#65 California-Santa Barbara (10-14)

avg: 1462.37  •  sd: 69.67  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
18 Brigham Young Loss 10-13 1525.24 Jan 26th Santa Barbara Invitational 2018
58 Kansas Win 13-11 1729.7 Jan 27th Santa Barbara Invitational 2018
111 Arizona State Loss 11-12 1164.21 Jan 27th Santa Barbara Invitational 2018
32 California Loss 8-10 1433.13 Jan 27th Santa Barbara Invitational 2018
5 Washington Loss 8-11 1685.8 Jan 27th Santa Barbara Invitational 2018
148 San Diego State Win 13-10 1475.22 Jan 28th Santa Barbara Invitational 2018
53 UCLA Win 13-10 1862.56 Jan 28th Santa Barbara Invitational 2018
90 Northern Arizona Win 9-7 1656.94 Feb 10th Stanford Open 2018
131 Chico State Loss 8-11 823.03 Feb 10th Stanford Open 2018
380 Stanford-B** Win 13-1 819.65 Ignored Feb 10th Stanford Open 2018
141 Boston College Win 11-8 1531.77 Feb 11th Stanford Open 2018
165 Humboldt State Loss 12-13 941.68 Feb 11th Stanford Open 2018
186 Cal Poly-Pomona Win 13-5 1583.1 Feb 11th Stanford Open 2018
20 Cal Poly-SLO Loss 7-15 1243.12 Feb 17th Presidents Day Invitational Tournament 2018
76 Chicago Win 11-9 1664.52 Feb 17th Presidents Day Invitational Tournament 2018
19 Colorado Loss 12-13 1725.95 Feb 17th Presidents Day Invitational Tournament 2018
55 Oregon State Win 11-10 1643.18 Feb 18th Presidents Day Invitational Tournament 2018
38 Southern California Loss 9-13 1215.33 Feb 18th Presidents Day Invitational Tournament 2018
148 San Diego State Loss 6-10 650.92 Feb 19th Presidents Day Invitational Tournament 2018
76 Chicago Win 14-8 1951.35 Feb 19th Presidents Day Invitational Tournament 2018
7 Pittsburgh Loss 8-15 1422.65 Mar 31st Easterns 2018
16 North Carolina-Wilmington Loss 11-12 1759.51 Mar 31st Easterns 2018
37 Central Florida Loss 11-13 1405.91 Mar 31st Easterns 2018
2 Carleton College** Loss 5-15 1628.2 Ignored Mar 31st Easterns 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)