#221 California-B (3-8)

avg: 854.61  •  sd: 84.65  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
179 Loyola Marymount Loss 9-10 907.73 Feb 4th Stanford Open
241 Humboldt State Loss 3-10 157.23 Feb 4th Stanford Open
180 Nevada-Reno Loss 7-9 750.67 Feb 4th Stanford Open
320 Stanford-B Win 10-4 913.66 Feb 5th Stanford Open
197 San Jose State Loss 6-7 826.05 Feb 5th Stanford Open
113 Claremont Loss 8-10 1049.75 Apr 1st Southwest Showdown
149 Cal Poly-SLO-B Win 11-10 1280.09 Apr 1st Southwest Showdown
230 Cal State-Long Beach Loss 8-9 666.9 Apr 1st Southwest Showdown
179 Loyola Marymount Loss 7-10 643.06 Apr 2nd Southwest Showdown
349 Grand Canyon-B** Win 15-2 662.64 Ignored Apr 2nd Southwest Showdown
152 Northern Arizona Loss 8-9 1018.35 Apr 2nd Southwest Showdown
**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)