#178 San Diego State (11-5)

avg: 843.93  •  sd: 64.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
223 Cal State-Long Beach Loss 8-11 242.59 Feb 18th Temecula Throwdown
273 California-Davis-B Win 12-7 907.4 Feb 18th Temecula Throwdown
163 California-San Diego-B Loss 7-13 361.94 Feb 18th Temecula Throwdown
229 California-Santa Barbara-B Win 11-8 958.19 Feb 18th Temecula Throwdown
245 Arizona State-B Win 11-9 763.49 Feb 19th Temecula Throwdown
223 Cal State-Long Beach Win 8-2 1208.2 Feb 19th Temecula Throwdown
163 California-San Diego-B Loss 10-11 794.48 Feb 19th Temecula Throwdown
265 Fresno State Win 13-8 944.16 Mar 11th Silicon Valley Rally
100 California-Davis Loss 8-10 931.92 Mar 11th Silicon Valley Rally
315 Stanford-B Win 10-5 657.07 Mar 11th Silicon Valley Rally
202 San Jose State Win 8-6 1028.2 Mar 11th Silicon Valley Rally
237 Chico State Win 10-8 811.6 Mar 12th Silicon Valley Rally
283 California-Santa Cruz-B Win 9-6 730.52 Mar 12th Silicon Valley Rally
197 Loyola Marymount Win 12-7 1275.5 Mar 18th Sundown Showdown
223 Cal State-Long Beach Win 13-10 936.34 Mar 18th Sundown Showdown
115 California-Irvine Loss 10-12 873.62 Mar 18th Sundown 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)