#39 California-Davis (13-9)

avg: 1593.28  •  sd: 86.35  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
90 Colorado State Win 11-7 1684.02 Jan 26th Santa Barbara Invite 2019
4 California-Santa Barbara Loss 7-13 1722.96 Jan 26th Santa Barbara Invite 2019
37 Washington University Loss 7-8 1545.56 Jan 26th Santa Barbara Invite 2019
13 Stanford Loss 8-13 1559.47 Jan 26th Santa Barbara Invite 2019
37 Washington University Win 10-8 1933.22 Jan 27th Santa Barbara Invite 2019
48 California-Santa Cruz Win 10-9 1645.82 Jan 27th Santa Barbara Invite 2019
275 Cal Poly SLO-B** Win 13-0 330.79 Ignored Feb 2nd Presidents Day Qualifiers Women
246 California-B** Win 13-1 780.65 Ignored Feb 2nd Presidents Day Qualifiers Women
187 California-San Diego-B** Win 10-4 1198.15 Ignored Feb 2nd Presidents Day Qualifiers Women
107 Chico State Win 13-8 1572.55 Feb 2nd Presidents Day Qualifiers Women
23 California Loss 8-9 1792.92 Feb 3rd Presidents Day Qualifiers Women
119 UCLA-B Win 12-6 1615.98 Feb 3rd Presidents Day Qualifiers Women
119 UCLA-B Win 10-1 1636.67 Feb 3rd Presidents Day Qualifiers Women
- Humboldt State** Win 13-2 912.42 Ignored Feb 9th Stanford Open 2019
48 California-Santa Cruz Loss 9-10 1395.82 Feb 9th Stanford Open 2019
107 Chico State Win 9-8 1201.39 Feb 9th Stanford Open 2019
129 Pacific Lutheran Win 6-5 1124.54 Feb 10th Stanford Open 2019
14 Colorado Loss 10-12 1808.73 Mar 2nd Stanford Invite 2019
4 California-Santa Barbara Loss 5-10 1706.59 Mar 2nd Stanford Invite 2019
24 Washington Loss 7-11 1405.69 Mar 2nd Stanford Invite 2019
38 Florida Loss 7-8 1486.11 Mar 3rd Stanford Invite 2019
50 Whitman Win 11-9 1758.11 Mar 3rd Stanford Invite 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)