#85 North Carolina-B (3-9)

avg: 439.03  •  sd: 83.44  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
30 Duke Loss 8-15 704 Jan 25th Carolina Kickoff 2025
100 Emory-B Win 15-0 498.64 Jan 25th Carolina Kickoff 2025
6 North Carolina** Loss 1-15 1175.39 Ignored Jan 25th Carolina Kickoff 2025
55 North Carolina State Loss 5-15 365.2 Jan 25th Carolina Kickoff 2025
47 Appalachian State Loss 5-11 444.53 Jan 26th Carolina Kickoff 2025
73 Emory Loss 7-10 168.58 Jan 26th Carolina Kickoff 2025
67 Cedarville Loss 6-7 566.55 Feb 15th 2025 Commonwealth Cup Weekend 1
65 Davidson Loss 4-7 241.47 Feb 15th 2025 Commonwealth Cup Weekend 1
40 Kenyon** Loss 2-11 482.36 Feb 15th 2025 Commonwealth Cup Weekend 1
80 Wake Forest Loss 4-6 121.56 Feb 15th 2025 Commonwealth Cup Weekend 1
94 Georgetown-B Win 11-1 695.52 Feb 16th 2025 Commonwealth Cup Weekend 1
105 Georgia-B** Win 11-0 148.83 Ignored Feb 16th 2025 Commonwealth Cup Weekend 1
**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)