#61 California-Davis (9-16)

avg: 1817.92  •  sd: 68.34  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
17 California-Santa Barbara Loss 5-13 1720.26 Jan 27th Santa Barbara Invitational 2018
26 California Loss 4-12 1533.23 Jan 27th Santa Barbara Invitational 2018
38 Victoria Loss 8-13 1524.04 Jan 27th Santa Barbara Invitational 2018
87 California-Santa Cruz Win 9-4 2187.75 Jan 28th Santa Barbara Invitational 2018
43 Southern California Loss 9-12 1644.92 Jan 28th Santa Barbara Invitational 2018
65 Utah Win 9-7 2061.25 Feb 10th Stanford Open 2018
77 Brown Win 7-6 1813.92 Feb 10th Stanford Open 2018
249 California-B** Win 10-2 1024.8 Ignored Feb 10th Stanford Open 2018
65 Utah Win 12-3 2381.92 Feb 11th Stanford Open 2018
87 California-Santa Cruz Win 13-6 2187.75 Feb 11th Stanford Open 2018
73 San Diego State Win 10-6 2219.89 Feb 11th Stanford Open 2018
16 Western Washington Loss 3-13 1744.39 Feb 17th Presidents Day Invitational Tournament 2018
17 California-Santa Barbara Loss 5-13 1720.26 Feb 17th Presidents Day Invitational Tournament 2018
11 Texas** Loss 5-12 1871.75 Ignored Feb 17th Presidents Day Invitational Tournament 2018
22 Minnesota Win 9-7 2508.17 Feb 17th Presidents Day Invitational Tournament 2018
79 Chicago Loss 6-8 1352.89 Feb 18th Presidents Day Invitational Tournament 2018
43 Southern California Loss 9-11 1741.08 Feb 18th Presidents Day Invitational Tournament 2018
105 Chico State Win 9-8 1604.34 Feb 19th Presidents Day Invitational Tournament 2018
36 Colorado College Loss 0-15 1433.18 Feb 19th Presidents Day Invitational Tournament 2018
13 Ohio State Loss 3-13 1804.92 Mar 3rd Stanford Invite 2018
16 Western Washington Loss 5-13 1744.39 Mar 3rd Stanford Invite 2018
4 Stanford Loss 8-13 2199.36 Mar 3rd Stanford Invite 2018
14 Whitman Loss 5-13 1786.14 Mar 4th Stanford Invite 2018
26 California Loss 4-13 1533.23 Mar 4th Stanford Invite 2018
43 Southern California Loss 5-9 1461.22 Mar 4th Stanford Invite 2018
**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)