(3) #83 Rutgers (10-12) ME 1

1432.97 (2)

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# Opponent Result Effect % of Ranking Status Date Event
18 Michigan Loss 10-13 6.56 4.25% Feb 8th Florida Warm Up 2019
65 Florida Win 11-9 15.64 4.25% Feb 8th Florida Warm Up 2019
22 Georgia Loss 6-13 -8.82 4.25% Feb 8th Florida Warm Up 2019
25 South Carolina Loss 8-10 3.93 4.14% Feb 9th Florida Warm Up 2019
17 Minnesota Loss 9-10 17.46 4.25% Feb 9th Florida Warm Up 2019
12 Texas Loss 6-12 -0.1 4.14% Feb 9th Florida Warm Up 2019
72 Alabama-Huntsville Loss 11-15 -14.67 4.25% Feb 9th Florida Warm Up 2019
106 Illinois State Win 13-9 13.9 4.25% Feb 10th Florida Warm Up 2019
49 Northwestern Loss 13-14 3.54 4.25% Feb 10th Florida Warm Up 2019
338 Wake Forest** Win 13-0 0 0% Ignored Feb 23rd Oak Creek Challenge 2019
114 Liberty Loss 9-10 -12.93 4.77% Feb 23rd Oak Creek Challenge 2019
345 American** Win 13-2 0 0% Ignored Feb 23rd Oak Creek Challenge 2019
137 North Carolina-B Win 12-8 12.1 4.77% Feb 23rd Oak Creek Challenge 2019
142 Princeton Loss 9-10 -17.46 4.77% Feb 24th Oak Creek Challenge 2019
206 West Chester Win 13-6 6.68 4.77% Feb 24th Oak Creek Challenge 2019
174 Cedarville Win 15-11 0.78 4.77% Feb 24th Oak Creek Challenge 2019
120 James Madison Loss 9-11 -27.19 6.37% Mar 30th Atlantic Coast Open 2019
33 Johns Hopkins Loss 8-13 -13.48 6.37% Mar 30th Atlantic Coast Open 2019
101 Connecticut Win 12-6 33.24 6.2% Mar 30th Atlantic Coast Open 2019
91 Mary Washington Loss 8-11 -28.32 6.37% Mar 30th Atlantic Coast Open 2019
171 RIT Win 14-12 -8.87 6.37% Mar 31st Atlantic Coast Open 2019
158 Lehigh Win 13-7 17.27 6.37% Mar 31st Atlantic Coast Open 2019
**Blowout Eligible

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.