#92 Duke (6-11)

avg: 1262.44  •  sd: 68.95  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina** Loss 3-13 1730.39 Ignored Jan 24th Carolina Kickoff 2020
81 North Carolina-Charlotte Loss 8-10 1055.36 Jan 25th Carolina Kickoff 2020
89 Carleton College-GoP Win 12-8 1719.48 Jan 25th Carolina Kickoff 2020
66 Georgetown Win 11-8 1758.45 Jan 25th Carolina Kickoff 2020
71 Kentucky Win 13-11 1602.59 Jan 26th Carolina Kickoff 2020
21 North Carolina State Loss 4-15 1220.92 Jan 26th Carolina Kickoff 2020
51 Tennessee Loss 8-10 1234.06 Jan 26th Carolina Kickoff 2020
117 Appalachian State Win 12-11 1268.66 Feb 8th Queen City Tune Up 2020 Open
7 Ohio State** Loss 5-12 1469.2 Ignored Feb 8th Queen City Tune Up 2020 Open
58 Virginia Loss 8-11 1086.55 Feb 8th Queen City Tune Up 2020 Open
48 Temple Loss 7-9 1223.62 Feb 9th Queen City Tune Up 2020 Open
41 Alabama Loss 4-12 1000 Feb 29th Easterns Qualifier 2020
73 Carnegie Mellon Loss 7-12 845.95 Feb 29th Easterns Qualifier 2020
111 Maryland Loss 7-8 1044.98 Feb 29th Easterns Qualifier 2020
32 Dartmouth Loss 6-11 1136.74 Feb 29th Easterns Qualifier 2020
131 Johns Hopkins Win 15-8 1659.38 Mar 1st Easterns Qualifier 2020
161 Georgia State Win 11-9 1237.7 Mar 1st Easterns Qualifier 2020
**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)