#38 Duke (15-5)

avg: 1890.8  •  sd: 84.34  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
150 North Carolina-B Win 15-8 1539.62 Jan 25th Carolina Kickoff 2025
99 Emory Win 15-2 1921.96 Jan 25th Carolina Kickoff 2025
62 North Carolina State Win 12-7 2172.04 Jan 25th Carolina Kickoff 2025
9 North Carolina Loss 8-15 1957.83 Jan 25th Carolina Kickoff 2025
55 Appalachian State Win 12-8 2140.53 Jan 26th Carolina Kickoff 2025
242 Emory-B** Win 15-2 444.95 Ignored Jan 26th Carolina Kickoff 2025
145 Boston College** Win 9-2 1586.95 Ignored Feb 22nd 2025 Commonwealth Cup Weekend 2
80 Carnegie Mellon Win 9-4 2075.55 Feb 22nd 2025 Commonwealth Cup Weekend 2
75 Chicago Win 6-3 2064.39 Feb 22nd 2025 Commonwealth Cup Weekend 2
33 Cornell Loss 6-8 1668.02 Feb 22nd 2025 Commonwealth Cup Weekend 2
71 Central Florida Win 10-6 2053.48 Feb 23rd 2025 Commonwealth Cup Weekend 2
70 Connecticut Win 9-8 1693.1 Feb 23rd 2025 Commonwealth Cup Weekend 2
57 James Madison Win 13-9 2100 Feb 23rd 2025 Commonwealth Cup Weekend 2
36 MIT Win 9-8 2024.22 Mar 29th East Coast Invite 2025
66 St Olaf Win 10-9 1748.78 Mar 29th East Coast Invite 2025
19 Notre Dame Loss 7-11 1819.2 Mar 29th East Coast Invite 2025
40 Haverford/Bryn Mawr Win 8-7 1968.37 Mar 29th East Coast Invite 2025
19 Notre Dame Loss 4-13 1686.09 Mar 30th East Coast Invite 2025
23 Pennsylvania Loss 5-11 1645.85 Mar 30th East Coast Invite 2025
36 MIT Win 5-4 2024.22 Mar 30th East Coast Invite 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)