#69 North Carolina State (9-13)

avg: 1293.24  •  sd: 71.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 Appalachian State Win 11-10 1339.74 Jan 25th Carolina Kickoff 2025
43 Duke Loss 7-12 1043.88 Jan 25th Carolina Kickoff 2025
152 North Carolina-B** Win 15-5 1289.66 Ignored Jan 25th Carolina Kickoff 2025
100 Emory Win 13-2 1675.12 Jan 26th Carolina Kickoff 2025
258 Emory-B** Win 15-0 286.43 Ignored Jan 26th Carolina Kickoff 2025
9 North Carolina** Loss 0-15 1518.27 Ignored Jan 26th Carolina Kickoff 2025
29 Georgia Loss 8-13 1228.8 Feb 15th Queen City Tune Up 2025
11 Michigan** Loss 2-13 1510.35 Ignored Feb 15th Queen City Tune Up 2025
118 Northwestern Win 13-4 1539.61 Feb 15th Queen City Tune Up 2025
21 Virginia Loss 3-7 1299.53 Feb 16th Queen City Tune Up 2025
35 Wisconsin Loss 4-8 1068.59 Feb 16th Queen City Tune Up 2025
43 Duke Win 10-5 2138.29 Apr 12th Carolina D I Womens Conferences 2025
167 East Carolina Win 8-4 1190.92 Apr 12th Carolina D I Womens Conferences 2025
188 Wake Forest** Win 13-1 1095.1 Ignored Apr 12th Carolina D I Womens Conferences 2025
60 South Carolina Loss 10-13 1055.92 Apr 12th Carolina D I Womens Conferences 2025
84 Clemson Loss 12-13 1038.38 Apr 13th Carolina D I Womens Conferences 2025
9 North Carolina Loss 7-15 1518.27 Apr 13th Carolina D I Womens Conferences 2025
160 Charleston** Win 12-3 1265.23 Ignored Apr 26th Atlantic Coast D I College Womens Regionals 2025
43 Duke Loss 9-15 1048.91 Apr 26th Atlantic Coast D I College Womens Regionals 2025
50 Liberty Loss 9-12 1143.63 Apr 26th Atlantic Coast D I College Womens Regionals 2025
21 Virginia** Loss 5-14 1299.53 Ignored Apr 26th Atlantic Coast D I College Womens Regionals 2025
50 Liberty Loss 9-11 1239.79 Apr 27th Atlantic Coast D I College Womens Regionals 2025
**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)