(8) #61 Emory (11-11)

1576.98 (47)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
43 Alabama-Huntsville Loss 8-9 0.04 7 3.74% Counts Jan 28th T Town Throwdown1
238 Spring Hill Win 13-8 -13.09 2 3.96% Counts Jan 28th T Town Throwdown1
131 Georgia State Win 12-4 10.49 14 3.8% Counts (Why) Jan 28th T Town Throwdown1
259 Jacksonville State** Win 11-3 0 7 0% Ignored (Why) Jan 28th T Town Throwdown1
85 Alabama Win 11-10 -0.21 10 3.96% Counts Jan 29th T Town Throwdown1
89 Mississippi State Win 11-9 4.38 7 3.96% Counts Jan 29th T Town Throwdown1
108 Vanderbilt Win 13-11 -0.85 54 3.96% Counts Jan 29th T Town Throwdown1
42 Grand Canyon Loss 8-9 0.15 4 4.45% Counts Feb 18th President’s Day Invite
10 California-Santa Cruz Win 12-11 31.51 1 4.71% Counts Feb 18th President’s Day Invite
9 Oregon Loss 9-13 7 10 4.71% Counts Feb 18th President’s Day Invite
6 Colorado Loss 7-13 3.12 13 4.71% Counts Feb 19th President’s Day Invite
46 Western Washington Win 9-7 17.65 26 4.32% Counts Feb 19th President’s Day Invite
17 Washington Loss 10-12 8.65 38 4.71% Counts Feb 19th President’s Day Invite
47 Colorado State Win 12-10 15.23 10 4.71% Counts Feb 20th President’s Day Invite
18 California Loss 9-13 -1.68 37 4.71% Counts Feb 20th President’s Day Invite
59 Cincinnati Loss 5-6 -6.58 70 5.07% Counts Apr 1st Huck Finn1
49 Notre Dame Loss 3-5 -15.91 27 4.32% Counts Apr 1st Huck Finn1
108 Vanderbilt Win 6-5 -6.64 54 5.07% Counts Apr 1st Huck Finn1
22 Washington University Loss 5-9 -12.17 53 5.71% Counts Apr 1st Huck Finn1
118 Marquette Win 10-8 -0.94 16 6.48% Counts Apr 2nd Huck Finn1
64 St. Olaf Loss 8-11 -26.72 28 6.66% Counts Apr 2nd Huck Finn1
75 Grinnell Loss 8-9 -14.46 39 6.3% Counts Apr 2nd Huck Finn1
**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.