#195 Sonoma State (9-10)

avg: 963.31  •  sd: 68.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Santa Clara Loss 2-13 899.77 Feb 3rd Presidents Day Qualifier 2018
332 California-San Diego-B Win 13-8 938.34 Feb 3rd Presidents Day Qualifier 2018
316 Cal Poly-SLO-B Win 11-4 1123.91 Feb 3rd Presidents Day Qualifier 2018
235 Arizona State-B Loss 9-13 384.09 Feb 4th Presidents Day Qualifier 2018
329 California-Irvine Win 13-9 883.44 Feb 4th Presidents Day Qualifier 2018
267 Cal State-Long Beach Loss 9-12 380.37 Feb 4th Presidents Day Qualifier 2018
35 Air Force** Loss 5-12 1039.57 Ignored Feb 10th Stanford Open 2018
276 San Jose State Win 12-7 1219.34 Feb 10th Stanford Open 2018
186 Cal Poly-Pomona Win 10-9 1108.1 Feb 10th Stanford Open 2018
158 Lewis & Clark Win 10-6 1598.29 Feb 11th Stanford Open 2018
32 California** Loss 5-13 1095.8 Ignored Feb 11th Stanford Open 2018
131 Chico State Loss 8-11 823.03 Feb 11th Stanford Open 2018
53 UCLA Loss 7-13 976.89 Feb 11th Stanford Open 2018
87 Las Positas Loss 8-13 897.04 Mar 10th Silicon Valley Rally 2018
360 Fresno State Win 13-8 838.71 Mar 10th Silicon Valley Rally 2018
131 Chico State Win 11-10 1313.64 Mar 10th Silicon Valley Rally 2018
146 Nevada-Reno Loss 4-7 653.14 Mar 11th Silicon Valley Rally 2018
165 Humboldt State Win 13-10 1394.83 Mar 11th Silicon Valley Rally 2018
79 California-Davis Loss 4-13 814.63 Mar 11th Silicon Valley Rally 2018
**Blowout Eligible

FAQ

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
  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
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)