#124 California-San Diego-B (9-8)

avg: 908.98  •  sd: 60.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
171 California-Irvine Win 10-3 1125.66 Feb 1st Presidents’ Day Qualifier Women
125 Chico State Loss 7-8 783.96 Feb 1st Presidents’ Day Qualifier Women
58 California-Santa Cruz Loss 4-10 766.65 Feb 1st Presidents’ Day Qualifier Women
142 Colorado-B Win 7-6 920 Feb 1st Presidents’ Day Qualifier Women
148 Arizona State Win 10-3 1333.56 Feb 2nd Presidents’ Day Qualifier Women
96 Occidental Loss 7-8 963.5 Feb 2nd Presidents’ Day Qualifier Women
58 California-Santa Cruz Loss 4-8 801.84 Feb 2nd Presidents’ Day Qualifier Women
145 UCLA-B Win 9-4 1366.28 Feb 29th 2nd Annual Claremont Ultimate Classic
96 Occidental Loss 5-7 760.36 Feb 29th 2nd Annual Claremont Ultimate Classic
161 Claremont Win 8-7 714.87 Feb 29th 2nd Annual Claremont Ultimate Classic
140 Santa Clara Win 9-4 1402.09 Mar 7th Santa Clara Rage Home Tournament 2020
196 California-B** Win 9-3 902.06 Ignored Mar 7th Santa Clara Rage Home Tournament 2020
144 Nevada-Reno Win 7-6 901.46 Mar 7th Santa Clara Rage Home Tournament 2020
86 San Diego State University Loss 5-8 720.51 Mar 7th Santa Clara Rage Home Tournament 2020
163 Sonoma State Win 8-6 863.02 Mar 8th Santa Clara Rage Home Tournament 2020
96 Occidental Loss 2-7 488.5 Mar 8th Santa Clara Rage Home Tournament 2020
86 San Diego State University Loss 6-10 677.95 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)