#12 Virginia (16-2)

avg: 2035.27  •  sd: 101.77  •  top 16/20: 93.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
65 Liberty** Win 13-1 1914.23 Ignored Jan 25th Winta Binta Vinta Fest 2020
166 West Virginia** Win 11-3 1155.33 Ignored Jan 25th Winta Binta Vinta Fest 2020
32 William & Mary Win 11-4 2240.57 Jan 25th Winta Binta Vinta Fest 2020
34 James Madison Win 13-4 2232.11 Jan 26th Winta Binta Vinta Fest 2020
65 Liberty** Win 14-2 1914.23 Ignored Jan 26th Winta Binta Vinta Fest 2020
217 Virginia-B** Win 15-1 679.69 Ignored Jan 26th Winta Binta Vinta Fest 2020
28 Michigan Win 9-8 1855.66 Feb 8th Queen City Tune Up 2020 Women
1 Carleton College Loss 6-13 1914.9 Feb 8th Queen City Tune Up 2020 Women
46 Pennsylvania Win 10-7 1860.06 Feb 8th Queen City Tune Up 2020 Women
56 North Carolina-Wilmington** Win 12-4 1984.72 Ignored Feb 8th Queen City Tune Up 2020 Women
38 Duke Win 10-8 1820.32 Feb 9th Queen City Tune Up 2020 Women
129 Harvard** Win 13-5 1462.04 Ignored Feb 22nd Commonwealth Cup 2020 Weekend 2
46 Pennsylvania Win 13-3 2070.4 Feb 22nd Commonwealth Cup 2020 Weekend 2
55 Columbia** Win 12-5 1990.42 Ignored Feb 22nd Commonwealth Cup 2020 Weekend 2
21 Vermont Win 10-6 2421.69 Feb 23rd Commonwealth Cup 2020 Weekend 2
25 Georgia Win 13-12 1900.26 Feb 23rd Commonwealth Cup 2020 Weekend 2
2 North Carolina Loss 8-13 1990.35 Feb 23rd Commonwealth Cup 2020 Weekend 2
33 North Carolina State Win 14-9 2112.17 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)