(15) #221 Christopher Newport (6-13)

995.31 (217)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
53 William & Mary Loss 7-13 6.26 159 3.83% Counts Jan 25th Mid Atlantic Warm Up 2025
282 Navy Win 13-6 14.67 375 3.83% Counts (Why) Jan 25th Mid Atlantic Warm Up 2025
167 Pennsylvania Win 13-8 28.27 298 3.83% Counts Jan 25th Mid Atlantic Warm Up 2025
111 Vermont-B Loss 1-13 -7.04 224 3.83% Counts (Why) Jan 25th Mid Atlantic Warm Up 2025
59 James Madison Loss 8-14 5.51 212 3.83% Counts Jan 26th Mid Atlantic Warm Up 2025
115 RIT Loss 10-13 2.42 291 3.83% Counts Jan 26th Mid Atlantic Warm Up 2025
140 George Mason Loss 7-13 -9.86 307 3.83% Counts Jan 26th Mid Atlantic Warm Up 2025
76 Williams Loss 1-13 -0.19 207 6.44% Counts (Why) Mar 29th Easterns 2025
67 Franciscan** Loss 0-13 0 228 0% Ignored (Why) Mar 29th Easterns 2025
290 Mary Washington Win 11-4 21.3 5.91% Counts (Why) Mar 29th Easterns 2025
78 Richmond Loss 3-13 -0.88 234 6.44% Counts (Why) Mar 29th Easterns 2025
102 North Carolina-Asheville Loss 5-15 -9.32 158 6.44% Counts (Why) Mar 30th Easterns 2025
142 Davidson Loss 8-15 -18.48 237 6.44% Counts Mar 30th Easterns 2025
102 North Carolina-Asheville Loss 1-11 -9.62 158 6.63% Counts (Why) Apr 12th Atlantic Coast D III Mens Conferences 2025
78 Richmond Loss 2-11 -0.91 234 6.63% Counts (Why) Apr 12th Atlantic Coast D III Mens Conferences 2025
290 Mary Washington Win 11-6 20.98 6.83% Counts (Why) Apr 12th Atlantic Coast D III Mens Conferences 2025
365 High Point Win 11-7 -12.67 7.03% Counts Apr 12th Atlantic Coast D III Mens Conferences 2025
142 Davidson Loss 7-15 -23.66 237 7.22% Counts (Why) Apr 13th Atlantic Coast D III Mens Conferences 2025
282 Navy Win 13-12 -8.28 375 7.22% Counts Apr 13th Atlantic Coast D III Mens Conferences 2025
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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.