#46 North Carolina-Wilmington (18-10)

avg: 1978.21  •  sd: 71.85  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
13 Ohio State Loss 8-10 2142.26 Jan 13th Florida Winter Classic 2018
57 Kansas Win 12-10 2074.66 Jan 13th Florida Winter Classic 2018
32 Florida Win 11-10 2205.24 Jan 13th Florida Winter Classic 2018
57 Kansas Loss 13-14 1711.54 Jan 14th Florida Winter Classic 2018
48 Georgia Win 9-6 2374.15 Jan 14th Florida Winter Classic 2018
80 James Madison Win 12-6 2226.37 Jan 27th Winta Binta Vinta Fest 2018
245 George Mason University** Win 13-1 1094.17 Ignored Jan 27th Winta Binta Vinta Fest 2018
88 Georgetown Win 8-3 2178.62 Jan 27th Winta Binta Vinta Fest 2018
215 Virginia-B** Win 13-1 1376.36 Ignored Jan 27th Winta Binta Vinta Fest 2018
66 Virginia Win 14-6 2371.7 Jan 28th Winta Binta Vinta Fest 2018
49 Duke Win 12-4 2551.48 Jan 28th Winta Binta Vinta Fest 2018
7 Tufts Loss 3-14 1908.79 Feb 24th Commonwealth Cup 2018
48 Georgia Loss 10-13 1627.44 Feb 24th Commonwealth Cup 2018
66 Virginia Win 14-10 2170.4 Feb 24th Commonwealth Cup 2018
45 Case Western Reserve Win 11-9 2227.78 Feb 25th Commonwealth Cup 2018
49 Duke Loss 7-13 1393.95 Feb 25th Commonwealth Cup 2018
15 North Carolina State Loss 9-11 2103.87 Feb 25th Commonwealth Cup 2018
107 LSU Win 11-5 2066.19 Mar 10th Tally Classic XIII
245 George Mason University** Win 15-3 1094.17 Ignored Mar 10th Tally Classic XIII
41 Georgia Tech Loss 7-10 1619.75 Mar 10th Tally Classic XIII
19 Vermont Loss 5-15 1663.48 Mar 10th Tally Classic XIII
180 South Florida Win 11-7 1467.46 Mar 10th Tally Classic XIII
47 Harvard Win 12-11 2102.97 Mar 11th Tally Classic XIII
32 Florida Loss 12-13 1955.24 Mar 11th Tally Classic XIII
48 Georgia Win 13-12 2080.58 Mar 31st Easterns 2018
148 Virginia Tech** Win 15-2 1789.59 Ignored Mar 31st Easterns 2018
39 Clemson Win 13-12 2144.14 Mar 31st Easterns 2018
15 North Carolina State Loss 7-15 1753.08 Mar 31st Easterns 2018
**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)