#41 Southern California (6-12)

avg: 1842.16  •  sd: 75.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
137 California-B** Win 13-2 1631.69 Ignored Feb 1st Stanford Open Womens
171 Nevada-Reno** Win 8-0 1404.15 Ignored Feb 1st Stanford Open Womens
51 British Columbia-B Win 8-6 2044.5 Feb 2nd Stanford Open Womens
51 British Columbia-B Loss 5-6 1619.01 Feb 2nd Stanford Open Womens
35 Carleton College-Eclipse Loss 8-10 1689.22 Feb 2nd Stanford Open Womens
65 Portland Loss 4-10 1026.91 Feb 2nd Stanford Open Womens
17 California-Santa Barbara Loss 6-13 1703.86 Feb 15th Presidents Day Invite 2025
5 Oregon** Loss 2-13 2144.32 Ignored Feb 15th Presidents Day Invite 2025
16 California-Davis Loss 2-13 1706.69 Feb 16th Presidents Day Invite 2025
17 California-Santa Barbara Loss 7-10 1914.19 Feb 16th Presidents Day Invite 2025
12 California-Santa Cruz Loss 6-13 1877.11 Feb 16th Presidents Day Invite 2025
43 Colorado State Win 10-9 1960.82 Feb 17th Presidents Day Invite 2025
26 Texas Win 10-8 2391.64 Feb 17th Presidents Day Invite 2025
1 British Columbia** Loss 4-15 2402.29 Ignored Mar 22nd Northwest Challenge 2025
10 California-San Diego** Loss 5-12 1895.97 Ignored Mar 22nd Northwest Challenge 2025
6 Vermont Loss 6-13 2071.2 Mar 22nd Northwest Challenge 2025
43 Colorado State Win 9-8 1960.82 Mar 23rd Northwest Challenge 2025
22 Western Washington Loss 6-11 1703.98 Mar 23rd Northwest Challenge 2025
**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)