(5) #90 Chicago (13-12)

1433.78 (6)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
36 Penn State Loss 9-14 -5.25 9 3.88% Counts Feb 11th Queen City Tune Up1
20 North Carolina State Loss 5-15 -3.57 3 3.88% Counts (Why) Feb 11th Queen City Tune Up1
25 North Carolina-Wilmington Loss 7-12 -2.83 24 3.88% Counts Feb 11th Queen City Tune Up1
22 Washington University Loss 11-15 3.65 53 3.88% Counts Feb 11th Queen City Tune Up1
134 Carnegie Mellon Win 11-9 2.09 18 3.88% Counts Feb 12th Queen City Tune Up1
62 Harvard Loss 11-12 0.41 22 3.88% Counts Feb 12th Queen City Tune Up1
318 Carleton College-Karls-C** Win 13-5 0 174 0% Ignored (Why) Mar 4th Midwest Throwdown 2023
64 St. Olaf Loss 9-11 -5.56 28 4.61% Counts Mar 4th Midwest Throwdown 2023
118 Marquette Loss 10-11 -12.48 16 4.61% Counts Mar 4th Midwest Throwdown 2023
313 Illinois-B** Win 10-2 0 332 0% Ignored (Why) Mar 5th Midwest Throwdown 2023
279 Wisconsin-Platteville** Win 13-3 0 189 0% Ignored (Why) Mar 5th Midwest Throwdown 2023
207 Illinois State Win 12-6 2.65 10 4.49% Counts (Why) Mar 5th Midwest Throwdown 2023
209 Alabama-Birmingham Win 11-6 0.63 9 4.62% Counts (Why) Mar 11th Tally Classic XVII
110 Clemson Win 10-6 18.06 3 4.49% Counts (Why) Mar 11th Tally Classic XVII
62 Harvard Win 9-8 12.61 22 4.62% Counts Mar 11th Tally Classic XVII
268 Georgia Southern Win 11-8 -21.42 9 4.89% Counts Mar 11th Tally Classic XVII
110 Clemson Win 10-9 0.69 3 4.89% Counts Mar 12th Tally Classic XVII
49 Notre Dame Loss 12-13 4.34 27 4.89% Counts Mar 12th Tally Classic XVII
104 Florida State Loss 4-5 -8.91 17 4% Counts Apr 1st Huck Finn1
68 Wisconsin-Milwaukee Loss 6-7 -0.45 2 4.81% Counts Apr 1st Huck Finn1
75 Grinnell Loss 4-5 -3 39 4% Counts Apr 1st Huck Finn1
116 John Brown Win 10-5 24.29 2 5.16% Counts (Why) Apr 1st Huck Finn1
112 Illinois Win 10-8 8.69 10 5.66% Counts Apr 2nd Huck Finn1
108 Vanderbilt Win 11-10 1.16 54 5.81% Counts Apr 2nd Huck Finn1
65 Indiana Loss 6-9 -15.6 98 5.16% Counts Apr 2nd Huck Finn1
**Blowout Eligible. Learn more about how this works here.

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.