#181 Virginia-B (1-10)

avg: 461.64  •  sd: 155.48  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Georgetown** Loss 4-10 919.65 Ignored Jan 27th Winta Binta Vinta 2024
70 James Madison** Loss 4-10 819.57 Ignored Jan 27th Winta Binta Vinta 2024
123 Liberty Loss 1-9 378.52 Jan 27th Winta Binta Vinta 2024
21 Ohio State** Loss 0-13 1482.16 Ignored Jan 27th Winta Binta Vinta 2024
70 James Madison Loss 4-5 1294.57 Jan 28th Winta Binta Vinta 2024
88 Virginia Tech** Loss 1-12 697.26 Ignored Jan 28th Winta Binta Vinta 2024
191 Elon Win 9-4 947.39 Feb 17th Commonwealth Cup Weekend 1 2024
151 George Washington Loss 5-7 463.09 Feb 17th Commonwealth Cup Weekend 1 2024
66 Tennessee** Loss 2-13 834.85 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
162 South Carolina-B Loss 1-13 40.77 Feb 18th Commonwealth Cup Weekend 1 2024
167 Wake Forest Loss 2-9 -25.48 Feb 18th Commonwealth Cup Weekend 1 2024
**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)