#29 California (8-14)

avg: 1680.52  •  sd: 53.4  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
14 UCLA Loss 6-13 1388.29 Jan 25th Santa Barbara Invite 2020
4 California-San Diego Loss 7-13 1680.12 Jan 25th Santa Barbara Invite 2020
5 Washington Loss 10-12 1907.4 Jan 25th Santa Barbara Invite 2020
52 Victoria Win 11-10 1533.07 Jan 25th Santa Barbara Invite 2020
35 Utah Win 12-7 2095.07 Jan 26th Santa Barbara Invite 2020
27 California-Davis Loss 7-11 1278.58 Jan 26th Santa Barbara Invite 2020
14 UCLA Loss 8-9 1863.29 Feb 15th Presidents Day Invite 2020
4 California-San Diego Loss 7-11 1770.76 Feb 15th Presidents Day Invite 2020
19 Colorado Win 11-9 2185.25 Feb 15th Presidents Day Invite 2020
44 Whitman Win 9-8 1651.67 Feb 15th Presidents Day Invite 2020
9 Stanford Loss 7-12 1584.63 Feb 16th Presidents Day Invite 2020
10 California-Santa Barbara Loss 7-13 1484.36 Feb 16th Presidents Day Invite 2020
19 Colorado Win 11-10 2061.04 Feb 16th Presidents Day Invite 2020
14 UCLA Loss 8-11 1622.68 Feb 17th Presidents Day Invite 2020
16 Western Washington Loss 8-11 1596.56 Feb 17th Presidents Day Invite 2020
45 Chicago Win 9-8 1642.53 Feb 17th Presidents Day Invite 2020
3 Tufts Loss 6-13 1668.54 Mar 7th Stanford Invite 2020
17 British Columbia Loss 4-10 1354.58 Mar 7th Stanford Invite 2020
60 Oregon Loss 8-9 1221.76 Mar 7th Stanford Invite 2020
39 Cal Poly-SLO Win 11-8 1922.59 Mar 8th Stanford Invite 2020
58 California-Santa Cruz Win 9-6 1785.21 Mar 8th Stanford Invite 2020
23 Minnesota Loss 7-8 1658.65 Mar 8th Stanford 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)