#10 California-Santa Cruz (21-6)

avg: 2089.74  •  sd: 75.17  •  top 16/20: 98.2%

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# Opponent Result Game Rating Status Date Event
120 California-Irvine** Win 13-2 1895.52 Ignored Jan 21st Presidents Day Qualifier
166 California-San Diego-B** Win 13-0 1693.76 Ignored Jan 21st Presidents Day Qualifier
243 California-Santa Barbara-B** Win 13-1 1352.23 Ignored Jan 21st Presidents Day Qualifier
105 California-Davis** Win 12-4 1944.88 Ignored Jan 22nd Presidents Day Qualifier
333 California-San Diego-C** Win 15-0 840.47 Ignored Jan 22nd Presidents Day Qualifier
151 Arizona State** Win 14-4 1746.59 Ignored Jan 28th Santa Barbara Invitational 2023
29 Utah State Win 13-11 2067.11 Jan 28th Santa Barbara Invitational 2023
58 California-San Diego Win 15-6 2181.41 Jan 28th Santa Barbara Invitational 2023
17 Washington Win 12-9 2335.51 Jan 28th Santa Barbara Invitational 2023
9 Oregon Win 13-12 2262.14 Jan 29th Santa Barbara Invitational 2023
15 UCLA Win 15-10 2481.89 Jan 29th Santa Barbara Invitational 2023
7 Cal Poly-SLO Loss 7-11 1708.46 Jan 29th Santa Barbara Invitational 2023
61 Emory Loss 11-12 1451.98 Feb 18th President’s Day Invite
9 Oregon Loss 9-13 1718.57 Feb 18th President’s Day Invite
42 Grand Canyon Win 15-4 2305.28 Feb 18th President’s Day Invite
6 Colorado Loss 10-11 2072.57 Feb 19th President’s Day Invite
29 Utah State Win 12-8 2279.43 Feb 19th President’s Day Invite
7 Cal Poly-SLO Loss 11-12 2050.35 Feb 19th President’s Day Invite
57 Stanford Win 10-9 1707.25 Feb 19th President’s Day Invite
15 UCLA Loss 8-10 1765.62 Feb 20th President’s Day Invite
42 Grand Canyon Win 13-4 2305.28 Feb 20th President’s Day Invite
58 California-San Diego Win 12-8 2022.57 Mar 4th Stanford Invite Mens
109 Southern California** Win 13-5 1923.91 Ignored Mar 4th Stanford Invite Mens
23 Wisconsin Win 11-10 2019.52 Mar 4th Stanford Invite Mens
9 Oregon Win 11-7 2604.03 Mar 5th Stanford Invite Mens
32 Oregon State Win 10-7 2195.39 Mar 5th Stanford Invite Mens
7 Cal Poly-SLO Win 7-6 2300.35 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)