(9) #48 Georgia (14-8)

1955.58 (389)

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# Opponent Result Effect % of Ranking Status Date Event
62 Central Florida Win 9-5 15.18 3.92% Jan 13th Florida Winter Classic 2018
174 Florida-B** Win 12-3 0 0% Ignored Jan 13th Florida Winter Classic 2018
180 South Florida** Win 11-2 0 0% Ignored Jan 13th Florida Winter Classic 2018
141 North Georgia** Win 13-3 0 0% Ignored Jan 13th Florida Winter Classic 2018
89 Iowa Win 13-7 8.25 4.56% Jan 14th Florida Winter Classic 2018
8 West Chester Loss 10-14 7.25 4.56% Jan 14th Florida Winter Classic 2018
46 North Carolina-Wilmington Loss 6-9 -16.73 4.06% Jan 14th Florida Winter Classic 2018
32 Florida Loss 3-10 -19.74 3.99% Jan 14th Florida Winter Classic 2018
25 Notre Dame Win 7-5 23.09 4.31% Feb 3rd Queen City Tune Up 2018 College Women
93 Cornell Win 11-6 7.13 5.13% Feb 3rd Queen City Tune Up 2018 College Women
1 Dartmouth** Loss 5-13 0 0% Ignored Feb 3rd Queen City Tune Up 2018 College Women
66 Virginia Win 10-9 -3.38 5.43% Feb 3rd Queen City Tune Up 2018 College Women
7 Tufts Loss 6-15 -3.23 6.45% Feb 24th Commonwealth Cup 2018
46 North Carolina-Wilmington Win 13-10 24.2 6.45% Feb 24th Commonwealth Cup 2018
66 Virginia Win 11-9 4.51 6.45% Feb 24th Commonwealth Cup 2018
75 Pennsylvania Win 12-9 6.3 6.45% Feb 25th Commonwealth Cup 2018
59 South Carolina Loss 11-12 -17.44 6.45% Feb 25th Commonwealth Cup 2018
66 Virginia Win 9-7 6.01 5.92% Feb 25th Commonwealth Cup 2018
148 Virginia Tech** Win 15-4 0 0% Ignored Mar 31st Easterns 2018
46 North Carolina-Wilmington Loss 12-13 -9.65 8.62% Mar 31st Easterns 2018
39 Clemson Loss 9-15 -42.6 8.62% Mar 31st Easterns 2018
93 Cornell Win 15-9 9.48 8.62% Mar 31st Easterns 2018
**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.