#137 California-B (8-13)

avg: 947.01  •  sd: 66.3  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
248 Cal Poly-Humboldt** Win 8-1 600 Ignored Feb 3rd Stanford Open 2024
30 Cal Poly-SLO** Loss 1-13 1241.91 Ignored Feb 3rd Stanford Open 2024
43 Portland** Loss 2-13 1061.6 Ignored Feb 3rd Stanford Open 2024
79 San Diego State Loss 4-10 760.25 Feb 17th Santa Clara University Presidents Day
72 Santa Clara Loss 3-7 796.79 Feb 17th Santa Clara University Presidents Day
184 California-Davis-B Win 10-2 1196.28 Feb 17th Santa Clara University Presidents Day
88 California-Irvine Loss 4-8 744.48 Feb 18th Santa Clara University Presidents Day
131 Occidental Loss 5-6 889.86 Feb 18th Santa Clara University Presidents Day
184 California-Davis-B Win 9-4 1196.28 Feb 18th Santa Clara University Presidents Day
238 California-San Diego-C** Win 13-1 617.59 Ignored Mar 9th Irvine Open
186 UCLA-B Win 6-5 710.49 Mar 9th Irvine Open
88 California-Irvine Loss 2-4 813.13 Mar 9th Irvine Open
184 California-Davis-B Win 7-4 1092.44 Mar 10th Irvine Open
85 California-San Diego-B Loss 2-9 739.27 Mar 10th Irvine Open
75 Lewis & Clark Loss 2-10 775.08 Mar 10th Irvine Open
97 Stanford-B Loss 4-9 635.17 Apr 14th Southwest Dev Womens Conferences 2024
148 Cal Poly-SLO-B Win 6-4 1246.66 Apr 14th Southwest Dev Womens Conferences 2024
47 Southern California Loss 3-7 1036.66 Apr 27th Southwest D I College Womens Regionals 2024
5 Stanford** Loss 0-15 1919.88 Ignored Apr 27th Southwest D I College Womens Regionals 2024
88 California-Irvine Loss 7-8 1184.28 Apr 27th Southwest D I College Womens Regionals 2024
136 Arizona State Win 6-5 1080.01 Apr 28th Southwest D I College Womens Regionals 2024
**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)