#9 California-Santa Barbara (16-7)

avg: 2421.8  •  sd: 48.96  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
11 Brigham Young Win 15-12 2619.36 Jan 26th Santa Barbara Invite 2024
32 UCLA Win 11-3 2451.87 Jan 27th Santa Barbara Invite 2024
5 Oregon Loss 8-10 2343.04 Jan 27th Santa Barbara Invite 2024
82 Northwestern** Win 15-4 1932.46 Ignored Jan 27th Santa Barbara Invite 2024
14 California-Santa Cruz Loss 10-12 1942.11 Jan 27th Santa Barbara Invite 2024
30 California Win 13-5 2484.55 Jan 28th Santa Barbara Invite 2024
32 UCLA Win 9-5 2380.93 Jan 28th Santa Barbara Invite 2024
55 Southern California** Win 15-5 2156.09 Ignored Feb 17th Presidents Day Invite 2024
23 Cal Poly-SLO Win 14-8 2507.32 Feb 17th Presidents Day Invite 2024
19 Colorado State Win 15-5 2713.2 Feb 17th Presidents Day Invite 2024
6 Stanford Loss 8-11 2193.02 Feb 18th Presidents Day Invite 2024
15 California-San Diego Loss 9-10 2037.55 Feb 18th Presidents Day Invite 2024
13 Western Washington Win 12-8 2652.95 Feb 18th Presidents Day Invite 2024
8 Colorado Win 10-9 2583.07 Feb 18th Presidents Day Invite 2024
6 Stanford Loss 6-12 1979.32 Feb 19th Presidents Day Invite 2024
15 California-San Diego Win 12-8 2603.7 Feb 19th Presidents Day Invite 2024
23 Cal Poly-SLO Win 8-5 2424.89 Mar 2nd Stanford Invite 2024
24 California-Davis Win 10-7 2360.76 Mar 2nd Stanford Invite 2024
25 Pittsburgh Win 9-4 2562.92 Mar 2nd Stanford Invite 2024
2 Vermont Loss 8-11 2424.02 Mar 2nd Stanford Invite 2024
1 British Columbia Loss 8-12 2452.99 Mar 3rd Stanford Invite 2024
25 Pittsburgh Win 8-5 2416.52 Mar 3rd Stanford Invite 2024
14 California-Santa Cruz Win 11-7 2647.13 Mar 3rd Stanford Invite 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)