(5) #49 Northwestern (7-16)

1637.69 (39)

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# Opponent Result Effect % of Ranking Status Date Event
4 Pittsburgh Loss 7-13 -0.39 3.61% Feb 8th Florida Warm Up 2019
48 Kennesaw State Loss 8-13 -18.24 3.61% Feb 8th Florida Warm Up 2019
28 Northeastern Win 12-11 9.85 3.61% Feb 8th Florida Warm Up 2019
6 Brigham Young Loss 9-13 2.94 3.61% Feb 9th Florida Warm Up 2019
2 Brown Loss 10-12 13.23 3.61% Feb 9th Florida Warm Up 2019
65 Florida Loss 10-11 -8.5 3.61% Feb 9th Florida Warm Up 2019
80 Oklahoma Loss 13-15 -14.97 3.61% Feb 9th Florida Warm Up 2019
83 Rutgers Win 14-13 -2.98 3.61% Feb 10th Florida Warm Up 2019
55 Florida State Win 13-6 21.49 3.61% Feb 10th Florida Warm Up 2019
6 Brigham Young Loss 9-11 11.11 4.29% Mar 2nd Stanford Invite 2019
21 California Loss 6-13 -17.67 4.29% Mar 2nd Stanford Invite 2019
3 Oregon Loss 8-13 2.47 4.29% Mar 2nd Stanford Invite 2019
14 Ohio State Loss 9-12 0.4 4.29% Mar 2nd Stanford Invite 2019
50 Stanford Win 12-11 5.38 4.29% Mar 3rd Stanford Invite 2019
17 Minnesota Loss 6-10 -7.49 3.94% Mar 3rd Stanford Invite 2019
10 Washington Loss 5-13 -8.66 4.29% Mar 3rd Stanford Invite 2019
4 Pittsburgh Loss 7-13 -0.59 5.41% Mar 30th Easterns 2019 Men
32 William & Mary Loss 10-12 -7.38 5.41% Mar 30th Easterns 2019 Men
20 Tufts Win 13-12 20.09 5.41% Mar 30th Easterns 2019 Men
26 North Carolina-Wilmington Win 13-11 21.27 5.41% Mar 30th Easterns 2019 Men
45 California-Santa Barbara Loss 7-15 -32.83 5.41% Mar 31st Easterns 2019 Men
47 Maryland Win 13-10 19.82 5.41% Mar 31st Easterns 2019 Men
54 Virginia Tech Loss 11-12 -8.19 5.41% Mar 31st Easterns 2019 Men
**Blowout Eligible

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.