(7) #246 Florida-B (12-10)

875.42 (57)

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# Opponent Result Effect % of Ranking Status Date Event
295 Embry-Riddle (Florida) Win 13-5 18.92 4.31% Feb 8th Florida Warm Up 2019
415 Florida Tech-B** Win 13-5 0 0% Ignored Feb 8th Florida Warm Up 2019
255 Boston College-B Win 13-6 25.06 4.31% Feb 8th Florida Warm Up 2019
227 Florida State-B Loss 7-11 -18.67 4.19% Feb 9th Florida Warm Up 2019
366 Central Florida-B Win 13-7 3.7 4.31% Feb 9th Florida Warm Up 2019
355 Northwestern-B Win 11-5 7.55 3.95% Feb 9th Florida Warm Up 2019
227 Florida State-B Win 15-7 28.79 4.31% Feb 10th Florida Warm Up 2019
207 North Florida Loss 8-11 -12.4 4.31% Feb 10th Florida Warm Up 2019
295 Embry-Riddle (Florida) Win 13-6 25.64 5.75% Mar 16th Tally Classic XIV
415 Florida Tech-B** Win 13-1 0 0% Ignored Mar 16th Tally Classic XIV
366 Central Florida-B Win 13-8 1.27 5.75% Mar 16th Tally Classic XIV
263 Georgia Tech-B Loss 11-13 -17.7 5.75% Mar 16th Tally Classic XIV
377 Stetson Win 14-7 3.71 5.75% Mar 17th Tally Classic XIV
227 Florida State-B Loss 11-14 -16.67 5.75% Mar 17th Tally Classic XIV
221 North Georgia Loss 12-15 -15.5 5.75% Mar 17th Tally Classic XIV
35 Middlebury** Loss 3-13 0 0% Ignored Mar 23rd College Southerns XVIII
234 Florida Tech Loss 9-11 -14.16 6.09% Mar 23rd College Southerns XVIII
78 Carleton College-GoP Loss 6-13 -1.15 6.09% Mar 23rd College Southerns XVIII
240 Wisconsin-Eau Claire Loss 10-11 -7.17 6.09% Mar 23rd College Southerns XVIII
257 Charleston Loss 8-15 -39.54 6.09% Mar 24th College Southerns XVIII
321 Carleton Hot Karls Win 9-6 7.59 5.41% Mar 24th College Southerns XVIII
207 North Florida Win 15-13 19.73 6.09% Mar 24th College Southerns XVIII
**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.