#36 California-Santa Cruz (18-9)

avg: 1628.84  •  sd: 68.6  •  top 16/20: 0.2%

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# Opponent Result Game Rating Status Date Event
- California-San Diego-C** Win 13-0 151.29 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
203 Cal Poly-SLO-B** Win 10-2 1423.79 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
214 California-San Diego-B** Win 10-3 1380.34 Ignored Feb 2nd 2020 Mens Presidents Day Qualifier
185 Cal State-Long Beach** Win 11-4 1500.75 Ignored Feb 2nd 2020 Mens Presidents Day Qualifier
148 Sonoma State** Win 12-4 1607.81 Ignored Feb 2nd 2020 Mens Presidents Day Qualifier
40 Santa Clara Win 9-7 1891.25 Feb 2nd 2020 Mens Presidents Day Qualifier
203 Cal Poly-SLO-B Win 13-8 1319.95 Feb 8th Stanford Open 2020
88 Claremont Win 10-8 1546.59 Feb 8th Stanford Open 2020
168 Lewis & Clark Win 12-6 1544.61 Feb 8th Stanford Open 2020
46 Arizona Win 8-5 2012.44 Feb 9th Stanford Open 2020
75 Nevada-Reno Win 10-7 1750.35 Feb 9th Stanford Open 2020
27 Western Washington Win 8-7 1844.07 Feb 9th Stanford Open 2020
39 California-San Diego Win 9-8 1742.36 Feb 15th Presidents Day Invite 2020
6 Oregon Loss 9-15 1578.57 Feb 15th Presidents Day Invite 2020
90 Southern California Win 14-6 1870.22 Feb 15th Presidents Day Invite 2020
5 Colorado Loss 8-11 1787.97 Feb 16th Presidents Day Invite 2020
2 Washington Loss 8-14 1740.42 Feb 16th Presidents Day Invite 2020
37 Oklahoma State Loss 9-12 1274.9 Feb 16th Presidents Day Invite 2020
19 Oregon State Loss 8-13 1331.96 Feb 16th Presidents Day Invite 2020
39 California-San Diego Win 10-9 1742.36 Feb 17th Presidents Day Invite 2020
28 California-Santa Barbara Loss 6-11 1169.79 Feb 17th Presidents Day Invite 2020
5 Colorado Loss 8-11 1787.97 Mar 7th Stanford Invite 2020
16 UCLA Loss 9-10 1744.83 Mar 7th Stanford Invite 2020
18 Tufts Loss 6-12 1264.34 Mar 7th Stanford Invite 2020
42 Utah Win 11-10 1724.84 Mar 8th Stanford Invite 2020
59 Whitman Win 11-10 1565.12 Mar 8th Stanford Invite 2020
90 Southern California Win 12-8 1711.37 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)