#87 California-Santa Cruz (10-8)

avg: 1587.75  •  sd: 89.03  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
44 Colorado State Loss 11-13 1752.76 Jan 27th Santa Barbara Invitational 2018
4 Stanford** Loss 4-13 2095.52 Ignored Jan 27th Santa Barbara Invitational 2018
18 Brigham Young Loss 8-13 1790.35 Jan 27th Santa Barbara Invitational 2018
43 Southern California Loss 5-14 1390.28 Jan 28th Santa Barbara Invitational 2018
61 California-Davis Loss 4-9 1217.92 Jan 28th Santa Barbara Invitational 2018
105 Chico State Loss 8-10 1216.68 Feb 10th Stanford Open 2018
73 San Diego State Loss 6-7 1598.73 Feb 10th Stanford Open 2018
118 Lewis & Clark Win 7-5 1724.59 Feb 10th Stanford Open 2018
124 Carleton College-Eclipse Win 10-8 1615.19 Feb 10th Stanford Open 2018
61 California-Davis Loss 6-13 1217.92 Feb 11th Stanford Open 2018
138 Santa Clara Win 12-6 1838.85 Feb 11th Stanford Open 2018
105 Chico State Win 10-5 2053.24 Feb 11th Stanford Open 2018
242 California-Davis-B** Win 12-2 1110.9 Ignored Feb 18th Santa Clara Tournament 2018
200 Nevada-Reno** Win 11-2 1483.18 Ignored Feb 18th Santa Clara Tournament 2018
265 Cal Poly-SLO-B** Win 13-2 620.41 Ignored Feb 18th Santa Clara Tournament 2018
249 California-B** Win 13-2 1024.8 Ignored Feb 19th Santa Clara Tournament 2018
136 Cal State-Long Beach Win 7-5 1599.55 Feb 19th Santa Clara Tournament 2018
138 Santa Clara Win 11-8 1625.15 Feb 19th Santa Clara Tournament 2018
**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)