(3) #133 Case Western Reserve (7-15)

1175.77 (23)

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# Opponent Result Effect % of Ranking Status Date Event
30 Auburn Loss 5-11 -2.39 3.47% Feb 3rd Queen City Tune Up 2018 College Open
64 North Carolina-Charlotte Loss 9-11 1.48 3.78% Feb 3rd Queen City Tune Up 2018 College Open
16 North Carolina-Wilmington** Loss 4-11 0 0% Ignored Feb 3rd Queen City Tune Up 2018 College Open
91 Penn State Loss 8-10 -2.47 3.68% Feb 3rd Queen City Tune Up 2018 College Open
151 George Mason Win 11-4 19.47 3.47% Feb 3rd Queen City Tune Up 2018 College Open
40 Iowa Loss 8-13 -2.09 4.25% Feb 17th Easterns Qualifier 2018
12 North Carolina State Loss 6-13 6.35 4.25% Feb 17th Easterns Qualifier 2018
73 Michigan State Loss 9-13 -7.76 4.25% Feb 17th Easterns Qualifier 2018
23 Georgia Tech Loss 7-11 4.37 4.14% Feb 17th Easterns Qualifier 2018
78 Georgetown Loss 9-10 5.07 4.25% Feb 17th Easterns Qualifier 2018
149 Davidson Win 13-10 13.01 4.25% Feb 18th Easterns Qualifier 2018
84 Virginia Loss 6-14 -16.58 4.25% Feb 18th Easterns Qualifier 2018
224 Georgia Southern Win 14-7 11.91 4.25% Feb 18th Easterns Qualifier 2018
144 Dayton Loss 8-11 -23.32 5.67% Mar 24th CWRUL Memorial 2018
50 Notre Dame Loss 8-13 -7.97 5.67% Mar 24th CWRUL Memorial 2018
203 Rochester Loss 10-11 -22.25 5.67% Mar 24th CWRUL Memorial 2018
105 Wisconsin-Milwaukee Win 11-10 16.03 5.67% Mar 24th CWRUL Memorial 2018
190 Northern Iowa Win 11-9 2.96 5.67% Mar 25th CWRUL Memorial 2018
240 Tennessee Tech Win 15-4 12.84 5.67% Mar 25th CWRUL Memorial 2018
192 Cedarville Win 12-9 8.26 5.67% Mar 25th CWRUL Memorial 2018
45 Illinois State Loss 7-15 -12.12 6.01% Mar 31st Huck Finn 2018
74 Washington University Loss 10-13 -5.46 6.01% Mar 31st Huck Finn 2018
**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.