#187 California-San Diego-B (7-6)

avg: 598.15  •  sd: 74.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
275 Cal Poly SLO-B** Win 13-2 330.79 Ignored Feb 2nd Presidents Day Qualifiers Women
39 California-Davis** Loss 4-10 993.28 Ignored Feb 2nd Presidents Day Qualifiers Women
119 UCLA-B Loss 3-12 436.67 Feb 2nd Presidents Day Qualifiers Women
107 Chico State Loss 1-8 476.39 Feb 2nd Presidents Day Qualifiers Women
246 California-B Win 12-3 780.65 Feb 3rd Presidents Day Qualifiers Women
224 Sonoma State Win 9-4 963.71 Feb 3rd Presidents Day Qualifiers Women
250 California-Davis-B Win 13-5 747.51 Feb 3rd Presidents Day Qualifiers Women
120 Arizona State Loss 3-8 427.17 Mar 23rd Trouble in Vegas 2019
243 Colorado-B Win 11-4 790.79 Mar 23rd Trouble in Vegas 2019
234 Nevada-Reno Win 8-4 835.88 Mar 23rd Trouble in Vegas 2019
224 Sonoma State Loss 5-7 35.56 Mar 23rd Trouble in Vegas 2019
243 Colorado-B Win 9-5 719.85 Mar 24th Trouble in Vegas 2019
194 Utah State Loss 8-9 448.27 Mar 24th Trouble in Vegas 2019
**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)