#174 East Carolina (8-3)

avg: 1032.42  •  sd: 66.17  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
418 Kennesaw State-B** Win 13-1 445.2 Ignored Mar 3rd Cola Classic 2018
280 South Carolina-B Win 10-7 1055.09 Mar 3rd Cola Classic 2018
242 Samford Win 10-9 911.01 Mar 3rd Cola Classic 2018
303 Charleston Win 12-8 1012.64 Mar 3rd Cola Classic 2018
125 Georgia College Loss 10-13 887.67 Mar 4th Cola Classic 2018
295 Georgia Tech-B Win 9-6 1015.16 Mar 4th Cola Classic 2018
243 Rowan Win 13-10 1112.15 Mar 24th Atlantic Coast Open 2018
78 Georgetown Loss 7-11 948.18 Mar 24th Atlantic Coast Open 2018
84 Virginia Loss 5-10 828.24 Mar 24th Atlantic Coast Open 2018
270 American Win 11-6 1253.99 Mar 25th Atlantic Coast Open 2018
151 George Mason Win 11-10 1241.84 Mar 25th Atlantic Coast Open 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)