#4 North Carolina (22-3)

avg: 2622.41  •  sd: 90.56  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
156 Clemson** Win 12-3 1322.32 Ignored Jan 20th Carolina Kickoff Womens 2024
94 Duke** Win 14-1 1867.64 Ignored Jan 20th Carolina Kickoff Womens 2024
26 North Carolina State Win 9-6 2347.11 Jan 20th Carolina Kickoff Womens 2024
94 Duke Win 13-6 1867.64 Jan 21st Carolina Kickoff Womens 2024
150 North Carolina-B** Win 13-2 1392.78 Ignored Jan 21st Carolina Kickoff Womens 2024
26 North Carolina State** Win 7-2 2528.54 Ignored Jan 21st Carolina Kickoff Womens 2024
16 Georgia Win 12-6 2734.06 Feb 10th Queen City Tune Up 2024
12 Michigan Win 15-7 2911.61 Feb 10th Queen City Tune Up 2024
17 Pennsylvania Win 12-7 2645.22 Feb 10th Queen City Tune Up 2024
21 Ohio State Win 15-8 2646.96 Feb 10th Queen City Tune Up 2024
3 Carleton College Win 15-11 3087.72 Feb 11th Queen City Tune Up 2024
7 Tufts Win 12-9 2865.54 Feb 11th Queen City Tune Up 2024
22 Notre Dame Win 10-6 2551.33 Feb 11th Queen City Tune Up 2024
30 California Win 13-7 2442.08 Mar 16th NW Challenge 2024
10 Washington Win 13-9 2767.34 Mar 16th NW Challenge 2024
3 Carleton College Loss 12-13 2581.55 Mar 17th NW Challenge 2024
8 Colorado Win 13-10 2786.21 Mar 17th NW Challenge 2024
13 Western Washington Win 13-4 2811.79 Mar 17th NW Challenge 2024
31 Brown** Win 11-3 2474.19 Ignored Mar 30th East Coast Invite 2024
2 Vermont Loss 11-15 2408.46 Mar 30th East Coast Invite 2024
41 SUNY-Binghamton Win 14-7 2286.48 Mar 30th East Coast Invite 2024
20 Northeastern Win 15-6 2701.67 Mar 30th East Coast Invite 2024
7 Tufts Win 9-8 2645.18 Mar 31st East Coast Invite 2024
2 Vermont Loss 10-12 2551.5 Mar 31st East Coast Invite 2024
12 Michigan Win 13-9 2730.17 Mar 31st East Coast Invite 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)