#39 California (11-9)

avg: 1867.09  •  sd: 70.22  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
115 Arizona State Win 8-4 1760.84 Feb 1st Presidents Day Qualifiers 2025
14 Cal Poly-SLO Loss 1-11 1843.09 Feb 1st Presidents Day Qualifiers 2025
220 California-San Diego-C** Win 13-0 984.47 Ignored Feb 1st Presidents Day Qualifiers 2025
63 California-Irvine Win 7-6 1767.75 Feb 1st Presidents Day Qualifiers 2025
162 Arizona** Win 11-3 1454.59 Ignored Feb 2nd Presidents Day Qualifiers 2025
25 UCLA Loss 5-11 1533.45 Feb 2nd Presidents Day Qualifiers 2025
63 California-Irvine Win 8-5 2096.35 Feb 2nd Presidents Day Qualifiers 2025
10 California-San Diego Loss 6-11 1949.28 Mar 1st Stanford Invite 2025 Womens
8 Washington Loss 10-13 2199.55 Mar 1st Stanford Invite 2025 Womens
31 Pittsburgh Win 10-9 2139.51 Mar 1st Stanford Invite 2025 Womens
17 California-Santa Barbara Loss 7-12 1783.34 Mar 1st Stanford Invite 2025 Womens
25 UCLA Loss 6-9 1714.88 Mar 2nd Stanford Invite 2025 Womens
30 Wisconsin Loss 7-8 1897.97 Mar 2nd Stanford Invite 2025 Womens
195 Texas A&M** Win 13-2 1194.04 Ignored Mar 22nd Womens Centex 2025
136 Trinity** Win 13-5 1635.87 Ignored Mar 22nd Womens Centex 2025
52 Washington University Loss 8-9 1613.3 Mar 22nd Womens Centex 2025
89 San Diego State Win 15-10 1849.09 Mar 22nd Womens Centex 2025
26 Texas Loss 11-15 1747.81 Mar 23rd Womens Centex 2025
83 Illinois Win 15-3 2039.05 Mar 23rd Womens Centex 2025
52 Washington University Win 12-11 1863.3 Mar 23rd Womens Centex 2025
**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)