#107 Chico State (12-6)

avg: 1076.39  •  sd: 101.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
246 California-B** Win 12-2 780.65 Ignored Feb 2nd Presidents Day Qualifiers Women
39 California-Davis Loss 8-13 1097.12 Feb 2nd Presidents Day Qualifiers Women
119 UCLA-B Win 9-7 1316.01 Feb 2nd Presidents Day Qualifiers Women
187 California-San Diego-B Win 8-1 1198.15 Feb 2nd Presidents Day Qualifiers Women
275 Cal Poly SLO-B** Win 13-0 330.79 Ignored Feb 3rd Presidents Day Qualifiers Women
23 California** Loss 3-12 1317.92 Ignored Feb 3rd Presidents Day Qualifiers Women
119 UCLA-B Win 10-2 1636.67 Feb 3rd Presidents Day Qualifiers Women
232 California-Irvine Win 6-5 411.61 Feb 3rd Presidents Day Qualifiers Women
39 California-Davis Loss 8-9 1468.28 Feb 9th Stanford Open 2019
55 Portland Win 8-7 1612.97 Feb 9th Stanford Open 2019
176 Santa Clara Win 12-5 1278.03 Feb 9th Stanford Open 2019
136 Occidental Loss 8-9 813.15 Mar 23rd Trouble in Vegas 2019
277 Arizona-B** Win 13-3 271.45 Ignored Mar 23rd Trouble in Vegas 2019
230 New Mexico** Win 13-5 920.67 Ignored Mar 23rd Trouble in Vegas 2019
194 Utah State Win 10-7 962.94 Mar 23rd Trouble in Vegas 2019
73 Northern Arizona Loss 6-8 1024.32 Mar 24th Trouble in Vegas 2019
136 Occidental Loss 4-9 338.15 Mar 24th Trouble in Vegas 2019
120 Arizona State Win 7-6 1152.17 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)