(13) #85 Boston College (11-8)

1516.2 (244)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
189 Baylor Win 11-5 10.51 350 5.2% Counts (Why) Mar 15th Mens Centex 2025
183 Tarleton State Loss 10-11 -29.62 453 5.67% Counts Mar 15th Mens Centex 2025
220 Texas-B Win 12-2 4.86 369 5.44% Counts (Why) Mar 15th Mens Centex 2025
293 Trinity** Win 13-3 0 149 0% Ignored (Why) Mar 15th Mens Centex 2025
225 Arkansas Win 15-10 -5.19 214 5.67% Counts Mar 16th Mens Centex 2025
90 Texas A&M Loss 12-14 -14.47 256 5.67% Counts Mar 16th Mens Centex 2025
284 Harding** Win 15-5 0 225 0% Ignored (Why) Mar 16th Mens Centex 2025
51 Cornell Loss 10-11 5.62 200 6.36% Counts Mar 29th East Coast Invite 2025
74 Temple Loss 10-12 -10.49 289 6.36% Counts Mar 29th East Coast Invite 2025
154 Johns Hopkins Win 9-7 1.21 250 5.84% Counts Mar 29th East Coast Invite 2025
97 SUNY-Buffalo Loss 10-11 -11.32 154 6.36% Counts Mar 29th East Coast Invite 2025
64 Georgetown Win 15-8 47.19 337 6.36% Counts (Why) Mar 30th East Coast Invite 2025
113 West Chester Loss 10-11 -16.89 200 6.36% Counts Mar 30th East Coast Invite 2025
92 Yale Win 12-4 37.34 228 6.11% Counts (Why) Mar 30th East Coast Invite 2025
230 Harvard Win 14-7 1.85 247 7.14% Counts (Why) Apr 12th Metro Boston D I Mens Conferences 2025
19 Tufts Loss 6-13 -6.59 134 7.14% Counts (Why) Apr 12th Metro Boston D I Mens Conferences 2025
318 Massachusetts-Lowell** Win 14-3 0 227 0% Ignored (Why) Apr 12th Metro Boston D I Mens Conferences 2025
214 MIT Win 15-1 8.04 281 7.14% Counts (Why) Apr 13th Metro Boston D I Mens Conferences 2025
105 Boston University Loss 12-14 -22.12 282 7.14% Counts Apr 13th Metro Boston D I Mens Conferences 2025
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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.