(5) #35 Cal Poly-SLO (11-9)

2036.55 (415)

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# Opponent Result Effect % of Ranking Status Date Event
2 California-San Diego** Loss 5-13 0 0% Ignored Jan 27th Santa Barbara Invitational 2018
18 Brigham Young Loss 10-13 -4.88 5.87% Jan 27th Santa Barbara Invitational 2018
43 Southern California Win 12-10 11.97 5.87% Jan 27th Santa Barbara Invitational 2018
28 Washington University Win 13-11 19.1 5.87% Jan 27th Santa Barbara Invitational 2018
17 California-Santa Barbara Loss 12-13 9.9 5.87% Jan 28th Santa Barbara Invitational 2018
26 California Loss 12-13 -1.77 5.87% Jan 28th Santa Barbara Invitational 2018
44 Colorado State Win 13-8 27.53 5.87% Jan 28th Santa Barbara Invitational 2018
164 UCLA-B** Win 13-1 0 0% Ignored Feb 3rd 2018 Presidents Day Qualifying Tournament
100 Arizona State Win 13-3 4.62 6.22% Feb 3rd 2018 Presidents Day Qualifying Tournament
242 California-Davis-B** Win 13-2 0 0% Ignored Feb 3rd 2018 Presidents Day Qualifying Tournament
189 Sonoma State** Win 13-3 0 0% Ignored Feb 3rd 2018 Presidents Day Qualifying Tournament
120 California-San Diego-B** Win 13-2 0 0% Ignored Feb 4th 2018 Presidents Day Qualifying Tournament
17 California-Santa Barbara Loss 10-13 -2.95 6.22% Feb 4th 2018 Presidents Day Qualifying Tournament
63 Arizona Win 9-7 1.76 5.71% Feb 4th 2018 Presidents Day Qualifying Tournament
2 California-San Diego** Loss 5-12 0 0% Ignored Mar 24th NW Challenge 2018
17 California-Santa Barbara Loss 5-15 -32.52 9.32% Mar 24th NW Challenge 2018
36 Colorado College Win 13-12 12.5 9.32% Mar 24th NW Challenge 2018
38 Victoria Loss 8-11 -39.27 9.32% Mar 24th NW Challenge 2018
69 Boston College Win 15-7 32.26 9.32% Mar 25th NW Challenge 2018
20 Washington Loss 6-15 -40.74 9.32% Mar 25th NW Challenge 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.