#17 California-San Diego (12-10)

avg: 1824.58  •  sd: 46.02  •  top 16/20: 88.8%

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# Opponent Result Game Rating Status Date Event
25 California-Davis Win 11-7 2186.84 Jan 28th Santa Barbara Invitational 2023
42 Wisconsin Win 9-8 1631.49 Jan 28th Santa Barbara Invitational 2023
7 Carleton College Loss 3-15 1678.35 Jan 29th Santa Barbara Invitational 2023
74 Utah Win 10-7 1614.07 Jan 29th Santa Barbara Invitational 2023
12 California-Santa Barbara Loss 5-10 1484.52 Jan 29th Santa Barbara Invitational 2023
87 Southern California** Win 11-4 1686.09 Ignored Feb 18th President’s Day Invite
28 Duke Win 11-7 2148.93 Feb 18th President’s Day Invite
12 California-Santa Barbara Loss 4-11 1458.42 Feb 18th President’s Day Invite
48 Texas Win 11-7 1926.85 Feb 18th President’s Day Invite
3 Colorado** Loss 3-14 1867.86 Ignored Feb 19th President’s Day Invite
31 California Win 10-6 2154.12 Feb 19th President’s Day Invite
29 UCLA Win 11-8 2030.23 Feb 19th President’s Day Invite
11 Oregon Loss 9-13 1678.25 Feb 19th President’s Day Invite
24 Carleton College-Eclipse Win 10-8 1995.5 Feb 20th President’s Day Invite
25 California-Davis Win 7-6 1844.95 Feb 20th President’s Day Invite
39 Santa Clara Win 9-5 2068.41 Mar 11th Stanford Invite Womens
3 Colorado** Loss 5-12 1867.86 Ignored Mar 11th Stanford Invite Womens
11 Oregon Loss 8-13 1600.66 Mar 11th Stanford Invite Womens
4 Tufts Loss 5-10 1852.54 Mar 11th Stanford Invite Womens
39 Santa Clara Win 8-7 1664.36 Mar 12th Stanford Invite Womens
25 California-Davis Loss 4-5 1594.95 Mar 12th Stanford Invite Womens
12 California-Santa Barbara Loss 8-9 1933.42 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)