#11 California-Santa Barbara (11-5)

avg: 1976.78  •  sd: 60.87  •  top 16/20: 99.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
12 Brigham Young Win 15-12 2222.63 Jan 26th Santa Barbara Invite 2024
80 Northwestern** Win 15-4 1552.95 Ignored Jan 27th Santa Barbara Invite 2024
13 California-Santa Cruz Loss 10-12 1664.02 Jan 27th Santa Barbara Invite 2024
30 UCLA Win 11-3 2207.48 Jan 27th Santa Barbara Invite 2024
5 Oregon Loss 8-10 1968.93 Jan 27th Santa Barbara Invite 2024
48 California** Win 13-5 1943.04 Ignored Jan 28th Santa Barbara Invite 2024
30 UCLA Win 9-5 2136.54 Jan 28th Santa Barbara Invite 2024
62 Southern California** Win 15-5 1814.11 Ignored Feb 17th Presidents Day Invite 2024
28 Colorado State Win 15-5 2234.36 Feb 17th Presidents Day Invite 2024
39 Cal Poly-SLO Win 14-8 2034.46 Feb 17th Presidents Day Invite 2024
7 Stanford Loss 8-11 1802.24 Feb 18th Presidents Day Invite 2024
10 Colorado Win 10-9 2109.02 Feb 18th Presidents Day Invite 2024
26 California-San Diego Loss 9-10 1543.61 Feb 18th Presidents Day Invite 2024
23 Western Washington Win 12-8 2151.31 Feb 18th Presidents Day Invite 2024
7 Stanford Loss 6-12 1588.53 Feb 19th Presidents Day Invite 2024
26 California-San Diego Win 12-8 2109.76 Feb 19th Presidents Day Invite 2024
**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)