#249 California-San Diego-C (3-20)

avg: -25.95  •  sd: 101.46  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
114 Arizona State** Loss 1-13 351.81 Ignored Feb 1st Presidents Day Qualifiers 2025
12 Cal Poly-SLO** Loss 0-13 1483.14 Ignored Feb 1st Presidents Day Qualifiers 2025
40 California** Loss 0-13 981.98 Ignored Feb 1st Presidents Day Qualifiers 2025
82 California-Irvine** Loss 1-13 590.17 Ignored Feb 1st Presidents Day Qualifiers 2025
114 Arizona State** Loss 1-13 351.81 Ignored Feb 2nd Presidents Day Qualifiers 2025
220 Cal State-Long Beach Loss 3-7 -383.97 Feb 2nd Presidents Day Qualifiers 2025
196 UCLA-B Loss 2-10 -137.51 Feb 2nd Presidents Day Qualifiers 2025
220 Cal State-Long Beach Loss 5-7 -112.11 Mar 2nd Claremont Classic 2025
133 Claremont Loss 4-8 246.42 Mar 2nd Claremont Classic 2025
196 UCLA-B Loss 2-5 -137.51 Mar 2nd Claremont Classic 2025
262 Southern California-B Win 7-2 36.04 Mar 2nd Claremont Classic 2025
220 Cal State-Long Beach Loss 3-7 -383.97 Mar 8th Gnomageddon
82 California-Irvine** Loss 1-12 590.17 Ignored Mar 8th Gnomageddon
133 Claremont** Loss 1-7 211.23 Ignored Mar 8th Gnomageddon
220 Cal State-Long Beach Win 6-5 341.03 Mar 9th Gnomageddon
105 San Diego State Loss 4-8 471 Mar 9th Gnomageddon
133 Claremont** Loss 2-10 211.23 Ignored Mar 9th Gnomageddon
164 Cal Poly-SLO-B** Loss 3-11 30.95 Ignored Apr 12th Southwest Dev Womens Conferences 2025
123 California-San Diego-B** Loss 2-8 310.49 Ignored Apr 12th Southwest Dev Womens Conferences 2025
196 UCLA-B Loss 2-13 -137.51 Apr 12th Southwest Dev Womens Conferences 2025
262 Southern California-B Win 7-4 -67.8 Apr 12th Southwest Dev Womens Conferences 2025
123 California-San Diego-B** Loss 3-14 310.49 Ignored Apr 13th Southwest Dev Womens Conferences 2025
196 UCLA-B Loss 6-11 -84.21 Apr 13th Southwest Dev Womens Conferences 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)