#97 Duke (11-9)

avg: 1273.42  •  sd: 64.74  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
3 North Carolina Loss 7-13 1648.59 Jan 31st Carolina Kickoff mens 2025
71 Case Western Reserve Win 13-11 1633.9 Feb 1st Carolina Kickoff mens 2025
48 Maryland Loss 8-13 1069.51 Feb 1st Carolina Kickoff mens 2025
71 Case Western Reserve Loss 10-13 1076.92 Feb 2nd Carolina Kickoff mens 2025
88 Georgetown Win 13-11 1537.51 Feb 2nd Carolina Kickoff mens 2025
87 Temple Win 12-11 1435.92 Feb 2nd Carolina Kickoff mens 2025
84 Ohio State Win 11-10 1444.67 Feb 22nd Easterns Qualifier 2025
47 McGill Loss 8-13 1095.28 Feb 22nd Easterns Qualifier 2025
32 Virginia Loss 8-13 1183.71 Feb 22nd Easterns Qualifier 2025
183 Kennesaw State Win 13-5 1490.74 Feb 22nd Easterns Qualifier 2025
66 Dartmouth Win 15-14 1577.25 Feb 23rd Easterns Qualifier 2025
87 Temple Win 15-10 1764.52 Feb 23rd Easterns Qualifier 2025
49 North Carolina State Loss 8-13 1068.64 Feb 23rd Easterns Qualifier 2025
276 Virginia Tech-B Win 15-7 1077.59 Mar 22nd Atlantic Coast Open 2025
170 Massachusetts -B Win 10-7 1349.52 Mar 22nd Atlantic Coast Open 2025
105 Liberty Loss 7-14 650.02 Mar 22nd Atlantic Coast Open 2025
159 George Mason Win 12-11 1122.77 Mar 22nd Atlantic Coast Open 2025
43 Virginia Tech Loss 11-15 1231.04 Mar 23rd Atlantic Coast Open 2025
115 Vermont-B Win 15-11 1575.88 Mar 23rd Atlantic Coast Open 2025
113 Lehigh Loss 8-11 838.63 Mar 23rd Atlantic Coast Open 2025
**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)