#2 California-San Diego (23-3)

avg: 2419.06  •  sd: 54.58  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
23 California Win 13-3 2517.92 Jan 26th Santa Barbara Invite 2019
15 Wisconsin Win 13-8 2518.12 Jan 26th Santa Barbara Invite 2019
84 Victoria** Win 13-4 1845.36 Ignored Jan 26th Santa Barbara Invite 2019
24 Washington Win 13-7 2430.12 Jan 27th Santa Barbara Invite 2019
13 Stanford Win 13-4 2655.63 Jan 27th Santa Barbara Invite 2019
4 California-Santa Barbara Win 13-9 2699.06 Jan 27th Santa Barbara Invite 2019
29 Northwestern** Win 13-5 2367.62 Ignored Feb 16th Presidents Day Invite 2019
7 Western Washington Win 9-5 2728.73 Feb 16th Presidents Day Invite 2019
16 Oregon Win 12-7 2538.24 Feb 17th Presidents Day Invite 2019
10 Northeastern Win 11-6 2654.64 Feb 17th Presidents Day Invite 2019
17 Vermont Win 10-3 2613.2 Feb 17th Presidents Day Invite 2019
21 Cal Poly-SLO Win 12-7 2464.1 Feb 18th Presidents Day Invite 2019
4 California-Santa Barbara Win 11-4 2880.49 Feb 18th Presidents Day Invite 2019
9 Texas Win 13-8 2640.16 Mar 2nd Stanford Invite 2019
32 Brigham Young Win 13-9 2129.05 Mar 2nd Stanford Invite 2019
13 Stanford Win 12-10 2293.76 Mar 2nd Stanford Invite 2019
14 Colorado Win 12-10 2284.98 Mar 3rd Stanford Invite 2019
19 UCLA Win 13-3 2565.86 Mar 3rd Stanford Invite 2019
5 Carleton College-Syzygy Win 13-6 2865.5 Mar 3rd Stanford Invite 2019
1 North Carolina Loss 8-15 1965.26 Mar 29th NW Challenge Tier 1 Womens
6 British Columbia Loss 11-15 1850.6 Mar 29th NW Challenge Tier 1 Womens
3 Ohio State Loss 12-14 2152.04 Mar 30th NW Challenge Tier 1 Womens
16 Oregon Win 11-10 2142.73 Mar 30th NW Challenge Tier 1 Womens
8 Dartmouth Win 12-10 2396.97 Mar 30th NW Challenge Tier 1 Womens
7 Western Washington Win 14-10 2598.37 Mar 31st NW Challenge Tier 1 Womens
13 Stanford Win 11-9 2304.84 Mar 31st NW Challenge Tier 1 Womens
**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)