#20 North Carolina-Wilmington (11-6)

avg: 1824.97  •  sd: 43.07  •  top 16/20: 51%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
71 Kentucky Win 9-6 1792.31 Jan 25th Carolina Kickoff 2020
153 Florida State** Win 13-5 1603.71 Ignored Jan 25th Carolina Kickoff 2020
91 Indiana Win 13-7 1827.65 Jan 25th Carolina Kickoff 2020
81 North Carolina-Charlotte Win 14-7 1900.91 Jan 26th Carolina Kickoff 2020
21 North Carolina State Loss 10-13 1492.78 Jan 26th Carolina Kickoff 2020
26 South Carolina Win 12-11 1869.93 Jan 26th Carolina Kickoff 2020
51 Tennessee Win 10-7 1886.39 Feb 8th Queen City Tune Up 2020 Open
119 Emory Win 10-5 1707.96 Feb 8th Queen City Tune Up 2020 Open
23 William & Mary Win 11-9 2037.43 Feb 8th Queen City Tune Up 2020 Open
45 Notre Dame Loss 11-12 1448.57 Feb 9th Queen City Tune Up 2020 Open
11 Minnesota Loss 9-12 1647.41 Mar 7th Smoky Mountain Invite 2020
35 Northeastern Win 12-7 2151.86 Mar 7th Smoky Mountain Invite 2020
1 North Carolina Loss 6-11 1783.7 Mar 7th Smoky Mountain Invite 2020
29 Wisconsin Win 15-13 1919.13 Mar 7th Smoky Mountain Invite 2020
8 Massachusetts Loss 13-15 1846.08 Mar 8th Smoky Mountain Invite 2020
7 Ohio State Loss 13-14 1944.2 Mar 8th Smoky Mountain Invite 2020
31 Texas-Dallas Win 13-12 1822.27 Mar 8th Smoky Mountain Invite 2020
**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)