#20 North Carolina State (16-11)

avg: 1945.4  •  sd: 44.67  •  top 16/20: 56.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
142 Carleton College-CHOP** Win 15-5 1783.5 Ignored Jan 28th Carolina Kickoff
84 Richmond Win 15-9 1965.4 Jan 28th Carolina Kickoff
24 North Carolina-Charlotte Win 14-10 2293.18 Jan 28th Carolina Kickoff
27 South Carolina Win 15-11 2229.34 Jan 28th Carolina Kickoff
45 Georgetown Win 15-12 1997.18 Jan 29th Carolina Kickoff
1 North Carolina Loss 8-15 1827.85 Jan 29th Carolina Kickoff
24 North Carolina-Charlotte Win 15-10 2348.08 Jan 29th Carolina Kickoff
90 Chicago Win 15-5 2033.78 Feb 11th Queen City Tune Up1
36 Penn State Win 15-5 2377.62 Feb 11th Queen City Tune Up1
25 North Carolina-Wilmington Win 11-9 2133.46 Feb 11th Queen City Tune Up1
22 Washington University Win 12-11 2030.32 Feb 11th Queen City Tune Up1
13 Tufts Loss 10-11 1943.22 Feb 12th Queen City Tune Up1
30 Ohio State Win 13-8 2332.13 Feb 12th Queen City Tune Up1
3 Massachusetts Loss 6-13 1711.41 Mar 4th Smoky Mountain Invite
107 Tennessee Win 13-7 1899.25 Mar 4th Smoky Mountain Invite
4 Texas Loss 8-11 1849.14 Mar 4th Smoky Mountain Invite
15 UCLA Loss 10-11 1903.29 Mar 4th Smoky Mountain Invite
72 Auburn Win 12-9 1843.16 Mar 5th Smoky Mountain Invite
12 Minnesota Loss 12-14 1849.96 Mar 5th Smoky Mountain Invite
21 Northeastern Loss 12-15 1606.68 Mar 5th Smoky Mountain Invite
72 Auburn Win 12-7 2018.31 Apr 1st Easterns 2023
3 Massachusetts Loss 8-12 1870.26 Apr 1st Easterns 2023
8 Pittsburgh Loss 6-12 1575.87 Apr 1st Easterns 2023
18 California Loss 8-13 1465.41 Apr 1st Easterns 2023
13 Tufts Win 14-13 2193.22 Apr 2nd Easterns 2023
18 California Loss 11-12 1836.57 Apr 2nd Easterns 2023
21 Northeastern Win 12-11 2032.17 Apr 2nd Easterns 2023
**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)