() #25 Pittsburgh (10-10)

1962.92 (9)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
97 Appalachian State Win 12-7 -12.73 1 5.42% Counts (Why) Feb 10th Queen City Tune Up 2024
3 Carleton College** Loss 5-13 0 17 0% Ignored (Why) Feb 10th Queen City Tune Up 2024
22 Notre Dame Win 11-10 12.46 6 5.42% Counts Feb 10th Queen City Tune Up 2024
37 Washington University Win 10-8 3.81 10 5.28% Counts Feb 10th Queen City Tune Up 2024
7 Tufts Loss 7-12 2.11 52 5.42% Counts Feb 11th Queen City Tune Up 2024
29 Wisconsin Win 14-10 20.03 98 5.42% Counts Feb 11th Queen City Tune Up 2024
96 Chicago Loss 11-12 -54.24 104 6.09% Counts Feb 24th Commonwealth Cup Weekend 2 2024
21 Ohio State Win 11-10 15.83 3 6.09% Counts Feb 24th Commonwealth Cup Weekend 2 2024
52 Yale Win 15-13 -10.03 52 6.09% Counts Feb 24th Commonwealth Cup Weekend 2 2024
16 Georgia Loss 5-11 -24.15 13 5.59% Counts (Why) Feb 25th Commonwealth Cup Weekend 2 2024
22 Notre Dame Loss 5-11 -30.04 6 5.59% Counts (Why) Feb 25th Commonwealth Cup Weekend 2 2024
35 Ohio Win 7-6 -2.11 23 5.03% Counts Feb 25th Commonwealth Cup Weekend 2 2024
41 SUNY-Binghamton Win 10-3 19.13 76 5.32% Counts (Why) Feb 25th Commonwealth Cup Weekend 2 2024
24 California-Davis Win 9-5 31.48 14 5.54% Counts (Why) Mar 2nd Stanford Invite 2024
9 California-Santa Barbara Loss 4-9 -7.95 18 5.33% Counts (Why) Mar 2nd Stanford Invite 2024
2 Vermont** Loss 2-10 0 50 0% Ignored (Why) Mar 2nd Stanford Invite 2024
10 Washington Win 6-4 36.88 21 4.68% Counts (Why) Mar 2nd Stanford Invite 2024
15 California-San Diego Loss 9-10 5.14 18 6.45% Counts Mar 3rd Stanford Invite 2024
9 California-Santa Barbara Loss 5-8 0.3 18 5.33% Counts Mar 3rd Stanford Invite 2024
8 Colorado Loss 5-11 -6.59 23 5.92% Counts (Why) Mar 3rd Stanford Invite 2024
**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.