(3) #146 Yale (9-12)

1060.05 (8)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
183 Connecticut College Loss 8-10 -18.66 357 4.12% Counts Feb 10th UMass Invite 2024
62 Massachusetts -B Loss 5-11 -9.23 149 3.89% Counts (Why) Feb 10th UMass Invite 2024
153 Rhode Island Win 10-9 4.04 44 4.24% Counts Feb 10th UMass Invite 2024
148 Rochester Loss 7-8 -5.8 78 3.76% Counts Feb 10th UMass Invite 2024
153 Rhode Island Loss 5-8 -17.69 44 3.5% Counts Feb 11th UMass Invite 2024
270 Rowan Win 12-5 2.2 14 4.06% Counts (Why) Feb 11th UMass Invite 2024
162 Wesleyan Win 13-9 15.22 45 4.24% Counts Feb 11th UMass Invite 2024
46 Williams Loss 6-10 -1.26 34 3.89% Counts Feb 11th UMass Invite 2024
167 Columbia Loss 7-10 -24.58 38 4.76% Counts Mar 2nd No Sleep till Brooklyn 2024
282 Hofstra** Win 6-0 0 129 0% Ignored (Why) Mar 2nd No Sleep till Brooklyn 2024
236 MIT Win 12-5 11.2 17 4.83% Counts (Why) Mar 2nd No Sleep till Brooklyn 2024
86 Bates Loss 9-10 7 87 5.04% Counts Mar 3rd No Sleep till Brooklyn 2024
196 NYU Win 10-8 2.26 36 4.9% Counts Mar 3rd No Sleep till Brooklyn 2024
46 Williams Loss 9-13 2.46 34 5.04% Counts Mar 3rd No Sleep till Brooklyn 2024
167 Columbia Loss 8-13 -40.51 38 6.35% Counts Mar 30th East Coast Invite 2024
101 Cornell Win 10-7 35.39 51 6% Counts Mar 30th East Coast Invite 2024
154 Harvard Win 13-8 31.12 116 6.35% Counts Mar 30th East Coast Invite 2024
123 Pennsylvania Win 10-7 30.46 59 6% Counts Mar 30th East Coast Invite 2024
70 Case Western Reserve Loss 9-11 3.89 4 6.35% Counts Mar 31st East Coast Invite 2024
96 Connecticut Loss 7-12 -22.44 26 6.35% Counts Mar 31st East Coast Invite 2024
98 Dartmouth Loss 9-11 -4.31 16 6.35% Counts Mar 31st East Coast Invite 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.