#12 UCLA (10-7)

avg: 1656.35  •  sd: 52.12  •  top 16/20: 97.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
55 Case Western Reserve Win 13-7 1769.84 Jan 25th Santa Barbara Invite 2020
6 Brigham Young Loss 7-13 1279.46 Jan 25th Santa Barbara Invite 2020
14 British Columbia Win 13-11 1874.26 Jan 25th Santa Barbara Invite 2020
40 California-San Diego Win 12-11 1497.73 Jan 25th Santa Barbara Invite 2020
15 California Win 12-11 1758.04 Jan 26th Santa Barbara Invite 2020
37 Dartmouth Win 13-9 1810.88 Jan 26th Santa Barbara Invite 2020
1 Washington Loss 5-13 1542.78 Jan 26th Santa Barbara Invite 2020
27 California-Santa Barbara Win 11-7 1937.07 Feb 15th Presidents Day Invite 2020
3 Colorado Loss 7-12 1382.3 Feb 15th Presidents Day Invite 2020
18 Oregon State Win 12-10 1834.98 Feb 15th Presidents Day Invite 2020
4 Cal Poly-SLO Loss 8-11 1519.79 Feb 16th Presidents Day Invite 2020
40 California-San Diego Win 15-6 1972.73 Feb 16th Presidents Day Invite 2020
5 Oregon Loss 11-15 1479.67 Feb 16th Presidents Day Invite 2020
48 Stanford Win 9-7 1593.84 Feb 16th Presidents Day Invite 2020
3 Colorado Loss 8-10 1640.14 Feb 17th Presidents Day Invite 2020
18 Oregon State Loss 8-10 1334.19 Feb 17th Presidents Day Invite 2020
48 Stanford Win 14-6 1914.5 Feb 17th Presidents Day Invite 2020
**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)