(4) #65 California-Santa Barbara (17-25)

1387.14 (10)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
4 Brigham Young** Loss 5-15 0 5 0% Ignored (Why) Jan 28th Santa Barbara Invite 2022
174 Kansas Win 9-7 -3.47 40 1.6% Counts Jan 29th Santa Barbara Invite 2022
7 Cal Poly-SLO Loss 7-13 0.56 5 1.74% Counts Jan 29th Santa Barbara Invite 2022
49 Utah Loss 7-11 -6.22 10 1.69% Counts Jan 29th Santa Barbara Invite 2022
41 California-Santa Cruz Loss 11-12 0.44 41 1.74% Counts Jan 30th Santa Barbara Invite 2022
70 Chicago Loss 8-9 -2.78 18 1.65% Counts Jan 30th Santa Barbara Invite 2022
30 Washington University Win 11-8 10.42 32 1.74% Counts Jan 30th Santa Barbara Invite 2022
226 San Diego State Win 9-5 -2.97 42 1.78% Counts (Why) Feb 19th Presidents Day Invite 2022
7 Cal Poly-SLO Loss 7-14 0.14 5 2.07% Counts Feb 19th Presidents Day Invite 2022
14 California Loss 9-13 0.52 41 2.07% Counts Feb 19th Presidents Day Invite 2022
49 Utah Win 9-7 7.46 10 1.9% Counts Feb 19th Presidents Day Invite 2022
61 Stanford Win 9-8 3.26 81 1.96% Counts Feb 20th Presidents Day Invite 2022
3 Colorado** Loss 3-15 0 25 0% Ignored (Why) Feb 20th Presidents Day Invite 2022
62 California-San Diego Win 12-7 11.49 76 2.07% Counts (Why) Feb 20th Presidents Day Invite 2022
75 Southern California Loss 5-8 -9.29 111 1.71% Counts Feb 21st Presidents Day Invite 2022
41 California-Santa Cruz Loss 7-8 0.47 41 1.84% Counts Feb 21st Presidents Day Invite 2022
20 Oregon Loss 11-12 4.7 54 2.07% Counts Feb 21st Presidents Day Invite 2022
75 Southern California Loss 10-13 -9.7 111 2.32% Counts Mar 5th Stanford Invite 2022
10 Washington Loss 8-13 0.72 113 2.32% Counts Mar 5th Stanford Invite 2022
58 Grand Canyon Loss 6-7 -1.48 12 1.92% Counts Mar 5th Stanford Invite 2022
61 Stanford Loss 8-13 -10.9 81 2.32% Counts Mar 6th Stanford Invite 2022
84 Santa Clara Win 10-9 -0.12 4 2.32% Counts Mar 6th Stanford Invite 2022
69 Texas-Dallas Win 10-8 5.42 29 2.26% Counts Mar 6th Stanford Invite 2022
58 Grand Canyon Loss 7-10 -7.65 12 2.2% Counts Mar 6th Stanford Invite 2022
21 McGill Loss 8-12 -2.98 39 2.61% Counts Mar 19th Mens College Centex
89 Central Florida Loss 9-10 -7.59 23 2.61% Counts Mar 19th Mens College Centex
34 Oklahoma Christian Win 12-11 8.13 37 2.61% Counts Mar 19th Mens College Centex
100 Iowa State Win 12-11 -1.97 38 2.61% Counts Mar 19th Mens College Centex
34 Oklahoma Christian Loss 5-11 -10.33 37 2.39% Counts (Why) Mar 20th Mens College Centex
16 Texas Loss 5-14 -4.94 29 2.61% Counts (Why) Mar 20th Mens College Centex
47 Florida Win 11-5 17.54 38 2.39% Counts (Why) Mar 20th Mens College Centex
165 Loyola Marymount Win 11-7 -0.27 32 3.02% Counts Apr 9th SoCal D I College Mens CC 2022
7 Cal Poly-SLO Loss 4-13 -0.34 5 3.1% Counts (Why) Apr 9th SoCal D I College Mens CC 2022
246 California-Irvine Win 13-6 -4.78 35 3.1% Counts (Why) Apr 9th SoCal D I College Mens CC 2022
62 California-San Diego Loss 11-13 -6.58 76 3.1% Counts Apr 10th SoCal D I College Mens CC 2022
31 UCLA Loss 9-10 3.04 58 3.1% Counts Apr 10th SoCal D I College Mens CC 2022
75 Southern California Win 10-9 1.84 111 3.91% Counts May 7th Southwest D I College Mens Regionals 2022
61 Stanford Win 13-12 6.63 81 3.91% Counts May 7th Southwest D I College Mens Regionals 2022
7 Cal Poly-SLO Loss 6-13 -0.44 5 3.91% Counts (Why) May 7th Southwest D I College Mens Regionals 2022
115 California-Davis Win 13-7 11.83 69 3.91% Counts (Why) May 7th Southwest D I College Mens Regionals 2022
84 Santa Clara Win 11-9 4.85 4 3.91% Counts May 8th Southwest D I College Mens Regionals 2022
31 UCLA Loss 10-13 -4.4 58 3.91% Counts May 8th Southwest D I College Mens Regionals 2022
**Blowout Eligible. Learn more about how this works here.

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.