(9) #99 MIT (13-8)

1127.48 (37)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
271 Virginia-B** Win 13-2 0 84 0% Ignored (Why) Feb 23rd Commonwealth Cup 2019
70 Maryland Loss 8-11 -8.15 20 4.71% Counts Feb 23rd Commonwealth Cup 2019
161 Drexel Win 13-0 14.02 9 4.71% Counts (Why) Feb 23rd Commonwealth Cup 2019
117 Catholic Loss 10-12 -15.92 109 4.71% Counts Feb 23rd Commonwealth Cup 2019
81 Ohio Win 10-9 13.15 2 4.71% Counts Feb 24th Commonwealth Cup 2019
135 Princeton Win 8-7 -2.62 32 4.19% Counts Feb 24th Commonwealth Cup 2019
171 NYU Win 10-3 10.06 99 4.62% Counts (Why) Mar 9th No Sleep Till Brooklyn
47 Williams Loss 5-9 -6.19 35 4.54% Counts Mar 9th No Sleep Till Brooklyn
44 Brown Loss 5-11 -8.83 56 4.86% Counts (Why) Mar 9th No Sleep Till Brooklyn
183 Marist Win 13-6 5.51 82 5.29% Counts (Why) Mar 9th No Sleep Till Brooklyn
130 Connecticut Win 7-2 18.56 17 3.84% Counts (Why) Mar 10th No Sleep Till Brooklyn
47 Williams Loss 4-9 -9.2 35 4.38% Counts (Why) Mar 10th No Sleep Till Brooklyn
44 Brown Loss 4-8 -6.05 56 4.2% Counts Mar 10th No Sleep Till Brooklyn
195 Texas A&M Win 9-6 -7.82 37 5.28% Counts Mar 23rd Womens College Centex 2019
217 Minnesota-B Win 11-6 -9.15 43 5.62% Counts (Why) Mar 23rd Womens College Centex 2019
168 Rice Win 9-7 -4.7 43 5.45% Counts Mar 23rd Womens College Centex 2019
102 LSU Win 13-6 37.37 23 5.94% Counts (Why) Mar 23rd Womens College Centex 2019
113 Oklahoma Win 9-8 2.99 26 5.62% Counts Mar 24th Womens College Centex 2019
72 Texas-Dallas Loss 11-14 -7.22 24 5.94% Counts Mar 24th Womens College Centex 2019
108 Southern California Win 9-7 12.93 23 5.45% Counts Mar 24th Womens College Centex 2019
80 St Olaf Loss 5-15 -28.82 28 5.94% Counts (Why) Mar 24th Womens College Centex 2019
**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.