(3) #12 Alabama-Huntsville (15-6) SE 2

1993.67 (3)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
33 Wisconsin Win 13-8 6.32 14 4.1% Counts Feb 2nd Florida Warm Up 2024
21 Tufts Loss 12-13 -12.38 79 4.1% Counts Feb 2nd Florida Warm Up 2024
101 Cornell Win 13-8 -11.66 51 4.1% Counts Feb 2nd Florida Warm Up 2024
41 Florida Win 13-3 7.57 4 4.1% Counts (Why) Feb 3rd Florida Warm Up 2024
17 Brigham Young Win 11-9 5.59 39 4.1% Counts Feb 3rd Florida Warm Up 2024
4 Massachusetts Loss 12-15 -2.53 47 4.1% Counts Feb 3rd Florida Warm Up 2024
14 Texas Loss 8-10 -13.28 30 3.99% Counts Feb 4th Florida Warm Up 2024
48 Missouri Win 12-7 1.89 13 4.34% Counts (Why) Feb 10th Queen City Tune Up 2024
92 Tennessee Win 15-8 -7.34 16 4.34% Counts (Why) Feb 10th Queen City Tune Up 2024
29 South Carolina Win 15-8 11.57 52 4.34% Counts (Why) Feb 10th Queen City Tune Up 2024
72 Georgetown** Win 15-5 0 30 0% Ignored (Why) Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Win 11-7 4.03 20 4.22% Counts Feb 11th Queen City Tune Up 2024
1 North Carolina Loss 12-15 -0.25 18 4.34% Counts Feb 11th Queen City Tune Up 2024
13 North Carolina State Win 14-12 7.89 42 4.34% Counts Feb 11th Queen City Tune Up 2024
16 Penn State Win 13-10 17.78 59 6.5% Counts Mar 30th Easterns 2024
5 Cal Poly-SLO Win 13-12 21.28 12 6.5% Counts Mar 30th Easterns 2024
9 Brown Loss 11-13 -13.73 49 6.5% Counts Mar 30th Easterns 2024
36 North Carolina-Charlotte Win 11-8 -0.68 20 6.5% Counts Mar 30th Easterns 2024
29 South Carolina Win 15-5 20.18 52 6.5% Counts (Why) Mar 31st Easterns 2024
11 Minnesota Loss 9-15 -35.25 102 6.5% Counts Mar 31st Easterns 2024
42 Michigan Win 13-10 -6.89 170 6.5% Counts Mar 31st Easterns 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.