#15 North Carolina State (13-4)

avg: 2353.08  •  sd: 63.3  •  top 16/20: 93.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
42 Wisconsin Win 8-5 2457.3 Feb 3rd Queen City Tune Up 2018 College Women
7 Tufts Win 11-10 2633.79 Feb 3rd Queen City Tune Up 2018 College Women
10 Pittsburgh Loss 3-11 1881.75 Feb 3rd Queen City Tune Up 2018 College Women
40 Kennesaw State Win 10-9 2142.59 Feb 3rd Queen City Tune Up 2018 College Women
49 Duke Win 15-9 2466.96 Feb 22nd Atlantic Coast Showcase ACS NCSU vs Duke
88 Georgetown Win 15-9 2094.1 Feb 24th Commonwealth Cup 2018
21 Michigan Win 14-13 2363.43 Feb 24th Commonwealth Cup 2018
3 North Carolina Loss 11-15 2349.33 Feb 24th Commonwealth Cup 2018
7 Tufts Loss 10-11 2383.79 Feb 25th Commonwealth Cup 2018
31 Penn State Win 10-8 2348.86 Feb 25th Commonwealth Cup 2018
46 North Carolina-Wilmington Win 11-9 2227.42 Feb 25th Commonwealth Cup 2018
66 Virginia Win 15-8 2336.51 Mar 16th Atlantic Coast Showcase ACS NCSU vs Virginia
3 North Carolina Loss 8-12 2289.34 Mar 20th Atlantic Coast Showcase ACS NCSU vs UNC
134 Tennessee** Win 15-4 1876.04 Ignored Mar 31st Easterns 2018
93 Cornell** Win 15-5 2140.61 Ignored Mar 31st Easterns 2018
49 Duke Win 14-6 2551.48 Mar 31st Easterns 2018
46 North Carolina-Wilmington Win 15-7 2578.21 Mar 31st Easterns 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)