#119 Southern California (1-10)

avg: 1067.82  •  sd: 65.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
3 Brigham Young** Loss 6-15 1449.06 Ignored Jan 28th Santa Barbara Invitational 2023
49 Case Western Reserve Loss 10-14 1010.55 Jan 28th Santa Barbara Invitational 2023
14 UCLA Loss 8-13 1267.4 Jan 28th Santa Barbara Invitational 2023
32 Victoria Loss 8-12 1120.21 Jan 28th Santa Barbara Invitational 2023
74 California-San Diego Loss 11-13 1084.25 Jan 29th Santa Barbara Invitational 2023
76 Stanford Win 9-8 1431.5 Jan 29th Santa Barbara Invitational 2023
74 California-San Diego Loss 11-13 1084.25 Mar 4th Stanford Invite Mens
9 California-Santa Cruz** Loss 5-13 1227.88 Ignored Mar 4th Stanford Invite Mens
28 Wisconsin Loss 4-13 995.9 Mar 4th Stanford Invite Mens
94 California-Santa Barbara Loss 6-11 662.84 Mar 5th Stanford Invite Mens
91 Santa Clara Loss 7-8 1093.37 Mar 5th Stanford Invite Mens
**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)