#25 North Carolina-Wilmington (13-4)

avg: 1694.13  •  sd: 63.52  •  top 16/20: 17.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
95 Chicago Win 12-7 1735.22 Feb 11th Queen City Tune Up1
42 Penn State Win 13-12 1650.71 Feb 11th Queen City Tune Up1
15 North Carolina State Loss 9-11 1556.08 Feb 11th Queen City Tune Up1
35 Washington University Win 14-13 1734.29 Feb 11th Queen City Tune Up1
47 Case Western Reserve Win 10-9 1580.38 Feb 12th Queen City Tune Up1
17 South Carolina Win 11-8 2139.48 Feb 12th Queen City Tune Up1
67 Maryland Win 12-9 1715.4 Feb 25th Easterns Qualifier 2023
106 Florida State Win 13-7 1717.98 Feb 25th Easterns Qualifier 2023
40 Duke Loss 11-12 1405.59 Feb 25th Easterns Qualifier 2023
70 Notre Dame Loss 11-12 1230.69 Feb 25th Easterns Qualifier 2023
48 Cornell Win 11-5 2053.75 Feb 26th Easterns Qualifier 2023
51 James Madison Win 12-10 1675.33 Feb 26th Easterns Qualifier 2023
24 North Carolina-Charlotte Loss 8-12 1259.27 Feb 26th Easterns Qualifier 2023
122 Carnegie Mellon Win 9-7 1361.61 Mar 4th Fish Bowl
42 Penn State Win 11-6 2072.41 Mar 4th Fish Bowl
42 Penn State Win 11-6 2072.41 Mar 5th Fish Bowl
83 Delaware Win 15-6 1872.96 Mar 5th Fish Bowl
**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)