(23) #180 SUNY-Buffalo (5-14)

620.02 (442)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
58 Cornell** Loss 1-12 0 1 0% Ignored (Why) Mar 2nd No Sleep till Brooklyn 2024
105 Mount Holyoke Loss 4-12 -2.36 221 6.97% Counts (Why) Mar 2nd No Sleep till Brooklyn 2024
201 Columbia-B Win 8-6 8.17 143 6.24% Counts Mar 3rd No Sleep till Brooklyn 2024
164 SUNY-Stony Brook Loss 3-6 -18.94 302 5% Counts Mar 3rd No Sleep till Brooklyn 2024
144 Skidmore Loss 3-10 -26.76 363 8% Counts (Why) Mar 30th Northeast Classic 2024
239 SUNY-Albany Win 9-4 -2.87 345 7.57% Counts (Why) Mar 30th Northeast Classic 2024
191 Syracuse Loss 5-8 -42.7 243 7.57% Counts Mar 30th Northeast Classic 2024
- Colgate Loss 4-8 -28.09 319 7.28% Counts Mar 31st Northeast Classic 2024
181 Vermont-C Loss 4-5 -9.02 511 6.3% Counts Mar 31st Northeast Classic 2024
228 Rensselaer Polytech Win 7-3 11.48 171 6.64% Counts (Why) Mar 31st Northeast Classic 2024
58 Cornell** Loss 1-13 0 1 0% Ignored (Why) Apr 20th Western NY D I Womens Conferences 2024
54 Ottawa** Loss 1-12 0 0% Ignored (Why) Apr 20th Western NY D I Womens Conferences 2024
150 RIT Win 9-7 58.55 24 9.99% Counts Apr 20th Western NY D I Womens Conferences 2024
191 Syracuse Win 9-3 52.71 243 9.01% Counts (Why) Apr 20th Western NY D I Womens Conferences 2024
110 Rutgers Loss 5-7 21.9 8 9.17% Counts Apr 27th Metro East D I College Womens Regionals 2024
71 Columbia** Loss 2-13 0 33 0% Ignored (Why) Apr 27th Metro East D I College Womens Regionals 2024
54 Ottawa** Loss 2-13 0 0% Ignored (Why) Apr 27th Metro East D I College Womens Regionals 2024
39 SUNY-Binghamton** Loss 5-13 0 17 0% Ignored (Why) Apr 27th Metro East D I College Womens Regionals 2024
150 RIT Loss 6-9 -19.48 24 10.25% Counts Apr 28th Metro East D I College Womens Regionals 2024
**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.