#28 Duke (13-9)

avg: 1682.04  •  sd: 95.39  •  top 16/20: 20.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina** Loss 2-14 2337.91 Ignored Jan 21st Carolina Kickoff womens and nonbinary
95 Temple Win 10-6 1525.27 Jan 21st Carolina Kickoff womens and nonbinary
30 South Carolina Loss 6-7 1535.8 Jan 22nd Carolina Kickoff womens and nonbinary
64 Appalachian State Win 10-5 1848.31 Jan 22nd Carolina Kickoff womens and nonbinary
144 North Carolina-B** Win 12-4 1260.77 Ignored Jan 22nd Carolina Kickoff womens and nonbinary
87 Southern California Win 10-4 1686.09 Feb 18th President’s Day Invite
17 California-San Diego Loss 7-11 1357.68 Feb 18th President’s Day Invite
12 California-Santa Barbara Loss 5-10 1484.52 Feb 18th President’s Day Invite
48 Texas Loss 7-9 1180.62 Feb 18th President’s Day Invite
8 Stanford Loss 2-12 1633.33 Feb 19th President’s Day Invite
53 Cal Poly-SLO Win 9-5 1909.08 Feb 19th President’s Day Invite
12 California-Santa Barbara Loss 5-9 1529.36 Feb 19th President’s Day Invite
74 Utah Win 9-4 1824.4 Feb 19th President’s Day Invite
24 Carleton College-Eclipse Win 10-7 2122.5 Feb 20th President’s Day Invite
25 California-Davis Loss 8-11 1354.34 Feb 20th President’s Day Invite
30 South Carolina Win 10-2 2260.8 Mar 25th Rodeo 2023
184 Georgetown-B** Win 13-1 863.71 Ignored Mar 25th Rodeo 2023
71 Massachusetts Win 13-4 1833.48 Mar 25th Rodeo 2023
215 Elon** Win 13-1 352.67 Ignored Mar 25th Rodeo 2023
60 Ohio Win 12-4 1899.34 Mar 26th Rodeo 2023
59 Penn State Win 13-9 1719.81 Mar 26th Rodeo 2023
21 North Carolina State Loss 8-11 1390.7 Mar 26th Rodeo 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)