#153 California-Davis (5-3)

avg: 700.12  •  sd: 63.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
225 Cal Poly-SLO-C Win 13-3 707.71 Jan 20th Pres Day Quals
223 California-Santa Barbara-B Win 13-5 744.32 Jan 20th Pres Day Quals
189 San Diego State Win 11-8 808.98 Jan 20th Pres Day Quals
121 UCLA-B Loss 9-13 525.03 Jan 21st Pres Day Quals
106 California-Irvine Loss 7-10 624.91 Jan 21st Pres Day Quals
- Chico State** Win 13-2 600 Ignored Feb 3rd Stanford Open 2024
103 Santa Clara Loss 4-9 421.97 Feb 3rd Stanford Open 2024
126 Loyola Marymount Win 8-7 1044.89 Feb 3rd Stanford Open 2024
**Blowout Eligible


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)