#38 Duke (6-5)

avg: 1557.66  •  sd: 84.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 Georgia Win 9-8 1900.26 Feb 8th Queen City Tune Up 2020 Women
84 Notre Dame Win 8-5 1641.99 Feb 8th Queen City Tune Up 2020 Women
83 Clemson Win 10-3 1802.24 Feb 8th Queen City Tune Up 2020 Women
36 Brigham Young Loss 7-11 1100.25 Feb 8th Queen City Tune Up 2020 Women
12 Virginia Loss 8-10 1772.61 Feb 9th Queen City Tune Up 2020 Women
89 Brown Win 11-5 1735.99 Feb 22nd Commonwealth Cup 2020 Weekend 2
33 North Carolina State Loss 8-9 1513.31 Feb 22nd Commonwealth Cup 2020 Weekend 2
15 Florida Loss 7-11 1511.31 Feb 22nd Commonwealth Cup 2020 Weekend 2
82 Oberlin Loss 9-11 968.81 Feb 22nd Commonwealth Cup 2020 Weekend 2
129 Harvard** Win 15-3 1462.04 Ignored Feb 23rd Commonwealth Cup 2020 Weekend 2
89 Brown Win 15-2 1735.99 Feb 23rd Commonwealth Cup 2020 Weekend 2
**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)