(6) #164 Massachusetts -B (8-11)

1220.7 (261)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
105 Boston University Loss 6-9 -9.39 282 4.72% Counts Mar 1st UMass Invite 2025
108 Columbia Loss 5-8 -10.61 224 4.39% Counts Mar 1st UMass Invite 2025
134 Maine Loss 8-9 -1.12 186 5.02% Counts Mar 1st UMass Invite 2025
76 Williams Loss 1-12 -12.25 207 5.1% Counts (Why) Mar 1st UMass Invite 2025
108 Columbia Loss 8-9 5.17 224 5.02% Counts Mar 2nd UMass Invite 2025
206 Tufts-B Win 9-6 11.89 4 4.72% Counts Mar 2nd UMass Invite 2025
63 Duke Loss 7-10 2.44 375 5.97% Counts Mar 22nd Atlantic Coast Open 2025
140 George Mason Loss 9-11 -11.12 307 6.32% Counts Mar 22nd Atlantic Coast Open 2025
112 Liberty Loss 10-11 4.04 173 6.32% Counts Mar 22nd Atlantic Coast Open 2025
264 Virginia Tech-B Win 15-3 13.52 344 6.32% Counts (Why) Mar 22nd Atlantic Coast Open 2025
170 Messiah Loss 13-15 -16.17 213 6.32% Counts Mar 23rd Atlantic Coast Open 2025
115 RIT Loss 13-15 -3.41 291 6.32% Counts Mar 23rd Atlantic Coast Open 2025
239 Wake Forest Win 10-4 17.46 333 5.52% Counts (Why) Mar 23rd Atlantic Coast Open 2025
364 MIT-B** Win 15-2 0 209 0% Ignored (Why) Apr 12th New England Dev Mens Conferences 2025
294 Northeastern-C Win 10-5 5.82 369 6.67% Counts (Why) Apr 12th New England Dev Mens Conferences 2025
206 Tufts-B Win 12-7 27.78 4 7.51% Counts (Why) Apr 12th New England Dev Mens Conferences 2025
409 Tufts-C** Win 14-2 0 0% Ignored (Why) Apr 12th New England Dev Mens Conferences 2025
294 Northeastern-C Win 10-7 -7.87 369 7.1% Counts Apr 13th New England Dev Mens Conferences 2025
111 Vermont-B Loss 6-9 -15.8 224 6.67% Counts Apr 13th New England Dev 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.