(2) #131 Pittsburgh-B (12-7)

1121.3 (64)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
149 Davidson Win 8-7 2.6 54 4.25% Counts Feb 15th 2025 Commonwealth Cup Weekend 1
241 Michigan-B Win 13-8 0.86 180 4.79% Counts Feb 15th 2025 Commonwealth Cup Weekend 1
255 Wake Forest Win 12-7 -0.72 9 4.79% Counts (Why) Feb 15th 2025 Commonwealth Cup Weekend 1
149 Davidson Loss 8-11 -21.73 54 4.79% Counts Feb 16th 2025 Commonwealth Cup Weekend 1
124 Denver Loss 11-12 -5.03 36 4.79% Counts Feb 16th 2025 Commonwealth Cup Weekend 1
303 Ball State** Win 15-4 0 149 0% Ignored (Why) Mar 15th Grand Rapids Invite 2025
212 Eastern Michigan Win 15-9 9.28 246 6.03% Counts Mar 15th Grand Rapids Invite 2025
292 Western Michigan** Win 15-4 0 83 0% Ignored (Why) Mar 15th Grand Rapids Invite 2025
190 Toronto Win 14-5 21.72 142 6.03% Counts (Why) Mar 15th Grand Rapids Invite 2025
60 Michigan State Loss 9-13 -3.44 88 6.03% Counts Mar 16th Grand Rapids Invite 2025
143 Michigan Tech Win 10-9 4.28 73 6.03% Counts Mar 16th Grand Rapids Invite 2025
221 Wisconsin-B Win 15-6 12.16 102 6.03% Counts (Why) Mar 16th Grand Rapids Invite 2025
71 Case Western Reserve Loss 4-15 -22.97 41 6.77% Counts (Why) Mar 29th East Coast Invite 2025
108 Columbia Loss 11-12 -1.97 52 6.77% Counts Mar 29th East Coast Invite 2025
192 Princeton Win 13-7 21.11 216 6.77% Counts (Why) Mar 29th East Coast Invite 2025
177 Towson Win 11-9 3.88 47 6.77% Counts Mar 29th East Coast Invite 2025
174 Delaware Win 9-5 21.34 78 5.81% Counts (Why) Mar 30th East Coast Invite 2025
88 Georgetown Loss 7-15 -29.97 78 6.77% Counts (Why) Mar 30th East Coast Invite 2025
116 West Chester Loss 11-13 -11.47 101 6.77% Counts Mar 30th East Coast Invite 2025
**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.