#37 North Carolina-Wilmington (13-12)

avg: 1635.07  •  sd: 61.54  •  top 16/20: 0.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
96 Appalachian State Win 13-7 1831.95 Feb 1st Carolina Kickoff mens 2025
84 Ohio State Win 13-8 1815.83 Feb 1st Carolina Kickoff mens 2025
27 South Carolina Loss 10-13 1451.46 Feb 1st Carolina Kickoff mens 2025
3 North Carolina Loss 7-15 1606.12 Feb 2nd Carolina Kickoff mens 2025
49 North Carolina State Loss 12-14 1343.84 Feb 2nd Carolina Kickoff mens 2025
81 North Carolina-Charlotte Win 14-8 1858.29 Feb 2nd Carolina Kickoff mens 2025
104 Alabama Loss 11-13 1010.12 Feb 15th Queen City Tune Up 2025
75 Carnegie Mellon Win 10-9 1496.25 Feb 15th Queen City Tune Up 2025
27 South Carolina Loss 7-13 1222.07 Feb 15th Queen City Tune Up 2025
69 Auburn Win 8-6 1728.88 Feb 16th Queen City Tune Up 2025
51 Purdue Loss 8-9 1430.86 Feb 16th Queen City Tune Up 2025
44 Emory Loss 6-11 1061.5 Feb 22nd Easterns Qualifier 2025
88 Georgetown Win 13-9 1727.24 Feb 22nd Easterns Qualifier 2025
87 Temple Win 13-6 1910.92 Feb 22nd Easterns Qualifier 2025
102 Syracuse Win 9-8 1385.39 Feb 22nd Easterns Qualifier 2025
64 James Madison Win 15-6 2057.38 Feb 23rd Easterns Qualifier 2025
47 McGill Win 11-9 1840.64 Feb 23rd Easterns Qualifier 2025
52 William & Mary Win 13-9 1970.05 Feb 23rd Easterns Qualifier 2025
6 Cal Poly-SLO Loss 7-13 1570.09 Mar 29th Easterns 2025
4 Carleton College Loss 6-12 1623.99 Mar 29th Easterns 2025
21 Georgia Tech Loss 7-11 1387.9 Mar 29th Easterns 2025
28 Pittsburgh Win 13-8 2260.89 Mar 29th Easterns 2025
16 Brown Loss 7-15 1319.73 Mar 30th Easterns 2025
21 Georgia Tech Loss 9-10 1729.79 Mar 30th Easterns 2025
20 Vermont Win 12-11 1983.08 Mar 30th Easterns 2025
**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)