() #20 Tufts (10-12)

1864.15 (8)

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# Opponent Result Effect % of Ranking Status Date Event
69 Emory Win 11-10 -8.88 3.71% Feb 8th Florida Warm Up 2019
15 Central Florida Loss 11-13 -3.95 3.71% Feb 8th Florida Warm Up 2019
73 Temple Win 13-6 8.34 3.71% Feb 8th Florida Warm Up 2019
48 Kennesaw State Loss 9-11 -17.97 3.71% Feb 9th Florida Warm Up 2019
7 Carleton College-CUT Win 15-13 18.04 3.71% Feb 9th Florida Warm Up 2019
13 Wisconsin Loss 9-10 0.46 3.71% Feb 9th Florida Warm Up 2019
12 Texas Loss 9-11 -3.98 3.71% Feb 9th Florida Warm Up 2019
17 Minnesota Win 15-8 25.09 3.71% Feb 10th Florida Warm Up 2019
27 LSU Loss 11-12 -8.14 3.71% Feb 10th Florida Warm Up 2019
25 South Carolina Loss 12-13 -9.92 4.67% Mar 9th Classic City Invite 2019
55 Florida State Win 13-8 11.94 4.67% Mar 9th Classic City Invite 2019
11 North Carolina State Loss 10-13 -8.07 4.67% Mar 9th Classic City Invite 2019
61 Tennessee Win 13-9 5.32 4.67% Mar 9th Classic City Invite 2019
48 Kennesaw State Win 9-8 -4.28 4.42% Mar 10th Classic City Invite 2019
28 Northeastern Win 10-9 1.8 4.67% Mar 10th Classic City Invite 2019
4 Pittsburgh Loss 9-13 -5.75 5.55% Mar 30th Easterns 2019 Men
49 Northwestern Loss 12-13 -20.67 5.55% Mar 30th Easterns 2019 Men
26 North Carolina-Wilmington Win 13-11 8.57 5.55% Mar 30th Easterns 2019 Men
32 William & Mary Win 13-9 17.7 5.55% Mar 30th Easterns 2019 Men
9 Massachusetts Loss 9-11 -2.81 5.55% Mar 31st Easterns 2019 Men
3 Oregon Loss 12-15 1.43 5.55% Mar 31st Easterns 2019 Men
11 North Carolina State Loss 10-12 -4.39 5.55% 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.