#151 North Carolina-B (8-10)

avg: 867.95  •  sd: 89.95  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
103 Clemson Win 8-5 1653.33 Jan 20th Carolina Kickoff Womens 2024
62 Duke** Loss 2-15 884.05 Ignored Jan 20th Carolina Kickoff Womens 2024
56 North Carolina State** Loss 0-14 943.77 Ignored Jan 20th Carolina Kickoff Womens 2024
62 Duke** Loss 4-13 884.05 Ignored Jan 21st Carolina Kickoff Womens 2024
6 North Carolina** Loss 2-13 1899.4 Ignored Jan 21st Carolina Kickoff Womens 2024
56 North Carolina State** Loss 1-15 943.77 Ignored Jan 21st Carolina Kickoff Womens 2024
145 Dartmouth Loss 7-9 626.76 Mar 23rd Rodeo 2024
62 Duke Loss 4-6 1118.45 Mar 23rd Rodeo 2024
138 Liberty Win 8-5 1400.36 Mar 23rd Rodeo 2024
86 Williams Loss 3-10 733.84 Mar 23rd Rodeo 2024
145 Dartmouth Loss 6-11 359.41 Mar 24th Rodeo 2024
62 Duke** Loss 3-12 884.05 Ignored Mar 24th Rodeo 2024
240 American-B** Win 9-2 572.93 Ignored Apr 13th Atlantic Coast Dev Womens Conferences 2024
217 Georgetown-B Win 7-5 620.82 Apr 13th Atlantic Coast Dev Womens Conferences 2024
219 William & Mary-B Win 10-3 883.01 Apr 13th Atlantic Coast Dev Womens Conferences 2024
240 American-B** Win 9-0 572.93 Ignored Apr 14th Atlantic Coast Dev Womens Conferences 2024
217 Georgetown-B Win 13-2 892.68 Apr 14th Atlantic Coast Dev Womens Conferences 2024
229 Virginia-B** Win 10-3 759.59 Ignored Apr 14th Atlantic Coast Dev Womens Conferences 2024
**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)