#1 North Carolina (23-2)

avg: 2231.92  •  sd: 45.67  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
62 Duke Win 13-7 2108.54 Jan 25th Carolina Kickoff 2019
85 Richmond** Win 13-4 2029.7 Ignored Jan 26th Carolina Kickoff 2019
78 Carleton College-GoP** Win 13-2 2057.72 Ignored Jan 26th Carolina Kickoff 2019
55 Florida State Win 15-9 2127.15 Jan 27th Carolina Kickoff 2019
26 North Carolina-Wilmington Win 15-7 2380.98 Jan 27th Carolina Kickoff 2019
11 North Carolina State Win 15-12 2328.06 Jan 27th Carolina Kickoff 2019
94 Appalachian State** Win 13-2 1972.43 Ignored Feb 9th Queen City Tune Up 2019 Men
40 Dartmouth Win 13-7 2244 Feb 9th Queen City Tune Up 2019 Men
14 Ohio State Win 11-10 2117.06 Feb 9th Queen City Tune Up 2019 Men
61 Tennessee** Win 13-5 2154.19 Ignored Feb 9th Queen City Tune Up 2019 Men
36 Alabama Win 15-7 2323.14 Feb 10th Queen City Tune Up 2019 Men
9 Massachusetts Win 15-8 2630.31 Feb 10th Queen City Tune Up 2019 Men
26 North Carolina-Wilmington Win 15-10 2234.58 Feb 10th Queen City Tune Up 2019 Men
50 Stanford Win 13-2 2232.74 Mar 2nd Stanford Invite 2019
13 Wisconsin Win 12-8 2442.12 Mar 2nd Stanford Invite 2019
30 Victoria Win 13-10 2094.04 Mar 2nd Stanford Invite 2019
2 Brown Win 13-8 2725.32 Mar 3rd Stanford Invite 2019
7 Carleton College-CUT Win 12-11 2243.64 Mar 3rd Stanford Invite 2019
12 Texas Win 13-6 2609.9 Mar 3rd Stanford Invite 2019
7 Carleton College-CUT Loss 11-13 1889.8 Mar 30th Easterns 2019 Men
43 Harvard Win 13-9 2090.84 Mar 30th Easterns 2019 Men
22 Georgia Win 13-7 2392.03 Mar 30th Easterns 2019 Men
54 Virginia Tech Win 13-11 1848.29 Mar 30th Easterns 2019 Men
2 Brown Loss 10-15 1775.55 Mar 31st Easterns 2019 Men
9 Massachusetts Win 15-12 2365.99 Mar 31st Easterns 2019 Men
**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)