#12 California-Santa Barbara (16-6)

avg: 1932.78  •  sd: 68.33  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
6 Brigham Young Loss 9-13 1736.26 Jan 27th Santa Barbara Invitational 2023
54 Cal Poly-SLO Win 12-6 1831.5 Jan 28th Santa Barbara Invitational 2023
71 Utah** Win 14-4 1696.07 Ignored Jan 28th Santa Barbara Invitational 2023
29 UCLA Win 14-4 2136.89 Jan 28th Santa Barbara Invitational 2023
8 Stanford Loss 7-8 1982.22 Jan 29th Santa Barbara Invitational 2023
30 California Win 12-7 2052.42 Jan 29th Santa Barbara Invitational 2023
18 California-San Diego Win 10-5 2271.39 Jan 29th Santa Barbara Invitational 2023
85 Southern California Win 10-7 1346.66 Feb 18th President’s Day Invite
18 California-San Diego Win 11-4 2297.49 Feb 18th President’s Day Invite
49 Texas Win 10-7 1719.29 Feb 18th President’s Day Invite
25 Duke Win 10-5 2142.64 Feb 18th President’s Day Invite
85 Southern California** Win 12-2 1556.99 Ignored Feb 19th President’s Day Invite
24 California-Davis Win 11-4 2193.24 Feb 19th President’s Day Invite
19 Colorado State Win 11-8 2052.61 Feb 19th President’s Day Invite
25 Duke Win 9-5 2097.8 Feb 19th President’s Day Invite
8 Stanford Loss 5-12 1507.22 Feb 20th President’s Day Invite
11 Oregon Loss 11-12 1844.78 Feb 20th President’s Day Invite
30 California Loss 5-7 1203.77 Mar 11th Stanford Invite Womens
9 Washington Win 11-7 2523.44 Mar 11th Stanford Invite Womens
1 North Carolina** Loss 4-10 2211.43 Ignored Mar 11th Stanford Invite Womens
18 California-San Diego Win 9-8 1822.49 Mar 12th Stanford Invite Womens
20 Western Washington Win 7-5 1982 Mar 12th Stanford Invite Womens
**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)