#26 North Carolina-Wilmington (15-11)

avg: 1780.98  •  sd: 57.72  •  top 16/20: 5.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
102 Georgetown Win 10-5 1925.08 Jan 26th Carolina Kickoff 2019
81 Georgia Tech Loss 8-9 1322.32 Jan 26th Carolina Kickoff 2019
61 Tennessee Win 10-4 2154.19 Jan 26th Carolina Kickoff 2019
69 Emory Win 15-9 2023.94 Jan 27th Carolina Kickoff 2019
52 Notre Dame Win 15-10 2080.27 Jan 27th Carolina Kickoff 2019
1 North Carolina Loss 7-15 1631.92 Jan 27th Carolina Kickoff 2019
131 Chicago Win 12-6 1845.81 Feb 9th Queen City Tune Up 2019 Men
24 Auburn Win 12-9 2142.14 Feb 9th Queen City Tune Up 2019 Men
64 Ohio Win 10-6 2035.56 Feb 9th Queen City Tune Up 2019 Men
44 Virginia Win 9-7 1950.75 Feb 9th Queen City Tune Up 2019 Men
47 Maryland Win 15-14 1781.33 Feb 10th Queen City Tune Up 2019 Men
1 North Carolina Loss 10-15 1778.32 Feb 10th Queen City Tune Up 2019 Men
11 North Carolina State Loss 11-15 1646.4 Feb 10th Queen City Tune Up 2019 Men
57 Carnegie Mellon Win 13-4 2187.38 Mar 9th Classic City Invite 2019
22 Georgia Win 13-10 2162.64 Mar 9th Classic City Invite 2019
81 Georgia Tech Win 13-8 1943.48 Mar 9th Classic City Invite 2019
28 Northeastern Win 13-10 2103.97 Mar 9th Classic City Invite 2019
9 Massachusetts Loss 7-10 1675.83 Mar 10th Classic City Invite 2019
11 North Carolina State Win 9-8 2152.57 Mar 10th Classic City Invite 2019
4 Pittsburgh Loss 4-13 1584.92 Mar 30th Easterns 2019 Men
49 Northwestern Loss 11-13 1408.85 Mar 30th Easterns 2019 Men
32 William & Mary Loss 8-11 1381.07 Mar 30th Easterns 2019 Men
20 Tufts Loss 11-13 1635.31 Mar 30th Easterns 2019 Men
43 Harvard Loss 9-12 1326.91 Mar 31st Easterns 2019 Men
28 Northeastern Loss 8-14 1239.8 Mar 31st Easterns 2019 Men
44 Virginia Win 12-10 1909.54 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)