(7) #21 Tufts (9-12)

1828.7 (79)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
12 Alabama-Huntsville Win 13-12 11.63 3 3.86% Counts Feb 2nd Florida Warm Up 2024
17 Brigham Young Loss 10-13 -11.29 39 3.86% Counts Feb 2nd Florida Warm Up 2024
185 South Florida** Win 13-3 0 12 0% Ignored (Why) Feb 2nd Florida Warm Up 2024
14 Texas Loss 8-13 -15.57 30 3.86% Counts Feb 3rd Florida Warm Up 2024
52 Virginia Tech Win 13-4 9.9 43 3.86% Counts (Why) Feb 3rd Florida Warm Up 2024
8 Vermont Loss 11-15 -6.87 36 3.86% Counts Feb 3rd Florida Warm Up 2024
56 Emory Win 15-4 8.78 31 3.86% Counts (Why) Feb 4th Florida Warm Up 2024
2 Georgia Loss 7-13 -5.79 80 4.86% Counts Mar 2nd Smoky Mountain Invite 2024
4 Massachusetts Loss 13-15 9.81 47 4.86% Counts Mar 2nd Smoky Mountain Invite 2024
11 Minnesota Loss 10-13 -7.9 102 4.86% Counts Mar 2nd Smoky Mountain Invite 2024
13 North Carolina State Loss 9-13 -15.35 42 4.86% Counts Mar 2nd Smoky Mountain Invite 2024
15 California Win 15-11 24.34 35 4.86% Counts Mar 3rd Smoky Mountain Invite 2024
13 North Carolina State Loss 10-15 -17.14 42 4.86% Counts Mar 3rd Smoky Mountain Invite 2024
14 Texas Loss 11-15 -13.95 30 4.86% Counts Mar 3rd Smoky Mountain Invite 2024
2 Georgia Loss 7-13 -7.4 80 6.12% Counts Mar 30th Easterns 2024
28 North Carolina-Wilmington Win 13-8 26.21 109 6.12% Counts Mar 30th Easterns 2024
7 Pittsburgh Loss 10-12 1.72 86 6.12% Counts Mar 30th Easterns 2024
33 Wisconsin Win 13-12 -3.8 14 6.12% Counts Mar 30th Easterns 2024
42 Michigan Loss 14-15 -25.25 170 6.12% Counts Mar 31st Easterns 2024
16 Penn State Win 12-11 14.18 59 6.12% Counts Mar 31st Easterns 2024
29 South Carolina Win 15-9 24.17 52 6.12% 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.