(21) #158 Bates (7-13)

671.71 (83)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
162 Colby Loss 7-8 -6.2 177 4.33% Counts Mar 9th Too Hot to Handle
86 Wellesley Loss 2-11 -6 323 4.48% Counts (Why) Mar 9th Too Hot to Handle
48 McGill** Loss 3-8 0 228 0% Ignored (Why) Mar 9th Too Hot to Handle
174 New Hampshire Win 7-6 1.48 177 4.03% Counts Mar 9th Too Hot to Handle
148 Boston University Win 6-5 8.16 256 4.41% Counts Mar 29th New England Open 2025
94 Rhode Island Loss 4-8 -6.5 163 4.61% Counts Mar 29th New England Open 2025
233 Northeastern-B Win 11-3 3.36 118 5.32% Counts (Why) Mar 29th New England Open 2025
131 Harvard Loss 5-10 -22.4 243 5.15% Counts Mar 30th New England Open 2025
174 New Hampshire Loss 7-8 -11.68 177 5.15% Counts Mar 30th New England Open 2025
148 Boston University Loss 8-10 -12.62 256 5.65% Counts Mar 30th New England Open 2025
162 Colby Loss 5-8 -26.5 177 5.39% Counts Apr 12th North New England D III Womens Conferences 2025
51 Middlebury** Loss 2-11 0 321 0% Ignored (Why) Apr 12th North New England D III Womens Conferences 2025
149 Dartmouth Win 8-6 19.96 412 5.59% Counts Apr 12th North New England D III Womens Conferences 2025
137 Bowdoin Win 10-7 34.27 74 6.16% Counts Apr 12th North New England D III Womens Conferences 2025
90 Williams Loss 4-9 -9.85 220 6.04% Counts (Why) Apr 26th New England D III College Womens Regionals 2025
86 Wellesley Loss 4-9 -8.23 323 6.04% Counts (Why) Apr 26th New England D III College Womens Regionals 2025
201 Stonehill Win 11-7 15.48 54 7.11% Counts Apr 26th New England D III College Womens Regionals 2025
166 Smith Win 10-6 32.54 6.71% Counts (Why) Apr 26th New England D III College Womens Regionals 2025
77 Mount Holyoke Loss 6-10 3.93 269 6.71% Counts Apr 27th New England D III College Womens Regionals 2025
86 Wellesley Loss 6-12 -8.22 323 7.11% Counts Apr 27th New England D III College Womens Regionals 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.