#289 Southern California-B (6-6)

avg: 739.54  •  sd: 68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
187 Arizona Loss 4-12 506.58 Mar 29th Sinvite 2025
154 Brigham Young-B Loss 2-13 644.54 Mar 29th Sinvite 2025
352 California-San Diego-B Win 13-8 916.74 Mar 29th Sinvite 2025
379 San Diego State-B Win 7-6 375.94 Mar 29th Sinvite 2025
187 Arizona Loss 10-11 981.58 Mar 30th Sinvite 2025
249 Northern Arizona Win 10-9 996.65 Mar 30th Sinvite 2025
249 Northern Arizona Win 10-9 996.65 Mar 30th Sinvite 2025
318 Arizona State-B Loss 7-9 323.64 Apr 12th Southwest Dev Mens Conferences 2025
352 California-San Diego-B Win 13-8 916.74 Apr 12th Southwest Dev Mens Conferences 2025
174 California-Santa Cruz-B Loss 3-13 562.98 Apr 12th Southwest Dev Mens Conferences 2025
214 UCLA-B Loss 7-9 728.46 Apr 12th Southwest Dev Mens Conferences 2025
379 San Diego State-B Win 15-6 850.94 Apr 13th Southwest Dev Mens Conferences 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)