() #21 Cal Poly-SLO (17-8)

1943.59 (46)

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# Opponent Result Effect % of Ranking Status Date Event
42 Chicago Loss 7-13 -38.5 3.95% Jan 26th Santa Barbara Invite 2019
19 UCLA Win 14-12 10 3.95% Jan 26th Santa Barbara Invite 2019
13 Stanford Win 13-9 21.81 3.95% Jan 26th Santa Barbara Invite 2019
15 Wisconsin Loss 8-13 -17.17 3.95% Jan 27th Santa Barbara Invite 2019
23 California Win 10-7 14.12 3.73% Jan 27th Santa Barbara Invite 2019
19 UCLA Win 15-14 6.05 3.95% Jan 27th Santa Barbara Invite 2019
158 Claremont** Win 13-1 0 0% Ignored Feb 9th Stanford Open 2019
234 Nevada-Reno** Win 13-1 0 0% Ignored Feb 9th Stanford Open 2019
68 Lewis & Clark Win 11-10 -22.65 4.43% Feb 9th Stanford Open 2019
54 Puget Sound Win 8-7 -13.38 3.94% Feb 10th Stanford Open 2019
55 Portland Win 6-4 -2.99 3.22% Feb 10th Stanford Open 2019
30 Utah Win 9-7 4.26 4.31% Feb 16th Presidents Day Invite 2019
13 Stanford Win 8-5 22.86 3.88% Feb 16th Presidents Day Invite 2019
14 Colorado Win 9-6 22.72 4.17% Feb 17th Presidents Day Invite 2019
34 Colorado College Win 11-4 16.22 4.31% Feb 17th Presidents Day Invite 2019
13 Stanford Win 7-6 9.58 3.88% Feb 17th Presidents Day Invite 2019
2 California-San Diego Loss 7-12 -2.22 4.7% Feb 18th Presidents Day Invite 2019
17 Vermont Win 9-8 9.04 4.44% Feb 18th Presidents Day Invite 2019
9 Texas Win 10-9 18.1 5.27% Mar 2nd Stanford Invite 2019
6 British Columbia Loss 8-10 1.38 5.13% Mar 2nd Stanford Invite 2019
23 California Loss 4-8 -25.81 4.19% Mar 2nd Stanford Invite 2019
38 Florida Win 12-6 13.35 5.13% Mar 2nd Stanford Invite 2019
4 California-Santa Barbara Loss 8-13 -8.86 5.27% Mar 3rd Stanford Invite 2019
13 Stanford Loss 6-11 -22.81 4.99% Mar 3rd Stanford Invite 2019
23 California Loss 9-11 -15.29 5.27% Mar 3rd Stanford Invite 2019
**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.