#227 Cal State-Long Beach (7-7)

avg: 722  •  sd: 66.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
186 Washington-B Loss 8-13 369.25 Feb 3rd Stanford Open 2024
94 Puget Sound Loss 5-10 686.89 Feb 3rd Stanford Open 2024
323 California-Santa Cruz-B Win 13-3 839.49 Feb 3rd Stanford Open 2024
310 Arizona State-B Win 9-4 902.77 Mar 24th Southwest Showdown 2024
212 San Diego State Win 11-8 1136.53 Mar 24th Southwest Showdown 2024
234 Claremont Loss 6-11 156.44 Mar 24th Southwest Showdown 2024
297 Occidental Win 9-8 490.99 Mar 24th Southwest Showdown 2024
285 Southern California-B Win 10-7 816.94 Mar 30th 2024 Sinvite
79 Grand Canyon** Loss 2-12 740.7 Ignored Mar 30th 2024 Sinvite
129 San Jose State Loss 3-7 526.45 Mar 30th 2024 Sinvite
330 California-San Diego-B Win 14-5 771.51 Mar 30th 2024 Sinvite
211 Arizona Win 9-7 1052.13 Mar 31st 2024 Sinvite
110 Arizona State Loss 6-9 774.02 Mar 31st 2024 Sinvite
79 Grand Canyon** Loss 3-13 740.7 Ignored Mar 31st 2024 Sinvite
**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)