#14 Virginia (22-5)

avg: 1930.66  •  sd: 39.61  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
51 Georgetown Win 9-5 1950.89 Jan 28th Winta Binta Vinta
41 James Madison Win 10-6 2020.18 Jan 28th Winta Binta Vinta
57 Virginia Tech Win 10-4 1937.15 Jan 28th Winta Binta Vinta
103 Virginia Commonwealth** Win 13-5 1572.7 Ignored Jan 28th Winta Binta Vinta
33 Ohio State Win 12-7 2154.41 Jan 29th Winta Binta Vinta
57 Virginia Tech Win 13-3 1937.15 Jan 29th Winta Binta Vinta
7 Carleton College Loss 7-13 1720.81 Feb 11th Queen City Tune Up1
38 Chicago Win 12-7 2087.73 Feb 11th Queen City Tune Up1
64 Appalachian State Win 13-6 1874.41 Feb 11th Queen City Tune Up1
60 Ohio Win 10-6 1795.5 Feb 11th Queen City Tune Up1
27 Minnesota Win 10-6 2181.41 Feb 12th Queen City Tune Up1
13 Pittsburgh Win 13-10 2261.49 Feb 12th Queen City Tune Up1
32 SUNY-Binghamton Loss 9-10 1526.59 Feb 25th Commonwealth Cup Weekend2 2023
89 Columbia Win 11-5 1678.66 Feb 25th Commonwealth Cup Weekend2 2023
10 Northeastern Loss 6-13 1534.33 Feb 25th Commonwealth Cup Weekend2 2023
59 Penn State Win 12-8 1742.4 Feb 25th Commonwealth Cup Weekend2 2023
10 Northeastern Loss 8-9 2009.33 Feb 26th Commonwealth Cup Weekend2 2023
33 Ohio State Win 13-8 2130.06 Feb 26th Commonwealth Cup Weekend2 2023
44 Pennsylvania Win 11-8 1849.92 Feb 26th Commonwealth Cup Weekend2 2023
13 Pittsburgh Win 9-8 2058.35 Feb 26th Commonwealth Cup Weekend2 2023
35 Michigan Win 12-9 1964.94 Mar 18th Womens Centex1
42 Wisconsin Win 13-8 2002.65 Mar 18th Womens Centex1
48 Texas Win 13-4 2059.95 Mar 18th Womens Centex1
36 Brown Win 13-5 2180.07 Mar 19th Womens Centex1
31 California Win 14-13 1782.96 Mar 19th Womens Centex1
18 Colorado State Loss 14-15 1686.5 Mar 19th Womens Centex1
33 Ohio State Win 13-8 2130.06 Mar 19th Womens Centex1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)