(15) #85 Richmond (19-7)

1429.7 (89)

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# Opponent Result Effect % of Ranking Status Date Event
73 Temple Loss 7-11 -13.99 3.26% Jan 25th Carolina Kickoff 2019
78 Carleton College-GoP Loss 9-13 -13.51 3.34% Jan 26th Carolina Kickoff 2019
62 Duke Win 12-7 22.21 3.34% Jan 26th Carolina Kickoff 2019
1 North Carolina** Loss 4-13 0 0% Ignored Jan 26th Carolina Kickoff 2019
25 South Carolina Loss 4-15 -8.41 3.34% Jan 27th Carolina Kickoff 2019
73 Temple Win 11-10 6.1 3.34% Jan 27th Carolina Kickoff 2019
197 George Mason Win 12-10 -6.99 3.54% Feb 2nd Mid Atlantic Warmup 2019
195 George Washington Win 13-6 6.4 3.54% Feb 2nd Mid Atlantic Warmup 2019
158 Lehigh Win 13-7 9.44 3.54% Feb 2nd Mid Atlantic Warmup 2019
151 SUNY-Binghamton Win 13-12 -5.24 3.54% Feb 2nd Mid Atlantic Warmup 2019
110 Williams Win 15-12 6.85 3.54% Feb 3rd Mid Atlantic Warmup 2019
39 Vermont Loss 8-15 -10.61 3.54% Feb 3rd Mid Atlantic Warmup 2019
88 Tennessee-Chattanooga Loss 9-11 -9.54 3.54% Feb 3rd Mid Atlantic Warmup 2019
107 Franciscan Win 13-11 5.82 4.46% Mar 2nd FCS D III Tune Up 2019
251 Samford Win 13-5 1.01 4.46% Mar 2nd FCS D III Tune Up 2019
138 Missouri S&T Win 13-8 13.86 4.46% Mar 2nd FCS D III Tune Up 2019
155 Elon Win 12-8 7.52 4.46% Mar 2nd FCS D III Tune Up 2019
183 Oberlin Win 12-7 6.2 4.46% Mar 3rd FCS D III Tune Up 2019
75 Air Force Win 11-7 23.38 4.34% Mar 3rd FCS D III Tune Up 2019
173 Georgia College Win 9-7 -3.47 4.1% Mar 3rd FCS D III Tune Up 2019
32 William & Mary Win 16-14 29.45 5.31% Mar 23rd Virginia Showcase Series 32319
182 Messiah Win 12-11 -15.61 5.62% Mar 30th D3 EASTUR 2019
248 Shippensburg Win 12-7 -2.55 5.62% Mar 30th D3 EASTUR 2019
320 Ohio State-B** Win 13-5 0 0% Ignored Mar 30th D3 EASTUR 2019
113 Davidson Loss 5-13 -43.37 5.62% Mar 31st D3 EASTUR 2019
141 Wesleyan Win 12-11 -5.33 5.62% Mar 31st D3 EASTUR 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.