#18 California (19-10)

avg: 1961.57  •  sd: 59.82  •  top 16/20: 61.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
9 Oregon Win 11-10 2262.14 Jan 28th Santa Barbara Invitational 2023
54 Northwestern Loss 10-12 1378.07 Jan 28th Santa Barbara Invitational 2023
46 Western Washington Win 12-9 2033.9 Jan 28th Santa Barbara Invitational 2023
57 Stanford Win 9-7 1861.59 Jan 28th Santa Barbara Invitational 2023
15 UCLA Win 12-10 2266.41 Jan 29th Santa Barbara Invitational 2023
7 Cal Poly-SLO Loss 8-13 1679.19 Jan 29th Santa Barbara Invitational 2023
44 Victoria Win 11-4 2296.72 Jan 29th Santa Barbara Invitational 2023
15 UCLA Loss 3-10 1428.29 Feb 18th President’s Day Invite
29 Utah State Win 12-9 2183.64 Feb 18th President’s Day Invite
46 Western Washington Win 10-4 2288.53 Feb 18th President’s Day Invite
9 Oregon Loss 9-13 1718.57 Feb 19th President’s Day Invite
32 Oregon State Loss 10-11 1680.73 Feb 19th President’s Day Invite
73 California-Santa Barbara Win 14-6 2091.64 Feb 19th President’s Day Invite
61 Emory Win 13-9 1995.55 Feb 20th President’s Day Invite
29 Utah State Win 13-10 2166.42 Feb 20th President’s Day Invite
32 Oregon State Win 10-9 1930.73 Mar 4th Stanford Invite Mens
7 Cal Poly-SLO Loss 8-12 1734.2 Mar 4th Stanford Invite Mens
73 California-Santa Barbara Win 11-10 1616.64 Mar 4th Stanford Invite Mens
47 Colorado State Win 13-9 2065.79 Mar 5th Stanford Invite Mens
9 Oregon Loss 6-13 1537.14 Mar 5th Stanford Invite Mens
44 Victoria Loss 7-12 1176.21 Mar 5th Stanford Invite Mens
58 California-San Diego Win 13-8 2077.57 Mar 5th Stanford Invite Mens
72 Auburn Win 10-5 2071.7 Apr 1st Easterns 2023
3 Massachusetts Loss 8-13 1815.25 Apr 1st Easterns 2023
8 Pittsburgh Loss 10-12 1917.05 Apr 1st Easterns 2023
20 North Carolina State Win 13-8 2441.56 Apr 1st Easterns 2023
14 Carleton College Win 14-12 2270.83 Apr 2nd Easterns 2023
25 North Carolina-Wilmington Win 14-6 2484.26 Apr 2nd Easterns 2023
20 North Carolina State Win 12-11 2070.4 Apr 2nd Easterns 2023
**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)