#304 Cal State-Long Beach (1-9)

avg: 362.79  •  sd: 112.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
91 Cal Poly-SLO-B** Loss 4-13 690.22 Ignored Feb 8th Stanford Open Mens
215 Nevada-Reno Loss 5-11 144.04 Feb 8th Stanford Open Mens
208 UCLA-B Win 9-6 1183.86 Feb 9th Stanford Open Mens
162 Washington-B** Loss 5-13 382.27 Ignored Feb 9th Stanford Open Mens
266 Chico State Loss 4-13 -52.49 Feb 9th Stanford Open Mens
95 Claremont** Loss 5-13 675.64 Ignored Mar 29th Southwest Showdown 2025
85 Southern California** Loss 2-13 717.15 Ignored Mar 29th Southwest Showdown 2025
186 Occidental Loss 8-11 510.3 Mar 29th Southwest Showdown 2025
186 Occidental Loss 5-13 275.91 Mar 30th Southwest Showdown 2025
208 UCLA-B Loss 6-11 218.6 Mar 30th Southwest Showdown 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)