(23) #91 Mary Washington (16-6)

1382.51 (87)

Click on column to sort  • 
# Opponent Result Effect % of Ranking Status Date Event
166 Virginia Commonwealth Win 13-11 -2.3 3.59% Feb 2nd Mid Atlantic Warmup 2019
137 North Carolina-B Win 13-11 2.96 3.59% Feb 2nd Mid Atlantic Warmup 2019
39 Vermont Loss 5-13 -10.3 3.59% Feb 2nd Mid Atlantic Warmup 2019
88 Tennessee-Chattanooga Loss 10-12 -7.5 3.59% Feb 2nd Mid Atlantic Warmup 2019
120 James Madison Loss 12-15 -14.9 3.59% Feb 3rd Mid Atlantic Warmup 2019
114 Liberty Win 15-9 16.12 3.59% Feb 3rd Mid Atlantic Warmup 2019
137 North Carolina-B Win 15-13 2.41 3.59% Feb 3rd Mid Atlantic Warmup 2019
32 William & Mary Loss 12-15 2.52 3.8% Feb 9th Virginia Showcase Series 2019 2919
155 Elon Win 12-10 0.25 4.52% Mar 2nd FCS D III Tune Up 2019
234 Florida Tech Win 14-12 -12.09 4.52% Mar 2nd FCS D III Tune Up 2019
173 Georgia College Win 12-10 -3.57 4.52% Mar 2nd FCS D III Tune Up 2019
183 Oberlin Win 10-9 -10.21 4.52% Mar 2nd FCS D III Tune Up 2019
138 Missouri S&T Win 13-8 16.28 4.52% Mar 3rd FCS D III Tune Up 2019
35 Middlebury Loss 11-13 5.45 4.52% Mar 3rd FCS D III Tune Up 2019
253 Anderson Win 12-10 -14.27 4.52% Mar 3rd FCS D III Tune Up 2019
83 Rutgers Win 11-8 25.14 5.7% Mar 30th Atlantic Coast Open 2019
118 MIT Win 9-7 10.18 5.23% Mar 30th Atlantic Coast Open 2019
345 American Win 13-9 -27.92 5.7% Mar 30th Atlantic Coast Open 2019
151 SUNY-Binghamton Win 10-9 -5.76 5.7% Mar 30th Atlantic Coast Open 2019
66 Penn State Win 11-10 16.78 5.7% Mar 31st Atlantic Coast Open 2019
35 Middlebury Loss 9-13 -4.51 5.7% Mar 31st Atlantic Coast Open 2019
115 Villanova Win 12-9 15.66 5.7% 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.