(5) #232 Messiah (10-10)

682.34 (30)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
215 Wooster Win 13-7 27.34 3 4.42% Counts (Why) Mar 5th FCS D III Tune Up
212 Kenyon Win 13-10 17.15 46 4.42% Counts Mar 5th FCS D III Tune Up
186 Davidson Loss 10-12 -3.44 42 4.42% Counts Mar 5th FCS D III Tune Up
109 Christopher Newport Loss 7-13 -4.64 52 4.42% Counts Mar 5th FCS D III Tune Up
170 Rochester Loss 8-13 -12.79 10 4.42% Counts Mar 6th FCS D III Tune Up
85 Richmond Loss 8-13 3.2 27 4.42% Counts Mar 6th FCS D III Tune Up
259 Pittsburgh-B Win 14-11 10.64 56 5.26% Counts Mar 26th 2022 B team Brodown
135 Scranton Loss 11-14 2.27 51 5.26% Counts Mar 26th 2022 B team Brodown
364 Pennsylvania-B Win 11-8 -32.15 48 5.26% Counts Mar 26th 2022 B team Brodown
252 Penn State-B Win 12-6 27.94 93 5.12% Counts (Why) Mar 27th 2022 B team Brodown
135 Scranton Loss 8-9 12 51 4.98% Counts Mar 27th 2022 B team Brodown
233 Shippensburg Loss 10-11 -9.29 61 6.63% Counts Apr 23rd West Penn D III College Mens CC 2022
308 Dickinson Win 13-8 8.46 49 6.63% Counts Apr 23rd West Penn D III College Mens CC 2022
238 Grove City Win 11-10 7.39 46 6.63% Counts Apr 23rd West Penn D III College Mens CC 2022
238 Grove City Win 13-7 38.09 46 6.63% Counts (Why) Apr 23rd West Penn D III College Mens CC 2022
357 Lehigh-B** Win 15-5 0 38 0% Ignored (Why) Apr 30th Ohio Valley D III College Mens Regionals 2022
238 Grove City Loss 12-14 -18.27 46 7.02% Counts Apr 30th Ohio Valley D III College Mens Regionals 2022
324 Xavier Loss 12-13 -46.07 37 7.02% Counts Apr 30th Ohio Valley D III College Mens Regionals 2022
233 Shippensburg Loss 11-15 -29.23 61 7.02% Counts May 1st Ohio Valley D III College Mens Regionals 2022
357 Lehigh-B** Win 15-6 0 38 0% Ignored (Why) May 1st Ohio Valley D III 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.