#33 Duke (15-6)

avg: 1790.66  •  sd: 56.87  •  top 16/20: 0.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina Loss 10-15 1939.05 Jan 27th Carolina Kickoff
52 Appalachian State Win 15-10 2087.65 Jan 28th Carolina Kickoff
45 Georgetown Win 14-13 1821.69 Jan 28th Carolina Kickoff
36 Penn State Loss 12-14 1556.66 Jan 28th Carolina Kickoff
36 Penn State Loss 11-15 1396.45 Jan 29th Carolina Kickoff
84 Richmond Win 13-9 1868.49 Jan 29th Carolina Kickoff
27 South Carolina Loss 12-15 1547.68 Jan 29th Carolina Kickoff
69 Maryland Win 12-11 1664.96 Feb 25th Easterns Qualifier 2023
104 Florida State Win 12-9 1690.37 Feb 25th Easterns Qualifier 2023
25 North Carolina-Wilmington Win 12-11 2009.26 Feb 25th Easterns Qualifier 2023
49 Notre Dame Win 13-9 2061.82 Feb 25th Easterns Qualifier 2023
71 Cornell Loss 11-15 1122.43 Feb 26th Easterns Qualifier 2023
56 James Madison Win 11-8 1965.25 Feb 26th Easterns Qualifier 2023
41 William & Mary Loss 10-13 1390.74 Feb 26th Easterns Qualifier 2023
162 American** Win 13-4 1704.41 Ignored Apr 1st Atlantic Coast Open 2023
190 MIT** Win 13-4 1591.57 Ignored Apr 1st Atlantic Coast Open 2023
77 Temple Win 13-10 1808.46 Apr 1st Atlantic Coast Open 2023
67 Virginia Tech Win 13-8 2049.45 Apr 1st Atlantic Coast Open 2023
70 Lehigh Win 15-8 2091.54 Apr 2nd Atlantic Coast Open 2023
84 Richmond Win 14-8 1985.95 Apr 2nd Atlantic Coast Open 2023
67 Virginia Tech Win 13-12 1678.29 Apr 2nd Atlantic Coast Open 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)