(26) #176 Navy (8-12)

1212.43 (178)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
153 Missouri S&T Win 13-9 22.75 401 4.26% Counts Mar 2nd FCS D III Tune Up 2024
172 Union (Tennessee) Loss 9-13 -17.68 143 4.26% Counts Mar 2nd FCS D III Tune Up 2024
81 Lewis & Clark Loss 9-13 -1.94 248 4.26% Counts Mar 2nd FCS D III Tune Up 2024
52 Whitman Loss 4-13 -2.48 320 4.26% Counts (Why) Mar 2nd FCS D III Tune Up 2024
274 Air Force Win 13-3 10.59 279 4.26% Counts (Why) Mar 3rd FCS D III Tune Up 2024
232 Butler Win 13-6 17.7 342 4.26% Counts (Why) Mar 3rd FCS D III Tune Up 2024
68 Franciscan Loss 5-13 -6.76 164 4.26% Counts (Why) Mar 3rd FCS D III Tune Up 2024
102 Connecticut Loss 11-12 8.94 246 5.37% Counts Mar 30th East Coast Invite 2024
26 McGill** Loss 4-13 0 223 0% Ignored (Why) Mar 30th East Coast Invite 2024
120 Syracuse Win 11-10 17.91 214 5.37% Counts Mar 30th East Coast Invite 2024
300 SUNY-Stony Brook Win 11-7 -1.69 218 5.22% Counts Mar 30th East Coast Invite 2024
85 Cornell Loss 9-13 -3.17 350 5.37% Counts Mar 31st East Coast Invite 2024
152 Harvard Win 11-9 19.74 288 5.37% Counts Mar 31st East Coast Invite 2024
120 Syracuse Loss 9-12 -8.76 214 5.37% Counts Mar 31st East Coast Invite 2024
209 Christopher Newport Loss 10-11 -17.45 369 6.38% Counts Apr 20th Atlantic Coast D III Mens Conferences 2024
84 Elon Loss 7-15 -16.09 334 6.38% Counts (Why) Apr 20th Atlantic Coast D III Mens Conferences 2024
179 North Carolina-Asheville Loss 6-12 -39.23 263 6.21% Counts Apr 20th Atlantic Coast D III Mens Conferences 2024
65 Richmond Loss 7-15 -9.37 311 6.38% Counts (Why) Apr 21st Atlantic Coast D III Mens Conferences 2024
256 Salisbury Win 15-8 18.56 56 6.38% Counts (Why) Apr 21st Atlantic Coast D III Mens Conferences 2024
179 North Carolina-Asheville Win 10-9 7.64 263 6.38% Counts Apr 21st Atlantic Coast D III Mens Conferences 2024
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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.