#62 Duke (10-8)

avg: 1551  •  sd: 61.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
94 Appalachian State Win 13-8 1868.59 Jan 25th Carolina Kickoff 2019
1 North Carolina Loss 7-13 1674.39 Jan 25th Carolina Kickoff 2019
85 Richmond Loss 7-12 909.19 Jan 26th Carolina Kickoff 2019
78 Carleton College-GoP Loss 10-13 1129.57 Jan 26th Carolina Kickoff 2019
119 Clemson Win 15-8 1848.36 Jan 27th Carolina Kickoff 2019
104 Portland Win 13-9 1757.73 Feb 9th Stanford Open 2019
261 Cal Poly-SLO-B** Win 11-2 1421.14 Ignored Feb 9th Stanford Open 2019
21 California Loss 6-13 1243.46 Feb 9th Stanford Open 2019
50 Stanford Loss 6-8 1332.25 Feb 10th Stanford Open 2019
199 Claremont Win 8-4 1561.15 Feb 10th Stanford Open 2019
11 North Carolina State Loss 9-13 1609 Mar 7th Atlantic Coast Showcase 3719
66 Penn State Loss 11-12 1410.24 Mar 30th Atlantic Coast Open 2019
35 Middlebury Win 12-11 1851.5 Mar 30th Atlantic Coast Open 2019
102 Georgetown Win 13-9 1769.75 Mar 30th Atlantic Coast Open 2019
195 George Washington Win 13-6 1603.81 Mar 30th Atlantic Coast Open 2019
118 MIT Win 14-11 1601.07 Mar 31st Atlantic Coast Open 2019
35 Middlebury Loss 11-15 1345.33 Mar 31st Atlantic Coast Open 2019
115 Villanova Win 14-11 1609.73 Mar 31st Atlantic Coast Open 2019
**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)