() #100 California-Santa Cruz (11-11)

1358.77 (2)

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# Opponent Result Effect % of Ranking Status Date Event
5 Cal Poly-SLO Loss 6-13 9.36 4.8% Jan 26th Santa Barbara Invite 2019
51 Western Washington Loss 11-13 2.12 4.8% Jan 26th Santa Barbara Invite 2019
40 Dartmouth Loss 7-13 -11.58 4.8% Jan 26th Santa Barbara Invite 2019
29 Texas-Dallas Loss 9-13 -0.27 4.8% Jan 26th Santa Barbara Invite 2019
219 Michigan State Win 13-11 -10.43 4.8% Jan 27th Santa Barbara Invite 2019
265 Cal State-Long Beach Win 13-1 2.24 5.08% Feb 2nd Presidents Day Qualifiers Men
344 California-Irvine Win 10-5 -13.17 4.52% Feb 2nd Presidents Day Qualifiers Men
395 California-San Diego-C** Win 11-0 0 0% Ignored Feb 2nd Presidents Day Qualifiers Men
261 Cal Poly-SLO-B Win 13-8 -2.22 5.08% Feb 3rd Presidents Day Qualifiers Men
169 Chico State Win 11-8 4.88 5.08% Feb 3rd Presidents Day Qualifiers Men
244 Colorado-B Win 13-10 -8.21 5.08% Feb 3rd Presidents Day Qualifiers Men
74 Arizona Win 13-10 25.52 5.38% Feb 9th Stanford Open 2019
41 Las Positas Loss 11-13 5.17 5.38% Feb 9th Stanford Open 2019
99 Lewis & Clark Win 11-9 14.18 5.38% Feb 9th Stanford Open 2019
241 Washington-B Win 11-5 6.74 4.94% Feb 9th Stanford Open 2019
93 California-Davis Loss 4-5 -4.09 3.71% Feb 10th Stanford Open 2019
3 Oregon** Loss 3-13 0 0% Ignored Feb 16th Presidents Day Invite 2019
51 Western Washington Loss 6-8 -1.52 4.9% Feb 16th Presidents Day Invite 2019
34 UCLA Loss 1-12 -13.32 5.47% Feb 17th Presidents Day Invite 2019
76 Utah Loss 7-8 -0.54 5.07% Feb 17th Presidents Day Invite 2019
271 San Diego State Win 11-5 1.22 5.23% Feb 18th Presidents Day Invite 2019
90 Santa Clara Loss 9-10 -5.86 5.7% Feb 18th Presidents Day 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.