(33) #128 SUNY-Binghamton (7-13)

1128.07 (178)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
69 Auburn Loss 3-10 -12.52 76 4.01% Counts (Why) Feb 22nd Easterns Qualifier 2025
64 James Madison Loss 6-13 -13.03 70 4.59% Counts (Why) Feb 22nd Easterns Qualifier 2025
63 Notre Dame Loss 10-13 0.16 277 4.59% Counts Feb 22nd Easterns Qualifier 2025
27 South Carolina Loss 9-12 14.73 50 4.59% Counts Feb 22nd Easterns Qualifier 2025
96 Appalachian State Loss 6-10 -15.39 68 4.21% Counts Feb 23rd Easterns Qualifier 2025
183 Kennesaw State Win 14-9 11.38 74 4.59% Counts Feb 23rd Easterns Qualifier 2025
102 Syracuse Loss 12-13 0.35 140 4.59% Counts Feb 23rd Easterns Qualifier 2025
176 Ithaca Win 13-4 24.81 155 5.79% Counts (Why) Mar 22nd Salt City Classic
301 Rensselaer Polytech** Win 13-4 0 128 0% Ignored (Why) Mar 22nd Salt City Classic
80 Rochester Loss 7-8 4.74 89 5.14% Counts Mar 22nd Salt City Classic
73 Williams Loss 6-11 -16.77 91 5.47% Counts Mar 22nd Salt City Classic
153 Carleton University Loss 13-14 -12.74 172 5.79% Counts Mar 23rd Salt City Classic
102 Syracuse Loss 8-13 -22.34 140 5.79% Counts Mar 23rd Salt City Classic
75 Carnegie Mellon Loss 9-10 7.72 26 6.13% Counts Mar 29th East Coast Invite 2025
132 Rutgers Win 8-7 6.54 135 5.45% Counts Mar 29th East Coast Invite 2025
101 Yale Loss 8-13 -23.62 46 6.13% Counts Mar 29th East Coast Invite 2025
177 Towson Win 15-4 25.95 47 6.13% Counts (Why) Mar 29th East Coast Invite 2025
174 Delaware Win 9-7 5.32 78 5.62% Counts Mar 30th East Coast Invite 2025
101 Yale Win 13-8 41.17 46 6.13% Counts Mar 30th East Coast Invite 2025
116 West Chester Loss 5-9 -25.82 101 5.26% Counts Mar 30th East Coast Invite 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.