#51 California-Santa Cruz (11-6)

avg: 1347.54  •  sd: 64.35  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
24 Northwestern Loss 7-13 1171.16 Jan 25th Santa Barbara Invite 2020
16 Vermont Loss 8-9 1774.81 Jan 25th Santa Barbara Invite 2020
8 California-Santa Barbara Loss 8-13 1539.33 Jan 25th Santa Barbara Invite 2020
32 Brigham Young Loss 10-11 1424.89 Jan 25th Santa Barbara Invite 2020
52 Washington University Win 9-8 1469.18 Jan 26th Santa Barbara Invite 2020
35 Cal Poly-SLO Loss 5-10 931.74 Jan 26th Santa Barbara Invite 2020
97 California-San Diego-B Win 10-4 1449.55 Feb 1st Presidents’ Day Qualifier Women
128 Colorado-B** Win 12-4 1171.13 Ignored Feb 1st Presidents’ Day Qualifier Women
91 Chico State Win 10-6 1396.92 Feb 1st Presidents’ Day Qualifier Women
94 Occidental Win 11-9 1121.62 Feb 2nd Presidents’ Day Qualifier Women
97 California-San Diego-B Win 8-4 1414.36 Feb 2nd Presidents’ Day Qualifier Women
87 Cal State-Long Beach Loss 9-10 836.99 Feb 2nd Presidents’ Day Qualifier Women
125 Humboldt State** Win 13-1 1188.58 Ignored Feb 8th Stanford Open 2020
135 Lewis & Clark Win 13-6 1082.66 Feb 8th Stanford Open 2020
78 San Diego State University Win 13-4 1652.63 Feb 8th Stanford Open 2020
90 Santa Clara Win 7-5 1254.58 Feb 9th Stanford Open 2020
26 California-Davis Win 6-5 1789.28 Feb 9th Stanford Open 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)