#9 California-Santa Cruz (21-6)

avg: 1895.46  •  sd: 72.07  •  top 16/20: 98.3%

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# Opponent Result Game Rating Status Date Event
115 California-Irvine** Win 13-2 1711.74 Ignored Jan 21st Presidents Day Qualifier
163 California-San Diego-B** Win 13-0 1519.48 Ignored Jan 21st Presidents Day Qualifier
229 California-Santa Barbara-B** Win 13-1 1192.58 Ignored Jan 21st Presidents Day Qualifier
100 California-Davis** Win 12-4 1794.58 Ignored Jan 22nd Presidents Day Qualifier
317 California-San Diego-C** Win 15-0 671.43 Ignored Jan 22nd Presidents Day Qualifier
124 Arizona State** Win 14-4 1664.85 Ignored Jan 28th Santa Barbara Invitational 2023
30 Utah State Win 13-11 1879.54 Jan 28th Santa Barbara Invitational 2023
64 California-San Diego Win 15-6 1979.59 Jan 28th Santa Barbara Invitational 2023
20 Washington Win 12-9 2087.42 Jan 28th Santa Barbara Invitational 2023
7 Oregon Win 13-12 2093.08 Jan 29th Santa Barbara Invitational 2023
14 UCLA Win 15-10 2284.43 Jan 29th Santa Barbara Invitational 2023
8 Cal Poly-SLO Loss 7-11 1466.05 Jan 29th Santa Barbara Invitational 2023
38 Emory Loss 11-12 1410.82 Feb 18th President’s Day Invite
7 Oregon Loss 9-13 1549.51 Feb 18th President’s Day Invite
43 Grand Canyon Win 15-4 2111.91 Feb 18th President’s Day Invite
6 Colorado Loss 10-11 1867.4 Feb 19th President’s Day Invite
30 Utah State Win 12-8 2091.85 Feb 19th President’s Day Invite
8 Cal Poly-SLO Loss 11-12 1807.95 Feb 19th President’s Day Invite
66 Stanford Win 10-9 1497.5 Feb 19th President’s Day Invite
14 UCLA Loss 8-10 1568.16 Feb 20th President’s Day Invite
43 Grand Canyon Win 13-4 2111.91 Feb 20th President’s Day Invite
64 California-San Diego Win 12-8 1820.75 Mar 4th Stanford Invite Mens
110 Southern California** Win 13-5 1731.56 Ignored Mar 4th Stanford Invite Mens
29 Wisconsin Win 11-10 1791.66 Mar 4th Stanford Invite Mens
7 Oregon Win 11-7 2434.97 Mar 5th Stanford Invite Mens
31 Oregon State Win 10-7 2020.69 Mar 5th Stanford Invite Mens
8 Cal Poly-SLO Win 7-6 2057.95 Mar 5th Stanford Invite Mens
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)