#19 UCLA (14-13)

avg: 1965.86  •  sd: 48.63  •  top 16/20: 69%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
21 Cal Poly-SLO Loss 12-14 1722.64 Jan 26th Santa Barbara Invite 2019
37 Washington University Win 13-7 2228.09 Jan 26th Santa Barbara Invite 2019
13 Stanford Loss 11-13 1826.79 Jan 26th Santa Barbara Invite 2019
42 Chicago Win 13-10 1892.64 Jan 26th Santa Barbara Invite 2019
21 Cal Poly-SLO Loss 14-15 1818.59 Jan 27th Santa Barbara Invite 2019
24 Washington Loss 10-12 1634.46 Jan 27th Santa Barbara Invite 2019
13 Stanford Win 12-11 2180.63 Jan 27th Santa Barbara Invite 2019
9 Texas Loss 4-7 1647.84 Feb 16th Presidents Day Invite 2019
10 Northeastern Loss 5-8 1654.34 Feb 16th Presidents Day Invite 2019
29 Northwestern Win 9-3 2367.62 Feb 17th Presidents Day Invite 2019
30 Utah Win 8-6 2059.36 Feb 17th Presidents Day Invite 2019
23 California Win 8-5 2371.52 Feb 18th Presidents Day Invite 2019
86 San Diego State** Win 12-5 1842.2 Ignored Feb 18th Presidents Day Invite 2019
16 Oregon Loss 7-8 1892.73 Mar 2nd Stanford Invite 2019
50 Whitman Win 13-5 2108.9 Mar 2nd Stanford Invite 2019
24 Washington Win 12-6 2451.9 Mar 2nd Stanford Invite 2019
5 Carleton College-Syzygy Loss 3-11 1665.5 Mar 2nd Stanford Invite 2019
23 California Win 9-7 2197.25 Mar 3rd Stanford Invite 2019
2 California-San Diego Loss 3-13 1819.06 Mar 3rd Stanford Invite 2019
4 California-Santa Barbara Win 10-9 2405.49 Mar 3rd Stanford Invite 2019
10 Northeastern Loss 8-9 1982.95 Mar 23rd Womens College Centex 2019
15 Wisconsin Loss 10-11 1896.96 Mar 23rd Womens College Centex 2019
42 Chicago Win 10-7 1954.16 Mar 23rd Womens College Centex 2019
9 Texas Loss 7-13 1586.47 Mar 24th Womens College Centex 2019
40 Michigan Win 12-10 1807.56 Mar 24th Womens College Centex 2019
12 Minnesota Loss 10-12 1831.59 Mar 24th Womens College Centex 2019
15 Wisconsin Win 10-9 2146.96 Mar 24th Womens College Centex 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)