#164 Cal Poly-SLO-B (7-14)

avg: 630.95  •  sd: 73.99  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
61 British Columbia-B** Loss 1-13 778.04 Ignored Feb 1st Stanford Open Womens
52 Oregon State Loss 7-13 914.7 Feb 1st Stanford Open Womens
46 Carleton College-Eclipse** Loss 4-13 938.86 Ignored Feb 1st Stanford Open Womens
178 Nevada-Reno Win 11-7 1022.69 Feb 2nd Stanford Open Womens
82 California-Irvine Loss 2-13 590.17 Feb 15th Santa Clara University WLT Tournament
183 Occidental Loss 7-8 400.62 Feb 15th Santa Clara University WLT Tournament
194 California-Davis-B Win 8-7 593.66 Feb 15th Santa Clara University WLT Tournament
143 California-B Win 8-3 1326.7 Feb 16th Santa Clara University WLT Tournament
82 California-Irvine Loss 2-13 590.17 Feb 16th Santa Clara University WLT Tournament
183 Occidental Loss 9-11 276.41 Feb 16th Santa Clara University WLT Tournament
249 California-San Diego-C** Win 11-3 574.05 Ignored Apr 12th Southwest Dev Womens Conferences 2025
196 UCLA-B Win 10-9 587.49 Apr 12th Southwest Dev Womens Conferences 2025
262 Southern California-B** Win 13-0 36.04 Ignored Apr 12th Southwest Dev Womens Conferences 2025
123 California-San Diego-B Loss 7-9 631.15 Apr 13th Southwest Dev Womens Conferences 2025
196 UCLA-B Win 13-10 790.63 Apr 13th Southwest Dev Womens Conferences 2025
123 California-San Diego-B Loss 5-8 456.88 Apr 13th Southwest Dev Womens Conferences 2025
8 Stanford** Loss 1-15 1534.37 Ignored Apr 26th Southwest D I College Womens Regionals 2025
82 California-Irvine Loss 7-15 590.17 Apr 26th Southwest D I College Womens Regionals 2025
41 Southern California** Loss 6-15 972.33 Ignored Apr 26th Southwest D I College Womens Regionals 2025
105 San Diego State Loss 10-14 637.11 Apr 27th Southwest D I College Womens Regionals 2025
123 California-San Diego-B Loss 6-10 414.33 Apr 27th Southwest D I College Womens Regionals 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)