#100 California-Santa Cruz (11-11)

avg: 1358.77  •  sd: 41.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
5 Cal Poly-SLO Loss 6-13 1544.46 Jan 26th Santa Barbara Invite 2019
51 Western Washington Loss 11-13 1400.92 Jan 26th Santa Barbara Invite 2019
40 Dartmouth Loss 7-13 1128.94 Jan 26th Santa Barbara Invite 2019
29 Texas-Dallas Loss 9-13 1353.34 Jan 26th Santa Barbara Invite 2019
219 Michigan State Win 13-11 1151.67 Jan 27th Santa Barbara Invite 2019
265 Cal State-Long Beach Win 13-1 1400.7 Feb 2nd Presidents Day Qualifiers Men
344 California-Irvine Win 10-5 1080.17 Feb 2nd Presidents Day Qualifiers Men
395 California-San Diego-C** Win 11-0 859.32 Ignored Feb 2nd Presidents Day Qualifiers Men
261 Cal Poly-SLO-B Win 13-8 1317.3 Feb 3rd Presidents Day Qualifiers Men
169 Chico State Win 11-8 1449.91 Feb 3rd Presidents Day Qualifiers Men
244 Colorado-B Win 13-10 1205.34 Feb 3rd Presidents Day Qualifiers Men
74 Arizona Win 13-10 1807.23 Feb 9th Stanford Open 2019
41 Las Positas Loss 11-13 1449.69 Feb 9th Stanford Open 2019
99 Lewis & Clark Win 11-9 1607.98 Feb 9th Stanford Open 2019
241 Washington-B Win 11-5 1488.48 Feb 9th Stanford Open 2019
93 California-Davis Loss 4-5 1252.55 Feb 10th Stanford Open 2019
3 Oregon** Loss 3-13 1588.99 Ignored Feb 16th Presidents Day Invite 2019
51 Western Washington Loss 6-8 1329.27 Feb 16th Presidents Day Invite 2019
34 UCLA Loss 1-12 1128.73 Feb 17th Presidents Day Invite 2019
76 Utah Loss 7-8 1348.73 Feb 17th Presidents Day Invite 2019
271 San Diego State Win 11-5 1380.82 Feb 18th Presidents Day Invite 2019
90 Santa Clara Loss 9-10 1261.86 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)