#100 Cal State-Long Beach (10-4)

avg: 1064.93  •  sd: 73.03  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
96 Occidental Loss 7-8 963.5 Feb 1st Presidents’ Day Qualifier Women
196 California-B Win 9-7 581.4 Feb 1st Presidents’ Day Qualifier Women
204 California-Davis-B** Win 10-4 830.13 Ignored Feb 1st Presidents’ Day Qualifier Women
148 Arizona State Win 9-5 1262.62 Feb 1st Presidents’ Day Qualifier Women
145 UCLA-B Win 9-3 1366.28 Feb 2nd Presidents’ Day Qualifier Women
90 Southern California Loss 5-9 595.61 Feb 2nd Presidents’ Day Qualifier Women
58 California-Santa Cruz Win 10-9 1491.65 Feb 2nd Presidents’ Day Qualifier Women
163 Sonoma State Win 10-3 1162.53 Mar 7th Santa Clara Rage Home Tournament 2020
187 Cal Poly SLO-B** Win 13-2 1021.63 Ignored Mar 7th Santa Clara Rage Home Tournament 2020
204 California-Davis-B** Win 9-2 830.13 Ignored Mar 7th Santa Clara Rage Home Tournament 2020
96 Occidental Loss 7-9 809.16 Mar 7th Santa Clara Rage Home Tournament 2020
144 Nevada-Reno Win 9-6 1195.03 Mar 8th Santa Clara Rage Home Tournament 2020
86 San Diego State University Win 7-6 1299.11 Mar 8th Santa Clara Rage Home Tournament 2020
96 Occidental Loss 7-8 963.5 Mar 8th Santa Clara Rage Home Tournament 2020
**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)