#93 California-Davis (6-5)

avg: 1377.55  •  sd: 96.21  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
- Stanford-B** Win 13-2 600 Ignored Feb 9th Stanford Open 2019
169 Chico State Win 10-7 1473.97 Feb 9th Stanford Open 2019
175 North Texas Win 13-8 1563.26 Feb 9th Stanford Open 2019
90 Santa Clara Loss 4-9 786.86 Feb 10th Stanford Open 2019
100 California-Santa Cruz Win 5-4 1483.77 Feb 10th Stanford Open 2019
21 California Loss 6-11 1296.77 Feb 16th Presidents Day Invite 2019
8 Colorado Loss 6-12 1516.13 Feb 16th Presidents Day Invite 2019
271 San Diego State Win 9-4 1380.82 Feb 17th Presidents Day Invite 2019
90 Santa Clara Win 6-5 1511.86 Feb 17th Presidents Day Invite 2019
76 Utah Loss 6-8 1173.23 Feb 18th Presidents Day Invite 2019
45 California-Santa Barbara Loss 7-8 1538.25 Feb 18th Presidents Day 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)