#354 California-Santa Cruz-B (6-12)

avg: 491.33  •  sd: 71.36  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
121 Cal Poly-SLO-B** Loss 0-13 801.25 Ignored Jan 20th Pres Day Quals
352 Cal Poly-SLO-C Loss 7-9 219.58 Jan 20th Pres Day Quals
125 California-Irvine** Loss 4-13 791.92 Ignored Jan 20th Pres Day Quals
298 Southern California-B Win 8-7 845.15 Jan 20th Pres Day Quals
332 California-San Diego-B Loss 9-10 466.08 Jan 21st Pres Day Quals
396 California-San Diego-C Win 11-9 366.31 Jan 21st Pres Day Quals
255 Cal State-Long Beach Loss 3-13 322.79 Feb 3rd Stanford Open 2024
155 Washington-B Loss 6-11 753.3 Feb 3rd Stanford Open 2024
169 Puget Sound** Loss 3-13 652.14 Ignored Feb 3rd Stanford Open 2024
334 California-Santa Barbara-B Loss 8-11 215.78 Mar 9th Silicon Valley Rally 2024
158 UCLA-B** Loss 2-13 689.07 Ignored Mar 9th Silicon Valley Rally 2024
344 Chico State Loss 7-11 73.25 Mar 9th Silicon Valley Rally 2024
338 Cal Poly-Humboldt Win 13-4 1163.56 Mar 10th Silicon Valley Rally 2024
230 California-Davis Loss 4-12 415.39 Mar 10th Silicon Valley Rally 2024
344 Chico State Win 10-9 665.15 Mar 10th Silicon Valley Rally 2024
332 California-San Diego-B Loss 7-15 -8.92 Apr 14th Southwest Dev Mens Conferences 2024
396 California-San Diego-C Win 15-5 717.11 Apr 14th Southwest Dev Mens Conferences 2024
334 California-Santa Barbara-B Win 9-8 706.39 Apr 14th Southwest Dev Mens Conferences 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)