#56 North Carolina-Wilmington (5-13)

avg: 1384.72  •  sd: 93.21  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
15 Florida Loss 4-12 1378.2 Jan 18th Florida Winter Classic 2020
31 Florida State Loss 3-11 1063.97 Jan 18th Florida Winter Classic 2020
7 Ohio State Loss 7-10 1717.1 Jan 18th Florida Winter Classic 2020
98 North Georgia Loss 5-11 472.34 Jan 18th Florida Winter Classic 2020
74 South Florida Loss 10-11 1138.77 Jan 19th Florida Winter Classic 2020
233 Miami** Win 15-2 406.89 Ignored Jan 19th Florida Winter Classic 2020
11 Dartmouth Loss 6-13 1435.62 Jan 19th Florida Winter Classic 2020
98 North Georgia Win 14-6 1672.34 Jan 19th Florida Winter Classic 2020
28 Michigan Loss 7-8 1605.66 Feb 8th Queen City Tune Up 2020 Women
1 Carleton College** Loss 0-13 1914.9 Ignored Feb 8th Queen City Tune Up 2020 Women
46 Pennsylvania Loss 6-8 1169.91 Feb 8th Queen City Tune Up 2020 Women
12 Virginia** Loss 4-12 1435.27 Ignored Feb 8th Queen City Tune Up 2020 Women
83 Clemson Win 13-2 1802.24 Feb 9th Queen City Tune Up 2020 Women
129 Harvard Win 15-7 1462.04 Feb 22nd Commonwealth Cup 2020 Weekend 2
28 Michigan Loss 8-9 1605.66 Feb 22nd Commonwealth Cup 2020 Weekend 2
82 Oberlin Win 11-9 1467.22 Feb 22nd Commonwealth Cup 2020 Weekend 2
7 Ohio State** Loss 4-15 1506.77 Ignored Feb 23rd Commonwealth Cup 2020 Weekend 2
22 Northwestern Loss 1-15 1189.74 Feb 23rd Commonwealth Cup 2020 Weekend 2
**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)