() #36 Alabama (16-10)

1723.14 (4)

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# Opponent Result Effect % of Ranking Status Date Event
24 Auburn Loss 8-9 -1.74 3.27% Jan 26th T Town Throwdown
48 Kennesaw State Win 10-8 6.49 3.37% Jan 26th T Town Throwdown
106 Illinois State Win 13-8 3.6 3.46% Jan 26th T Town Throwdown
27 LSU Loss 10-11 -2.52 3.46% Jan 26th T Town Throwdown
48 Kennesaw State Loss 12-14 -10.67 3.46% Jan 27th T Town Throwdown
160 Vanderbilt Win 14-8 -2.25 3.46% Jan 27th T Town Throwdown
72 Alabama-Huntsville Win 15-10 7.69 3.46% Jan 27th T Town Throwdown
52 Notre Dame Win 9-7 6.76 3.57% Feb 9th Queen City Tune Up 2019 Men
108 North Carolina-Charlotte Win 12-7 4.95 3.89% Feb 9th Queen City Tune Up 2019 Men
57 Carnegie Mellon Win 10-6 13.32 3.57% Feb 9th Queen City Tune Up 2019 Men
11 North Carolina State Loss 7-11 -6.38 3.78% Feb 9th Queen City Tune Up 2019 Men
1 North Carolina Loss 7-15 -3.69 3.89% Feb 10th Queen City Tune Up 2019 Men
47 Maryland Win 15-12 9.45 3.89% Feb 10th Queen City Tune Up 2019 Men
14 Ohio State Loss 11-13 1.62 3.89% Feb 10th Queen City Tune Up 2019 Men
322 Mississippi** Win 13-2 0 0% Ignored Mar 2nd Mardi Gras XXXII
112 Wisconsin-Whitewater Win 13-6 8.87 4.62% Mar 2nd Mardi Gras XXXII
23 Texas Tech Win 13-8 29.27 4.62% Mar 2nd Mardi Gras XXXII
27 LSU Win 13-10 18.54 4.62% Mar 2nd Mardi Gras XXXII
185 Alabama-Birmingham** Win 13-4 0 0% Ignored Mar 3rd Mardi Gras XXXII
82 Texas State Win 12-8 7.78 4.62% Mar 3rd Mardi Gras XXXII
159 Mississippi State Win 13-5 0.15 5.19% Mar 16th Tally Classic XIV
68 Cincinnati Loss 10-13 -29.31 5.19% Mar 16th Tally Classic XIV
61 Tennessee Win 12-10 3.78 5.19% Mar 16th Tally Classic XIV
15 Central Florida Loss 10-15 -10.2 5.19% Mar 16th Tally Classic XIV
79 Tulane Loss 10-12 -27.61 5.19% Mar 17th Tally Classic XIV
43 Harvard Loss 9-14 -28.7 5.19% 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.