#130 Virginia-B (1-10)

avg: 265.8  •  sd: 142.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
64 James Madison** Loss 4-10 550.57 Ignored Jan 27th Winta Binta Vinta 2024
103 Liberty Loss 1-9 153.8 Jan 27th Winta Binta Vinta 2024
53 Georgetown** Loss 4-10 701.64 Ignored Jan 27th Winta Binta Vinta 2024
18 Ohio State** Loss 0-13 1245.53 Ignored Jan 27th Winta Binta Vinta 2024
64 James Madison Loss 4-5 1025.57 Jan 28th Winta Binta Vinta 2024
75 Virginia Tech** Loss 1-12 390.56 Ignored Jan 28th Winta Binta Vinta 2024
86 Tennessee** Loss 2-13 291.91 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
118 George Washington Loss 5-7 193.9 Feb 17th Commonwealth Cup Weekend 1 2024
133 Elon Win 9-4 797.49 Feb 17th Commonwealth Cup Weekend 1 2024
122 South Carolina-B Loss 1-13 -126.42 Feb 18th Commonwealth Cup Weekend 1 2024
123 Wake Forest Loss 2-9 -160.01 Feb 18th Commonwealth Cup Weekend 1 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)