#11 Emory (8-4)

avg: 1920.68  •  sd: 108.74  •  top 16/20: 84.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
347 Radford** Win 15-3 978.28 Ignored Jan 27th Joint Summit XXXIII College Open
223 High Point** Win 15-6 1463.1 Ignored Jan 27th Joint Summit XXXIII College Open
98 Clemson Win 12-6 1917.35 Jan 27th Joint Summit XXXIII College Open
6 Brown Loss 12-13 1921.71 Mar 3rd Stanford Invite 2018
5 Washington Loss 12-13 1926.41 Mar 3rd Stanford Invite 2018
15 Stanford Loss 8-12 1444.47 Mar 3rd Stanford Invite 2018
43 British Columbia Loss 9-10 1469.64 Mar 4th Stanford Invite 2018
22 Tufts Win 12-8 2191.33 Mar 4th Stanford Invite 2018
47 Iowa State Win 14-8 2104.28 Mar 31st Huck Finn 2018
52 Harvard Win 15-4 2136.01 Mar 31st Huck Finn 2018
58 Kansas Win 15-8 2065.67 Mar 31st Huck Finn 2018
91 Penn State Win 12-7 1894.41 Mar 31st Huck Finn 2018
**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)