(21) #51 Purdue (11-9)

1555.86 (185)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
8 Brigham Young Loss 7-13 -1.83 13 4.1% Counts Jan 31st Florida Warm Up 2025
62 Tulane Win 10-7 11.94 4 3.88% Counts Jan 31st Florida Warm Up 2025
40 Wisconsin Loss 6-13 -22.55 19 4.1% Counts (Why) Jan 31st Florida Warm Up 2025
28 Pittsburgh Loss 6-13 -16.71 11 4.1% Counts (Why) Feb 1st Florida Warm Up 2025
134 South Florida Win 13-5 6.69 68 4.1% Counts (Why) Feb 1st Florida Warm Up 2025
40 Wisconsin Loss 8-13 -18.11 19 4.1% Counts Feb 1st Florida Warm Up 2025
38 Utah State Loss 12-13 -2.06 44 4.1% Counts Feb 2nd Florida Warm Up 2025
103 Texas A&M Loss 5-9 -30.77 133 3.52% Counts Feb 2nd Florida Warm Up 2025
64 James Madison Win 13-12 1.28 70 4.6% Counts Feb 15th Queen City Tune Up 2025
49 North Carolina State Loss 8-13 -23.49 21 4.6% Counts Feb 15th Queen City Tune Up 2025
25 Penn State Loss 9-13 -7.09 56 4.6% Counts Feb 15th Queen City Tune Up 2025
75 Carnegie Mellon Win 10-7 9.33 26 4.35% Counts Feb 16th Queen City Tune Up 2025
37 North Carolina-Wilmington Win 9-8 9.29 84 4.35% Counts Feb 16th Queen City Tune Up 2025
135 Mississippi State Win 13-6 10.81 23 6.5% Counts (Why) Mar 29th Huck Finn 2025
141 Northwestern Win 15-8 5.69 46 6.5% Counts (Why) Mar 29th Huck Finn 2025
74 Oklahoma Christian Win 11-9 4.97 26 6.5% Counts Mar 29th Huck Finn 2025
82 St Olaf Win 13-8 18.18 9 6.5% Counts Mar 29th Huck Finn 2025
50 Colorado State Win 12-9 24.43 35 6.5% Counts Mar 30th Huck Finn 2025
63 Notre Dame Win 11-9 10.63 277 6.5% Counts Mar 30th Huck Finn 2025
15 Washington University Loss 11-13 11.61 70 6.5% Counts Mar 30th Huck Finn 2025
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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.