#37 California-San Diego (6-10)

avg: 1570.96  •  sd: 69.61  •  top 16/20: 0.1%

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# Opponent Result Game Rating Status Date Event
5 Brigham Young Loss 8-13 1569.54 Jan 25th Santa Barbara Invite 2020
13 British Columbia Loss 7-13 1290.78 Jan 25th Santa Barbara Invite 2020
12 UCLA Loss 11-12 1731.88 Jan 25th Santa Barbara Invite 2020
49 Case Western Reserve Win 13-8 1915.37 Jan 25th Santa Barbara Invite 2020
41 Santa Clara Win 12-5 2154.48 Jan 26th Santa Barbara Invite 2020
7 Colorado State University Loss 7-13 1399.84 Jan 26th Santa Barbara Invite 2020
43 Stanford Win 9-8 1642.3 Jan 26th Santa Barbara Invite 2020
6 Oregon Loss 9-13 1642.19 Feb 15th Presidents Day Invite 2020
30 California-Santa Cruz Loss 8-9 1481.84 Feb 15th Presidents Day Invite 2020
87 Southern California Win 11-5 1779.21 Feb 15th Presidents Day Invite 2020
17 Oregon State Loss 9-13 1382.12 Feb 16th Presidents Day Invite 2020
138 San Diego State Win 15-7 1520.15 Feb 16th Presidents Day Invite 2020
57 Illinois Win 13-6 1979.88 Feb 16th Presidents Day Invite 2020
12 UCLA Loss 6-15 1256.88 Feb 16th Presidents Day Invite 2020
35 Oklahoma State Loss 8-14 1047.25 Feb 17th Presidents Day Invite 2020
30 California-Santa Cruz Loss 9-10 1481.84 Feb 17th Presidents Day Invite 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)