(3) #95 Temple (7-11)

1029.11 (173)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
28 Duke Loss 6-10 7.94 139 4.82% Counts Jan 21st Carolina Kickoff womens and nonbinary
1 North Carolina** Loss 1-15 0 141 0% Ignored (Why) Jan 21st Carolina Kickoff womens and nonbinary
64 Appalachian State Loss 3-8 -15.11 136 4.08% Counts (Why) Jan 22nd Carolina Kickoff womens and nonbinary
21 North Carolina State** Loss 3-12 0 132 0% Ignored (Why) Jan 22nd Carolina Kickoff womens and nonbinary
144 North Carolina-B Win 10-7 1.11 135 4.97% Counts Jan 22nd Carolina Kickoff womens and nonbinary
47 Florida Loss 7-11 -2.06 142 6.82% Counts Feb 25th Commonwealth Cup Weekend2 2023
71 Massachusetts Loss 8-11 -12.15 145 7.01% Counts Feb 25th Commonwealth Cup Weekend2 2023
33 Ohio State** Loss 6-15 0 145 0% Ignored (Why) Feb 25th Commonwealth Cup Weekend2 2023
65 Carnegie Mellon Win 12-11 27.87 115 7.01% Counts Feb 26th Commonwealth Cup Weekend2 2023
99 MIT Loss 8-9 -12.13 201 6.63% Counts Feb 26th Commonwealth Cup Weekend2 2023
56 Tennessee Win 8-6 39.16 128 6.02% Counts Feb 26th Commonwealth Cup Weekend2 2023
185 Messiah Win 9-5 -20.66 161 8.03% Counts (Why) Apr 1st Shady Encounters
97 NYU Win 5-3 25.83 9 6.07% Counts (Why) Apr 1st Shady Encounters
67 Mount Holyoke Loss 5-7 -7.19 27 7.43% Counts Apr 1st Shady Encounters
139 Wesleyan Win 9-0 22.74 1 7.74% Counts (Why) Apr 1st Shady Encounters
151 Rutgers Win 7-4 5.56 255 7.12% Counts (Why) Apr 2nd Shady Encounters
67 Mount Holyoke Loss 3-8 -28.37 27 7.28% Counts (Why) Apr 2nd Shady Encounters
61 Vermont-B Loss 2-12 -33.79 174 8.98% Counts (Why) Apr 2nd Shady Encounters
**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.