(14) #106 Michigan State (9-8)

1150.16 (23)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
- Harding Win 13-6 6.04 0 6.09% Counts (Why) Mar 19th Missouri Loves Company MLC
229 Wisconsin- La Crosse Win 13-9 -2.99 21 6.09% Counts Mar 19th Missouri Loves Company MLC
82 Missouri Loss 8-12 -21.05 23 6.09% Counts Mar 19th Missouri Loves Company MLC
51 Indiana Loss 9-15 -12.54 9 6.09% Counts Mar 20th Missouri Loves Company MLC
108 Vanderbilt Loss 10-15 -30.05 22 6.09% Counts Mar 20th Missouri Loves Company MLC
44 Cincinnati Loss 3-11 -13.57 6 5.59% Counts (Why) Mar 20th Missouri Loves Company MLC
12 Michigan** Loss 3-13 0 21 0% Ignored (Why) Apr 9th Michigan D I College Mens CC 2022
122 Grand Valley State Win 11-10 6 59 7.24% Counts Apr 9th Michigan D I College Mens CC 2022
198 Western Michigan Win 10-6 9.42 72 6.65% Counts (Why) Apr 9th Michigan D I College Mens CC 2022
283 Eastern Michigan** Win 13-5 0 55 0% Ignored (Why) Apr 9th Michigan D I College Mens CC 2022
283 Eastern Michigan** Win 15-4 0 55 0% Ignored (Why) Apr 10th Michigan D I College Mens CC 2022
122 Grand Valley State Loss 14-15 -13.52 59 7.24% Counts Apr 10th Michigan D I College Mens CC 2022
60 Notre Dame Win 13-12 37.99 25 8.61% Counts Apr 30th Great Lakes D I College Mens Regionals 2022
51 Indiana Loss 4-15 -26.19 9 8.61% Counts (Why) Apr 30th Great Lakes D I College Mens Regionals 2022
70 Chicago Loss 11-14 -11.07 18 8.61% Counts Apr 30th Great Lakes D I College Mens Regionals 2022
191 Illinois State Win 15-7 24.43 25 8.61% Counts (Why) May 1st Great Lakes D I College Mens Regionals 2022
93 Kentucky Win 11-7 49.01 35 8.38% Counts May 1st Great Lakes D I College Mens Regionals 2022
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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.