() #38 Florida (11-16)

1611.11 (24)

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# Opponent Result Effect % of Ranking Status Date Event
3 Ohio State Loss 8-10 17.6 3.41% Jan 19th Florida Winter Classic 2019
8 Dartmouth Loss 4-12 -1.82 3.36% Jan 19th Florida Winter Classic 2019
112 Central Florida Win 10-5 0.55 3.11% Jan 19th Florida Winter Classic 2019
43 Georgia Tech Win 8-7 2.23 3.11% Jan 19th Florida Winter Classic 2019
3 Ohio State** Loss 4-10 0 0% Ignored Jan 20th Florida Winter Classic 2019
20 North Carolina-Wilmington Win 12-11 17.19 3.5% Jan 20th Florida Winter Classic 2019
26 Georgia Loss 8-14 -10.84 3.5% Jan 20th Florida Winter Classic 2019
25 Clemson Win 9-7 21.46 3.82% Feb 9th Queen City Tune Up 2019 Women
41 Harvard Win 8-2 18.62 3.24% Feb 9th Queen City Tune Up 2019 Women
58 Penn State Loss 7-9 -17.45 3.82% Feb 9th Queen City Tune Up 2019 Women
11 Pittsburgh Loss 6-9 2.06 3.7% Feb 9th Queen City Tune Up 2019 Women
3 Ohio State** Loss 4-15 0 0% Ignored Feb 10th Queen City Tune Up 2019 Women
5 Carleton College-Syzygy** Loss 6-15 0 0% Ignored Feb 10th Queen City Tune Up 2019 Women
26 Georgia Win 11-6 32.12 3.94% Feb 10th Queen City Tune Up 2019 Women
32 Brigham Young Loss 7-11 -18.6 4.82% Mar 2nd Stanford Invite 2019
6 British Columbia** Loss 0-13 0 0% Ignored Mar 2nd Stanford Invite 2019
21 Cal Poly-SLO Loss 6-12 -12.49 4.82% Mar 2nd Stanford Invite 2019
23 California Loss 9-11 3 4.95% Mar 2nd Stanford Invite 2019
39 California-Davis Win 8-7 4.93 4.4% Mar 3rd Stanford Invite 2019
50 Whitman Win 9-5 18.94 4.25% Mar 3rd Stanford Invite 2019
69 Notre Dame Loss 9-10 -23.95 5.56% Mar 16th Tally Classic XIV
115 South Florida Win 11-3 2.12 5.1% Mar 16th Tally Classic XIV
41 Harvard Win 12-10 11.45 5.56% Mar 16th Tally Classic XIV
51 Florida State Loss 10-11 -13.4 5.56% Mar 16th Tally Classic XIV
69 Notre Dame Loss 8-11 -38.11 5.56% Mar 17th Tally Classic XIV
25 Clemson Loss 3-14 -19.93 5.56% Mar 17th Tally Classic XIV
93 Kennesaw State Win 11-7 2.5 5.41% Mar 17th Tally Classic XIV
**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.