(2) #33 Johns Hopkins (17-4)

1731.17 (28)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
88 Tennessee-Chattanooga Win 13-7 9.96 11 3.9% Counts (Why) Feb 16th Easterns Qualifier 2019
44 Virginia Win 12-11 2.65 126 3.9% Counts Feb 16th Easterns Qualifier 2019
145 Dayton Win 13-11 -12.69 12 3.9% Counts Feb 16th Easterns Qualifier 2019
101 Connecticut Win 11-8 -0.38 79 3.9% Counts Feb 16th Easterns Qualifier 2019
81 Georgia Tech Win 15-12 0.68 48 3.9% Counts Feb 17th Easterns Qualifier 2019
38 Purdue Win 15-13 7.71 12 3.9% Counts Feb 17th Easterns Qualifier 2019
44 Virginia Loss 9-15 -23.34 126 3.9% Counts Feb 17th Easterns Qualifier 2019
204 SUNY-Buffalo Win 13-8 -13.6 58 4.91% Counts Mar 16th Oak Creek Invite 2019
66 Penn State Win 13-11 1.7 26 4.91% Counts Mar 16th Oak Creek Invite 2019
108 North Carolina-Charlotte Win 13-8 4.65 10 4.91% Counts Mar 16th Oak Creek Invite 2019
54 Virginia Tech Win 13-8 19.87 34 4.91% Counts Mar 16th Oak Creek Invite 2019
101 Connecticut Win 15-13 -8.31 79 4.91% Counts Mar 17th Oak Creek Invite 2019
47 Maryland Win 15-12 11.66 58 4.91% Counts Mar 17th Oak Creek Invite 2019
18 Michigan Loss 12-15 -6.35 37 4.91% Counts Mar 17th Oak Creek Invite 2019
120 James Madison Win 13-7 6.37 5 5.52% Counts (Why) Mar 30th Atlantic Coast Open 2019
101 Connecticut Loss 8-9 -27.52 79 5.22% Counts Mar 30th Atlantic Coast Open 2019
83 Rutgers Win 13-8 11.56 2 5.52% Counts Mar 30th Atlantic Coast Open 2019
171 RIT Win 13-6 -2.89 45 5.52% Counts (Why) Mar 30th Atlantic Coast Open 2019
66 Penn State Win 15-4 23.59 26 5.52% Counts (Why) Mar 31st Atlantic Coast Open 2019
35 Middlebury Loss 10-12 -14.17 57 5.52% Counts Mar 31st Atlantic Coast Open 2019
118 MIT Win 15-7 9.14 58 5.52% Counts (Why) Mar 31st Atlantic Coast Open 2019
**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.