#34 Virginia (9-10)

avg: 1526.64  •  sd: 61.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
75 Virginia Tech Win 13-0 1590.56 Jan 27th Winta Binta Vinta 2024
50 William & Mary Loss 8-9 1200.95 Jan 27th Winta Binta Vinta 2024
55 Penn State Win 9-8 1419.64 Jan 27th Winta Binta Vinta 2024
103 Liberty** Win 9-0 1353.8 Ignored Jan 28th Winta Binta Vinta 2024
55 Penn State Win 7-5 1622.79 Jan 28th Winta Binta Vinta 2024
18 Ohio State Loss 5-9 1316.47 Jan 28th Winta Binta Vinta 2024
32 South Carolina Loss 4-13 967.57 Feb 10th Queen City Tune Up 2024
31 Wisconsin Loss 9-14 1109.53 Feb 10th Queen City Tune Up 2024
4 Vermont** Loss 2-15 1703.43 Ignored Feb 10th Queen City Tune Up 2024
50 William & Mary Win 12-5 1925.95 Feb 10th Queen City Tune Up 2024
71 Appalachian State Win 15-3 1686.4 Feb 11th Queen City Tune Up 2024
18 Ohio State Loss 9-12 1500.16 Feb 11th Queen City Tune Up 2024
14 Georgia Loss 9-10 1772.82 Feb 24th Commonwealth Cup Weekend 2 2024
16 Notre Dame Loss 8-15 1322.72 Feb 24th Commonwealth Cup Weekend 2 2024
6 Tufts** Loss 5-15 1625.03 Ignored Feb 24th Commonwealth Cup Weekend 2 2024
22 North Carolina State Win 9-8 1849.03 Feb 25th Commonwealth Cup Weekend 2 2024
18 Ohio State Loss 7-11 1378.63 Feb 25th Commonwealth Cup Weekend 2 2024
42 Yale Win 8-5 1909.92 Feb 25th Commonwealth Cup Weekend 2 2024
25 Ohio Win 8-7 1803.46 Feb 25th Commonwealth Cup Weekend 2 2024
**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)