() #24 California-Davis (8-12)

1971.09 (14)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
23 Cal Poly-SLO Win 10-7 23.2 6 5.62% Counts Jan 27th Santa Barbara Invite 2024
3 Carleton College** Loss 6-15 0 17 0% Ignored (Why) Jan 27th Santa Barbara Invite 2024
10 Washington Loss 6-10 -6.83 21 5.45% Counts Jan 27th Santa Barbara Invite 2024
27 Utah Win 12-6 32.51 10 5.78% Counts (Why) Jan 27th Santa Barbara Invite 2024
6 Stanford Loss 6-13 -0.79 23 5.94% Counts (Why) Jan 28th Santa Barbara Invite 2024
18 Victoria Loss 8-9 1.5 32 5.62% Counts Jan 28th Santa Barbara Invite 2024
14 California-Santa Cruz Loss 7-10 -10.74 21 5.62% Counts Jan 28th Santa Barbara Invite 2024
113 Denver** Win 14-2 0 18 0% Ignored (Why) Feb 17th Presidents Day Invite 2024
5 Oregon** Loss 4-12 0 28 0% Ignored (Why) Feb 17th Presidents Day Invite 2024
15 California-San Diego Loss 9-12 -11.69 18 7.06% Counts Feb 17th Presidents Day Invite 2024
55 Southern California Win 11-8 -3.75 12 7.06% Counts Feb 18th Presidents Day Invite 2024
32 UCLA Win 9-7 11.09 2 6.48% Counts Feb 18th Presidents Day Invite 2024
69 California-San Diego-B Win 11-6 -0.14 41 6.68% Counts (Why) Feb 18th Presidents Day Invite 2024
23 Cal Poly-SLO Win 9-2 37.22 6 5.84% Counts (Why) Feb 19th Presidents Day Invite 2024
32 UCLA Loss 6-9 -35.99 2 6.27% Counts Feb 19th Presidents Day Invite 2024
9 California-Santa Barbara Loss 7-10 4.95 18 7.5% Counts Mar 2nd Stanford Invite 2024
2 Vermont** Loss 3-12 0 50 0% Ignored (Why) Mar 2nd Stanford Invite 2024
25 Pittsburgh Loss 5-9 -39.21 9 6.8% Counts Mar 2nd Stanford Invite 2024
18 Victoria Loss 3-9 -31.55 32 6.55% Counts (Why) Mar 3rd Stanford Invite 2024
32 UCLA Win 7-3 29.33 2 5.75% Counts (Why) Mar 3rd Stanford Invite 2024
**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.