up1
4 Tufts Loss 8-15 11.12 142 4.39% Counts Feb 11th Queen City Tune Up1
45 Washington University Win 10-6 14.95 140 4.03% Counts (Why) Feb 11th Queen City Tune Up1
27 Minnesota Loss 8-9 -2.57 138 4.15% Counts Feb 12th Queen City Tune Up1
13 Pittsburgh Loss 7-9 1.45 145 4.03% Counts Feb 12th Queen City Tune Up1
40 Georgia Loss 9-12 -22.52 149 4.93% Counts Feb 25th Commonwealth Cup Weekend2 2023
36 Brown Loss 10-11 -8.53 143 4.93% Counts Feb 25th Commonwealth Cup Weekend2 2023
19 Yale Loss 8-12 -14.24 219 4.93% Counts Feb 25th Commonwealth Cup Weekend2 2023
13 Pittsburgh Loss 7-11 -7.72 145 4.8% Counts Feb 25th Commonwealth Cup Weekend2 2023
69 Case Western Reserve Win 13-3 11.97 134 4.93% Counts (Why) Feb 26th Commonwealth Cup Weekend2 2023
71 Massachusetts Win 13-6 11.09 145 4.93% Counts (Why) Feb 26th Commonwealth Cup Weekend2 2023
26 Notre Dame Loss 7-8 -2.58 132 4.38% Counts Feb 26th Commonwealth Cup Weekend2 2023
59 Penn State Loss 10-11 -22.99 119 4.93% Counts Feb 26th Commonwealth Cup Weekend2 2023
36 Brown Win 11-6 29.78 143 5.55% Counts (Why) Mar 18th Womens Centex1
48 Texas Win 13-10 10.5 139 5.86% Counts Mar 18th Womens Centex1
14 Virginia Loss 9-12 -2.13 139 5.86% Counts Mar 18th Womens Centex1
18 Colorado State Loss 10-15 -16.3 143 5.86% Counts Mar 19th Womens Centex1
33 Ohio State Loss 10-12 -13.94 145 5.86% Counts Mar 19th Womens Centex1
42 Wisconsin Loss 11-13 -21.3 142 5.86% Counts Mar 19th Womens Centex1
42 Wisconsin Win 13-3 30.33 142 5.86% Counts (Why) Mar 19th Womens Centex1
**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.