(1) #27 LSU (17-11)

1777.74 (1)

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# Opponent Result Effect % of Ranking Status Date Event
36 Alabama Win 11-10 2.3 3.17% Jan 26th T Town Throwdown
72 Alabama-Huntsville Win 13-5 10.01 3.17% Jan 26th T Town Throwdown
132 Kentucky Win 10-7 -4.23 2.99% Jan 26th T Town Throwdown
37 Illinois Win 13-9 11.81 3.17% Jan 26th T Town Throwdown
48 Kennesaw State Win 15-14 -0.2 3.17% Jan 27th T Town Throwdown
24 Auburn Win 11-10 4.71 3.17% Jan 27th T Town Throwdown
160 Vanderbilt** Win 15-4 0 0% Ignored Jan 27th T Town Throwdown
6 Brigham Young Loss 9-13 -2.27 3.55% Feb 8th Florida Warm Up 2019
28 Northeastern Loss 8-9 -4.41 3.36% Feb 8th Florida Warm Up 2019
43 Harvard Loss 10-11 -8.49 3.55% Feb 8th Florida Warm Up 2019
25 South Carolina Win 15-10 17.04 3.55% Feb 9th Florida Warm Up 2019
12 Texas Loss 7-10 -5.48 3.36% Feb 9th Florida Warm Up 2019
13 Wisconsin Win 11-8 21.69 3.55% Feb 9th Florida Warm Up 2019
22 Georgia Loss 8-11 -11.38 3.55% Feb 9th Florida Warm Up 2019
43 Harvard Win 15-10 12.83 3.55% Feb 10th Florida Warm Up 2019
20 Tufts Win 12-11 7.79 3.55% Feb 10th Florida Warm Up 2019
36 Alabama Loss 10-13 -16.89 4.23% Mar 2nd Mardi Gras XXXII
65 Florida Win 13-10 3.8 4.23% Mar 2nd Mardi Gras XXXII
227 Florida State-B Win 13-6 -11.58 4.23% Mar 2nd Mardi Gras XXXII
82 Texas State Loss 11-13 -24.88 4.23% Mar 2nd Mardi Gras XXXII
159 Mississippi State** Win 13-2 0 0% Ignored Mar 3rd Mardi Gras XXXII
103 Georgia State Loss 10-11 -24.46 4.23% Mar 3rd Mardi Gras XXXII
19 Colorado State Win 12-11 12.29 4.74% Mar 16th Centex 2019 Men
8 Colorado Loss 8-13 -8.89 4.74% Mar 16th Centex 2019 Men
29 Texas-Dallas Win 11-10 5.93 4.74% Mar 16th Centex 2019 Men
67 Oklahoma State Win 15-11 6.84 4.74% Mar 17th Centex 2019 Men
13 Wisconsin Loss 13-15 0.45 4.74% Mar 17th Centex 2019 Men
12 Texas Loss 12-13 5.34 4.74% Mar 17th Centex 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.