(1) #130 Liberty (7-15)

773.74 (124)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
80 American Loss 7-11 -2.82 154 4.06% Counts Jan 28th Winta Binta Vinta
33 Ohio State** Loss 2-9 0 145 0% Ignored (Why) Jan 28th Winta Binta Vinta
62 William & Mary Loss 4-11 -3.83 136 3.83% Counts (Why) Jan 28th Winta Binta Vinta
145 Virginia-B Win 11-4 19.18 133 3.83% Counts (Why) Jan 28th Winta Binta Vinta
51 Georgetown** Loss 3-9 0 120 0% Ignored (Why) Jan 29th Winta Binta Vinta
145 Virginia-B Win 9-8 0.3 133 3.94% Counts Jan 29th Winta Binta Vinta
41 James Madison** Loss 5-13 0 120 0% Ignored (Why) Feb 18th Commonwealth Cup Weekend1 2023
57 Virginia Tech Loss 6-11 0.82 118 4.69% Counts Feb 18th Commonwealth Cup Weekend1 2023
116 Cedarville Loss 6-12 -23.45 134 4.82% Counts Feb 19th Commonwealth Cup Weekend1 2023
135 Mary Washington Loss 6-10 -25.88 119 4.55% Counts Feb 19th Commonwealth Cup Weekend1 2023
145 Virginia-B Win 7-6 0.31 133 4.1% Counts Feb 19th Commonwealth Cup Weekend1 2023
58 Williams Loss 3-13 -3.47 110 6.62% Counts (Why) Mar 25th Rodeo 2023
59 Penn State Loss 6-13 -5.14 119 6.62% Counts (Why) Mar 25th Rodeo 2023
144 North Carolina-B Win 9-7 10.76 135 6.07% Counts Mar 25th Rodeo 2023
21 North Carolina State** Loss 4-13 0 132 0% Ignored (Why) Mar 26th Rodeo 2023
71 Massachusetts Loss 9-12 8.1 145 6.62% Counts Mar 26th Rodeo 2023
60 Ohio Loss 6-10 1.9 133 6.07% Counts Mar 26th Rodeo 2023
- George Mason Win 13-2 9.16 148 7.01% Counts (Why) Apr 1st Atlantic Coast Open 2023
181 George Washington Win 11-5 9.17 175 6.43% Counts (Why) Apr 1st Atlantic Coast Open 2023
55 Cornell Loss 5-13 -2.09 101 7.01% Counts (Why) Apr 1st Atlantic Coast Open 2023
181 George Washington Win 12-5 9.62 175 6.73% Counts (Why) Apr 2nd Atlantic Coast Open 2023
55 Cornell Loss 4-13 -2.09 101 7.01% Counts (Why) Apr 2nd Atlantic Coast Open 2023
**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.