#38 Duke (14-7)

avg: 1590.76  •  sd: 46.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina** Loss 6-15 1688.56 Ignored Jan 26th Carolina Kickoff 2024
78 Carleton College-CHOP Loss 14-15 1223.94 Jan 27th Carolina Kickoff 2024
126 Lehigh Win 15-10 1599 Jan 27th Carolina Kickoff 2024
36 North Carolina-Charlotte Win 15-14 1743.26 Jan 27th Carolina Kickoff 2024
84 Appalachian State Win 14-11 1640.14 Jan 28th Carolina Kickoff 2024
29 South Carolina Loss 13-15 1469.73 Jan 28th Carolina Kickoff 2024
16 Penn State Loss 9-12 1575.86 Jan 28th Carolina Kickoff 2024
50 Alabama Win 13-10 1829.71 Feb 24th Easterns Qualifier 2024
16 Penn State Loss 6-13 1321.23 Feb 24th Easterns Qualifier 2024
58 Maryland Win 12-7 1963.48 Feb 24th Easterns Qualifier 2024
76 Purdue Win 13-5 1957.22 Feb 24th Easterns Qualifier 2024
52 Virginia Tech Win 12-11 1600.52 Feb 25th Easterns Qualifier 2024
29 South Carolina Loss 12-15 1383.42 Feb 25th Easterns Qualifier 2024
66 Virginia Loss 11-14 1080.83 Feb 25th Easterns Qualifier 2024
36 North Carolina-Charlotte Win 11-10 1743.26 Feb 25th Easterns Qualifier 2024
97 Florida State Win 15-12 1548.26 Mar 30th Atlantic Coast Open 2024
61 William & Mary Win 15-12 1732.5 Mar 30th Atlantic Coast Open 2024
165 RIT Win 15-8 1530.1 Mar 30th Atlantic Coast Open 2024
73 Richmond Win 12-11 1489.26 Mar 30th Atlantic Coast Open 2024
58 Maryland Win 15-13 1657.14 Mar 31st Atlantic Coast Open 2024
61 William & Mary Win 15-13 1646.19 Mar 31st Atlantic Coast Open 2024
**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)