() #228 Mixed Duluth (3-17)

328.27 (3)

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# Opponent Result Effect % of Ranking Status Date Event
184 Mousetrap Loss 6-11 -10.26 4.52% Jul 14th MN Ultimate Disc Invitational 2018
130 Impact Loss 5-11 1.86 4.38% Jul 14th MN Ultimate Disc Invitational 2018
233 ALTimate Brews Win 10-8 10.86 4.65% Jul 14th MN Ultimate Disc Invitational 2018
72 Bird** Loss 3-11 0 0% Ignored Jul 14th MN Ultimate Disc Invitational 2018
145 Pandamonium Loss 9-10 21.67 4.77% Jul 14th MN Ultimate Disc Invitational 2018
185 Boomtown Pandas Loss 8-9 9.33 4.52% Jul 15th MN Ultimate Disc Invitational 2018
166 Spirit Fowl Loss 7-11 -0.36 4.65% Jul 15th MN Ultimate Disc Invitational 2018
- MN Superior Loss 7-12 -9.6 4.77% Jul 15th MN Ultimate Disc Invitational 2018
184 Mousetrap Loss 9-13 -5.9 6.23% Aug 18th Cooler Classic 30
191 Coalition Ultimate Loss 10-11 11.06 6.23% Aug 18th Cooler Classic 30
137 ELevate** Loss 3-13 0 0% Ignored Aug 18th Cooler Classic 30
233 ALTimate Brews Win 15-9 31.62 6.23% Aug 19th Cooler Classic 30
191 Coalition Ultimate Loss 7-13 -17.69 6.23% Aug 19th Cooler Classic 30
217 Mastodon Loss 8-15 -27.26 6.23% Aug 19th Cooler Classic 30
112 Mojo Jojo** Loss 4-13 0 0% Ignored Sep 8th Northwest Plains Mixed Sectional Championship 2018
191 Coalition Ultimate Loss 8-13 -16.16 7.31% Sep 8th Northwest Plains Mixed Sectional Championship 2018
217 Mastodon Win 11-10 22.08 7.31% Sep 8th Northwest Plains Mixed Sectional Championship 2018
166 Spirit Fowl Loss 7-12 -4.82 7.31% Sep 8th Northwest Plains Mixed Sectional Championship 2018
185 Boomtown Pandas Loss 2-13 -21.92 7.31% Sep 9th Northwest Plains Mixed Sectional Championship 2018
191 Coalition Ultimate Loss 11-13 4.94 7.31% Sep 9th Northwest Plains Mixed Sectional Championship 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.