#255 Cal State-Long Beach (7-13)

avg: 922.79  •  sd: 60.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
155 Washington-B Loss 8-13 803.84 Feb 3rd Stanford Open 2024
169 Puget Sound Loss 5-10 678.24 Feb 3rd Stanford Open 2024
354 California-Santa Cruz-B Win 13-3 1091.33 Feb 3rd Stanford Open 2024
350 Arizona State-B Win 9-4 1111.42 Mar 24th Southwest Showdown 2024
211 San Diego State Win 11-8 1445.75 Mar 24th Southwest Showdown 2024
235 Claremont Loss 6-11 448.51 Mar 24th Southwest Showdown 2024
339 Occidental Win 9-8 683.41 Mar 24th Southwest Showdown 2024
298 Southern California-B Win 10-7 1109.81 Mar 30th 2024 Sinvite
71 Grand Canyon** Loss 2-12 1029.57 Ignored Mar 30th 2024 Sinvite
124 San Jose State Loss 3-7 792.34 Mar 30th 2024 Sinvite
332 California-San Diego-B Win 14-5 1191.08 Mar 30th 2024 Sinvite
219 Arizona Win 9-7 1331.82 Mar 31st 2024 Sinvite
133 Arizona State Loss 6-9 956.53 Mar 31st 2024 Sinvite
71 Grand Canyon** Loss 3-13 1029.57 Ignored Mar 31st 2024 Sinvite
113 Southern California Loss 5-13 854.03 Apr 13th SoCal D I Mens Conferences 2024
60 California-Santa Barbara** Loss 4-12 1115.6 Ignored Apr 13th SoCal D I Mens Conferences 2024
211 San Diego State Loss 6-11 533.44 Apr 13th SoCal D I Mens Conferences 2024
24 UCLA** Loss 3-13 1420.7 Ignored Apr 13th SoCal D I Mens Conferences 2024
192 Loyola Marymount Loss 6-8 855.4 Apr 14th SoCal D I Mens Conferences 2024
125 California-Irvine Loss 7-13 834.39 Apr 14th SoCal D I Mens Conferences 2024
**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)