(2) #19 Tufts (15-7)

2030.53 (134)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
49 Chicago Loss 12-13 -16.02 94 3.79% Counts Feb 15th Queen City Tune Up 2025
53 William & Mary Win 13-12 -7.69 159 3.79% Counts Feb 15th Queen City Tune Up 2025
55 Maryland Win 13-8 6.33 129 3.79% Counts Feb 15th Queen City Tune Up 2025
49 Chicago Win 11-0 11.45 94 3.48% Counts (Why) Feb 16th Queen City Tune Up 2025
62 North Carolina State Win 10-4 7.56 87 3.31% Counts (Why) Feb 16th Queen City Tune Up 2025
70 Dartmouth Win 13-5 9.42 166 4.77% Counts (Why) Mar 15th Mens Centex 2025
36 Middlebury Win 9-7 3.1 221 4.38% Counts Mar 15th Mens Centex 2025
14 Texas Loss 5-10 -23.88 94 4.24% Counts Mar 15th Mens Centex 2025
44 Wisconsin Win 11-8 5.63 149 4.77% Counts Mar 15th Mens Centex 2025
49 Chicago Win 13-8 10.74 94 4.77% Counts Mar 16th Mens Centex 2025
58 Illinois Win 14-11 -2.22 158 4.77% Counts Mar 16th Mens Centex 2025
14 Texas Win 13-10 18.18 94 4.77% Counts Mar 16th Mens Centex 2025
26 Michigan Loss 9-13 -29.54 274 5.36% Counts Mar 29th Easterns 2025
27 Minnesota Win 10-8 6.82 178 5.21% Counts Mar 29th Easterns 2025
4 North Carolina Loss 9-13 -5.05 154 5.36% Counts Mar 29th Easterns 2025
20 Vermont Win 10-9 6.96 170 5.36% Counts Mar 29th Easterns 2025
13 California Win 13-9 27.83 134 5.36% Counts Mar 30th Easterns 2025
3 Carleton College Loss 9-13 -4.78 161 5.36% Counts Mar 30th Easterns 2025
1 Massachusetts Loss 8-15 -10.11 158 5.36% Counts Mar 30th Easterns 2025
85 Boston College Win 13-6 5.48 244 6.01% Counts (Why) Apr 12th Metro Boston D I Mens Conferences 2025
318 Massachusetts-Lowell** Win 15-4 0 227 0% Ignored (Why) Apr 12th Metro Boston D I Mens Conferences 2025
16 Northeastern Loss 9-12 -20.42 161 6.01% Counts Apr 13th Metro Boston D I Mens Conferences 2025
**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.