() #45 Washington University (14-6)

1479.26 (140)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
40 Georgia Win 11-9 19.98 149 6.23% Counts Feb 11th Queen City Tune Up1
202 North Carolina-Wilmington** Win 10-4 0 164 0% Ignored (Why) Feb 11th Queen City Tune Up1
35 Michigan Loss 6-10 -21.59 145 5.72% Counts Feb 11th Queen City Tune Up1
4 Tufts** Loss 4-15 0 142 0% Ignored (Why) Feb 11th Queen City Tune Up1
21 North Carolina State Win 9-8 25.19 132 5.9% Counts Feb 12th Queen City Tune Up1
26 Notre Dame Loss 5-12 -24.87 132 5.98% Counts (Why) Feb 12th Queen City Tune Up1
124 Saint Louis** Win 11-1 0 150 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
106 Marquette Win 11-6 1.45 179 7.01% Counts (Why) Mar 4th Midwest Throwdown 2023
148 Washington University-B** Win 11-1 0 165 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
188 Wisconsin-B** Win 11-2 0 147 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
73 St. Olaf Win 13-1 27.83 95 7.41% Counts (Why) Mar 5th Midwest Throwdown 2023
70 Northwestern Win 7-6 -7.54 145 6.13% Counts Mar 5th Midwest Throwdown 2023
165 Truman State** Win 13-1 0 150 0% Ignored (Why) Mar 5th Midwest Throwdown 2023
54 Georgia Tech Loss 10-12 -33.08 140 8.32% Counts Mar 18th Womens Centex1
70 Northwestern Win 13-10 7.95 145 8.32% Counts Mar 18th Womens Centex1
23 Texas-Dallas Loss 6-12 -28.11 139 8.1% Counts Mar 18th Womens Centex1
82 Central Florida Win 11-5 20.73 148 7.63% Counts (Why) Mar 19th Womens Centex1
16 Middlebury Loss 4-15 -22.09 128 8.32% Counts (Why) Mar 19th Womens Centex1
44 Pennsylvania Win 11-10 11.8 147 8.32% Counts Mar 19th Womens Centex1
48 Texas Win 7-5 21.86 139 6.61% Counts 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.