(5) #183 Oberlin (8-11)

1041.96 (5)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
138 Missouri S&T Loss 8-13 -16.1 34 4.97% Counts Mar 2nd FCS D III Tune Up 2019
155 Elon Loss 11-13 -6.34 28 4.97% Counts Mar 2nd FCS D III Tune Up 2019
91 Mary Washington Loss 9-10 11.27 87 4.97% Counts Mar 2nd FCS D III Tune Up 2019
146 North Carolina-Asheville Loss 9-11 -5.38 23 4.97% Counts Mar 2nd FCS D III Tune Up 2019
223 Rensselaer Polytech Loss 6-13 -37.92 24 4.97% Counts (Why) Mar 3rd FCS D III Tune Up 2019
85 Richmond Loss 7-12 -6.94 89 4.97% Counts Mar 3rd FCS D III Tune Up 2019
300 High Point Win 13-7 10.07 18 4.97% Counts (Why) Mar 3rd FCS D III Tune Up 2019
302 Rose-Hulman Win 7-5 -2.69 35 4.18% Counts Mar 9th D III Midwestern Invite 2019
84 Brandeis Loss 10-12 8.43 115 5.26% Counts Mar 9th D III Midwestern Invite 2019
177 Winona State Loss 5-6 -4.38 83 4% Counts Mar 9th D III Midwestern Invite 2019
186 Macalester Loss 6-10 -25.7 44 4.83% Counts Mar 10th D III Midwestern Invite 2019
331 Kenyon Win 13-6 7.41 38 5.91% Counts (Why) Mar 23rd CWRUL Memorial 2019
231 Knox Win 10-9 -0.4 53 5.91% Counts Mar 23rd CWRUL Memorial 2019
347 Wright State Win 13-6 3.09 1 5.91% Counts (Why) Mar 23rd CWRUL Memorial 2019
154 Syracuse Loss 7-13 -28.19 84 5.91% Counts Mar 23rd CWRUL Memorial 2019
171 RIT Win 11-7 30.91 45 5.75% Counts Mar 24th CWRUL Memorial 2019
38 Purdue Loss 8-15 6.3 12 5.91% Counts Mar 24th CWRUL Memorial 2019
158 Lehigh Win 13-11 19.84 18 5.91% Counts Mar 24th CWRUL Memorial 2019
145 Dayton Win 11-7 37.5 12 5.75% Counts Mar 24th CWRUL Memorial 2019
**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.