#2 North Carolina (11-0)

avg: 2097.87  •  sd: 83.36  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
70 Duke** Win 13-3 1689.23 Ignored Jan 24th Carolina Kickoff 2020
83 Carleton College-GoP** Win 13-2 1624.96 Ignored Jan 25th Carolina Kickoff 2020
85 North Carolina-Charlotte** Win 13-4 1622.16 Ignored Jan 25th Carolina Kickoff 2020
80 Kentucky** Win 15-2 1654.52 Ignored Jan 26th Carolina Kickoff 2020
23 South Carolina Win 15-8 2090.12 Jan 26th Carolina Kickoff 2020
13 North Carolina State Win 14-10 2044.45 Jan 26th Carolina Kickoff 2020
- Carnegie Mellon** Win 13-4 2068.49 Ignored Feb 8th Queen City Tune Up 2020 Open
- Purdue Win 13-8 2109.81 Feb 8th Queen City Tune Up 2020 Open
- Temple** Win 13-2 2086.81 Ignored Feb 8th Queen City Tune Up 2020 Open
10 Georgia Win 13-7 2225.03 Feb 9th Queen City Tune Up 2020 Open
- Ohio State Win 11-8 2013.75 Feb 9th Queen City Tune Up 2020 Open
**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)