#25 North Carolina-Wilmington (16-8)

avg: 1884.26  •  sd: 49.93  •  top 16/20: 12.3%

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# Opponent Result Game Rating Status Date Event
90 Chicago Win 12-7 1954.29 Feb 11th Queen City Tune Up1
36 Penn State Win 13-12 1902.62 Feb 11th Queen City Tune Up1
20 North Carolina State Loss 9-11 1696.19 Feb 11th Queen City Tune Up1
22 Washington University Win 14-13 2030.32 Feb 11th Queen City Tune Up1
50 Case Western Reserve Win 10-9 1765.01 Feb 12th Queen City Tune Up1
27 South Carolina Win 11-8 2213.79 Feb 12th Queen City Tune Up1
69 Maryland Win 12-9 1885.32 Feb 25th Easterns Qualifier 2023
104 Florida State Win 13-7 1902.54 Feb 25th Easterns Qualifier 2023
33 Duke Loss 11-12 1665.66 Feb 25th Easterns Qualifier 2023
49 Notre Dame Loss 11-12 1518.26 Feb 25th Easterns Qualifier 2023
71 Cornell Win 11-5 2103.6 Feb 26th Easterns Qualifier 2023
56 James Madison Win 12-10 1837.76 Feb 26th Easterns Qualifier 2023
24 North Carolina-Charlotte Loss 8-12 1453.32 Feb 26th Easterns Qualifier 2023
134 Carnegie Mellon Win 9-7 1515.65 Mar 4th Fish Bowl
36 Penn State Win 11-6 2324.31 Mar 4th Fish Bowl
36 Penn State Win 11-6 2324.31 Mar 5th Fish Bowl
97 Delaware Win 15-6 2019 Mar 5th Fish Bowl
12 Minnesota Loss 11-13 1842.07 Apr 1st Easterns 2023
1 North Carolina Loss 8-11 2027.05 Apr 1st Easterns 2023
30 Ohio State Win 11-9 2085.18 Apr 1st Easterns 2023
21 Northeastern Loss 10-11 1782.17 Apr 1st Easterns 2023
18 California Loss 6-14 1361.57 Apr 2nd Easterns 2023
23 Wisconsin Win 14-13 2019.52 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)