(9) #70 Notre Dame (12-8)

1355.69 (67)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
108 Tennessee Win 15-8 17.61 68 4.73% Counts (Why) Feb 11th Queen City Tune Up1
39 William & Mary Loss 9-15 -16.66 56 4.73% Counts Feb 11th Queen City Tune Up1
50 Virginia Loss 11-12 -1.84 66 4.73% Counts Feb 11th Queen City Tune Up1
19 Ohio State Loss 7-15 -10.41 69 4.73% Counts (Why) Feb 11th Queen City Tune Up1
53 Appalachian State Loss 10-11 -2.36 76 4.73% Counts Feb 12th Queen City Tune Up1
73 Purdue Win 12-6 26.93 88 4.6% Counts (Why) Feb 12th Queen City Tune Up1
67 Maryland Loss 12-13 -6.2 48 5.31% Counts Feb 25th Easterns Qualifier 2023
106 Florida State Win 13-10 7.45 75 5.31% Counts Feb 25th Easterns Qualifier 2023
40 Duke Loss 9-13 -13.66 72 5.31% Counts Feb 25th Easterns Qualifier 2023
25 North Carolina-Wilmington Win 12-11 25.97 69 5.31% Counts Feb 25th Easterns Qualifier 2023
84 Alabama Win 15-11 16.68 49 5.31% Counts Feb 26th Easterns Qualifier 2023
47 Case Western Reserve Loss 11-15 -15.77 46 5.31% Counts Feb 26th Easterns Qualifier 2023
55 Georgetown Loss 7-11 -22.41 67 5.16% Counts Feb 26th Easterns Qualifier 2023
141 LSU Win 10-9 -14.19 27 5.96% Counts Mar 11th Tally Classic XVII
106 Florida State Win 10-6 17.4 75 5.47% Counts (Why) Mar 11th Tally Classic XVII
253 Georgia Southern** Win 13-5 0 79 0% Ignored (Why) Mar 11th Tally Classic XVII
204 South Florida Win 11-5 -1.87 78 5.47% Counts (Why) Mar 11th Tally Classic XVII
95 Chicago Win 13-12 -1.01 89 5.96% Counts Mar 12th Tally Classic XVII
153 Minnesota-Duluth Win 15-14 -17.97 73 5.96% Counts Mar 12th Tally Classic XVII
94 Tulane Win 13-10 12.12 77 5.96% Counts Mar 12th Tally Classic XVII
**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.