#105 California-Davis (15-3)

avg: 1344.88  •  sd: 74.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
275 UCLA-B** Win 13-2 1208.47 Ignored Jan 21st Presidents Day Qualifier
149 Cal Poly-SLO-B Loss 11-13 926.25 Jan 21st Presidents Day Qualifier
324 Caltech** Win 13-2 890.12 Ignored Jan 21st Presidents Day Qualifier
312 San Diego State-B Win 13-7 913.65 Jan 21st Presidents Day Qualifier
149 Cal Poly-SLO-B Loss 9-11 905.89 Jan 22nd Presidents Day Qualifier
166 California-San Diego-B Win 13-5 1693.76 Jan 22nd Presidents Day Qualifier
10 California-Santa Cruz** Loss 4-12 1489.74 Ignored Jan 22nd Presidents Day Qualifier
149 Cal Poly-SLO-B Win 8-5 1608.7 Feb 4th Stanford Open
138 Occidental Win 11-6 1744.97 Feb 4th Stanford Open
342 Santa Clara-B** Win 13-0 764.22 Ignored Feb 4th Stanford Open
180 Nevada-Reno Win 10-7 1419.68 Feb 5th Stanford Open
138 Occidental Win 10-8 1460.94 Feb 5th Stanford Open
232 Chico State Win 10-6 1278.51 Mar 11th Silicon Valley Rally
197 San Jose State Win 11-6 1497.75 Mar 11th Silicon Valley Rally
178 San Diego State Win 10-8 1302.51 Mar 11th Silicon Valley Rally
320 Stanford-B** Win 11-3 913.66 Ignored Mar 11th Silicon Valley Rally
265 Fresno State** Win 13-4 1266.88 Ignored Mar 12th Silicon Valley Rally
291 California-Santa Cruz-B** Win 13-1 1123.06 Ignored Mar 12th Silicon Valley Rally
**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)