#285 Southern California-B (3-8)

avg: 427.27  •  sd: 72.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
151 Cal Poly-SLO-B** Loss 2-13 433.94 Ignored Jan 20th Pres Day Quals
134 California-Irvine** Loss 1-13 509.09 Ignored Jan 20th Pres Day Quals
323 California-Santa Cruz-B Loss 7-8 114.49 Jan 20th Pres Day Quals
320 Cal Poly-SLO-C Win 10-7 649.26 Jan 21st Pres Day Quals
330 California-San Diego-B Win 9-5 700.56 Jan 21st Pres Day Quals
227 Cal State-Long Beach Loss 7-10 332.33 Mar 30th 2024 Sinvite
330 California-San Diego-B Loss 8-8 171.51 Mar 30th 2024 Sinvite
79 Grand Canyon** Loss 2-12 740.7 Ignored Mar 30th 2024 Sinvite
129 San Jose State** Loss 4-11 526.45 Ignored Mar 30th 2024 Sinvite
292 California-Santa Barbara-B Win 15-10 856.25 Mar 31st 2024 Sinvite
234 Claremont Loss 9-14 229.27 Mar 31st 2024 Sinvite
**Blowout Eligible

FAQ

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
  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
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)