#4 California-Santa Barbara (16-4)

avg: 2280.49  •  sd: 66.86  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
32 Brigham Young Win 13-5 2310.48 Jan 25th Santa Barbara Invite 2019
90 Colorado State** Win 13-5 1817.13 Ignored Jan 26th Santa Barbara Invite 2019
39 California-Davis Win 13-7 2150.81 Jan 26th Santa Barbara Invite 2019
37 Washington University Win 13-9 2089.12 Jan 26th Santa Barbara Invite 2019
23 California Win 13-9 2336.48 Jan 27th Santa Barbara Invite 2019
15 Wisconsin Win 13-9 2440.52 Jan 27th Santa Barbara Invite 2019
2 California-San Diego Loss 9-13 2000.5 Jan 27th Santa Barbara Invite 2019
34 Colorado College Win 9-3 2303.78 Feb 16th Presidents Day Invite 2019
86 San Diego State** Win 10-3 1842.2 Ignored Feb 16th Presidents Day Invite 2019
9 Texas Win 10-4 2744 Feb 17th Presidents Day Invite 2019
14 Colorado Win 11-8 2412.46 Feb 17th Presidents Day Invite 2019
13 Stanford Win 11-5 2655.63 Feb 17th Presidents Day Invite 2019
17 Vermont Win 9-6 2431.76 Feb 18th Presidents Day Invite 2019
2 California-San Diego Loss 4-11 1819.06 Feb 18th Presidents Day Invite 2019
14 Colorado Win 13-5 2646.85 Mar 2nd Stanford Invite 2019
39 California-Davis Win 10-5 2167.18 Mar 2nd Stanford Invite 2019
24 Washington Win 13-6 2472.59 Mar 2nd Stanford Invite 2019
21 Cal Poly-SLO Win 13-8 2439.75 Mar 3rd Stanford Invite 2019
19 UCLA Loss 9-10 1840.86 Mar 3rd Stanford Invite 2019
5 Carleton College-Syzygy Loss 8-12 1824.34 Mar 3rd Stanford 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)