(26) #154 Harvard (3-17)

1023.19 (116)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
70 Case Western Reserve Win 14-10 35.36 4 4.55% Counts Feb 10th Queen City Tune Up 2024
16 Penn State** Loss 2-15 0 59 0% Ignored (Why) Feb 10th Queen City Tune Up 2024
61 William & Mary Win 13-10 35.11 50 4.55% Counts Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Loss 6-15 -0.23 20 4.55% Counts (Why) Feb 10th Queen City Tune Up 2024
34 Ohio State Loss 8-11 12.06 140 4.55% Counts Feb 11th Queen City Tune Up 2024
92 Tennessee Loss 7-8 5.01 16 4.04% Counts Feb 11th Queen City Tune Up 2024
27 Georgia Tech** Loss 5-13 0 17 0% Ignored (Why) Feb 24th Easterns Qualifier 2024
36 North Carolina-Charlotte Loss 2-13 -0.27 20 5.1% Counts (Why) Feb 24th Easterns Qualifier 2024
126 Lehigh Loss 9-10 -0.15 13 5.1% Counts Feb 24th Easterns Qualifier 2024
66 Virginia Loss 5-13 -12.32 111 5.1% Counts (Why) Feb 24th Easterns Qualifier 2024
106 Notre Dame Loss 9-12 -8.51 25 5.1% Counts Feb 25th Easterns Qualifier 2024
68 James Madison Loss 7-12 -8.97 20 5.1% Counts Feb 25th Easterns Qualifier 2024
158 Kennesaw State Loss 11-12 -7.39 9 5.1% Counts Feb 25th Easterns Qualifier 2024
167 Columbia Win 13-5 39.12 38 6.81% Counts (Why) Mar 30th East Coast Invite 2024
101 Cornell Loss 10-11 5.58 51 6.81% Counts Mar 30th East Coast Invite 2024
123 Pennsylvania Loss 8-10 -9.83 59 6.63% Counts Mar 30th East Coast Invite 2024
146 Yale Loss 8-13 -33.58 8 6.81% Counts Mar 30th East Coast Invite 2024
98 Dartmouth Loss 7-10 -11.52 16 6.44% Counts Mar 31st East Coast Invite 2024
169 Rutgers Loss 9-11 -23.45 29 6.81% Counts Mar 31st East Coast Invite 2024
150 Navy Loss 9-11 -17.41 45 6.81% Counts Mar 31st East Coast Invite 2024
**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.