() #150 Truman State (5-13)

567.5 (44)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
64 Iowa Loss 3-10 -1.49 29 5.37% Counts (Why) Mar 5th Midwest Throwdown 2022
106 Michigan Tech Loss 4-6 -3.84 7 4.46% Counts Mar 5th Midwest Throwdown 2022
33 Minnesota** Loss 0-11 0 28 0% Ignored (Why) Mar 5th Midwest Throwdown 2022
162 Oklahoma Win 8-4 24.55 34 4.89% Counts (Why) Mar 5th Midwest Throwdown 2022
121 Saint Louis Win 7-5 26.28 23 4.89% Counts Mar 6th Midwest Throwdown 2022
35 Colorado State** Loss 0-13 0 47 0% Ignored (Why) Mar 6th Midwest Throwdown 2022
71 Northwestern Loss 6-10 1.37 32 5.64% Counts Mar 6th Midwest Throwdown 2022
38 Wisconsin** Loss 2-13 0 18 0% Ignored (Why) Mar 6th Midwest Throwdown 2022
33 Minnesota Loss 5-6 42.92 28 5.57% Counts Mar 26th Old Capital Open 2022
130 Nebraska Win 6-5 14.14 20 5.57% Counts Mar 26th Old Capital Open 2022
68 Kansas Loss 4-5 22.77 30 5.03% Counts Mar 27th Old Capital Open 2022
121 Saint Louis Loss 4-9 -26.85 23 6.05% Counts (Why) Mar 27th Old Capital Open 2022
200 Wisconsin-B Win 7-3 14.02 16 5.31% Counts (Why) Mar 27th Old Capital Open 2022
174 Colorado School of Mines Win 10-9 -5.26 66 9.76% Counts Apr 30th South Central D III College Womens Regionals 2022
98 Rice Loss 1-15 -25.78 95 9.76% Counts (Why) Apr 30th South Central D III College Womens Regionals 2022
132 Trinity Loss 6-10 -39.83 51 8.96% Counts Apr 30th South Central D III College Womens Regionals 2022
174 Colorado School of Mines Loss 7-9 -44.58 66 8.96% Counts May 1st South Central D III College Womens Regionals 2022
132 Trinity Loss 11-12 -3.63 51 9.76% Counts May 1st South Central D III College Womens Regionals 2022
**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.