#332 California-San Diego-B (5-10)

avg: 591.08  •  sd: 85.33  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
144 Santa Clara** Loss 5-13 736.2 Ignored Jan 20th Pres Day Quals
158 UCLA-B Loss 7-10 899.41 Jan 20th Pres Day Quals
221 California-B Loss 6-11 502.22 Jan 21st Pres Day Quals
298 Southern California-B Loss 5-9 191.09 Jan 21st Pres Day Quals
354 California-Santa Cruz-B Win 10-9 616.33 Jan 21st Pres Day Quals
255 Cal State-Long Beach Loss 5-14 322.79 Mar 30th 2024 Sinvite
71 Grand Canyon** Loss 4-10 1029.57 Ignored Mar 30th 2024 Sinvite
298 Southern California-B Win 8-8 720.15 Mar 30th 2024 Sinvite
124 San Jose State** Loss 1-12 792.34 Ignored Mar 30th 2024 Sinvite
334 California-Santa Barbara-B Loss 5-15 -18.61 Mar 31st 2024 Sinvite
235 Claremont Loss 1-15 395.21 Mar 31st 2024 Sinvite
221 California-B Loss 5-11 448.92 Apr 14th Southwest Dev Mens Conferences 2024
396 California-San Diego-C Win 13-9 535.67 Apr 14th Southwest Dev Mens Conferences 2024
354 California-Santa Cruz-B Win 15-7 1091.33 Apr 14th Southwest Dev Mens Conferences 2024
334 California-Santa Barbara-B Win 15-3 1181.39 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)